MAGISTRSKO DELO. Jan Ornik

Transcription

1 MAGISTRSKO DELO Jan Ornik Maribor, 2016

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3 MAGISTRSKO DELO Novi načini zaznavanja mikroplastike Mentor: prof. dr. Nataša Vaupotič Kandidat: Jan Ornik Somentor: Dr.-Ing. Jan C. Balzer Maribor, 2016

4 MASTER S THESIS Novel Concepts for the Detection of Microplastics Supervisor: prof. dr. Nataša Vaupotič Candidate: Jan Ornik Co-supervisor: Dr.-Ing. Jan C. Balzer Maribor, 2016

5 Acknowledgements I thank Prof. Dr. Martin Koch for introducing me to the topic of Microplastics and accepting me to his group, as well as to the members of the group for all the support. My stay abroad during the research and writing of the thesis was financially supported by Javni sklad Republike Slovenije za razvoj kadrov in štipendije. Special thanks go to the supervisor prof. dr. Nataša Vaupotič and co-supervisor Dr.-Ing. Jan C. Balzer for all the support, help, and guidance during the research and writing of the thesis. I am grateful to my family for the selfless support throughout the process of my education and for always being there when needed. Finally, I would like to thank Marina for her patience and support throughout the time of writing the thesis.

6 UNIVERZA V MARIBORU FAKULTETA ZA NARAVOSLOVJE IN MATEMATIKO Ornik, J.: NOVI NAČINI ZAZNAVANJA MIKROPLASTIKE Magistrska naloga, Univerza v Mariboru, Fakulteta za naravoslovje in matematiko, Oddelek za fiziko, 2016 POVZETEK Mikroplastika so delci plastike, manjši od 5 mm, ki jih najdemo v okolju in so lahko nevarni živim bitjem. Pričakuje se, da se bo onesnaženost okolja z mikroplastiko s časom povečevala. Kljub temu še ni izdelanih nadzornih protokolov, ki bi vključevali spektroskopske metode, s katerimi bi lahko avtomatizirano analizirali vzorce mikroplastike in tako zagotovili natančne in reprezentativne podatke o onesnaženju. V magistrskem delu analiziramo primernost fotoluminiscenčne spektroskopije za zaznavanje mikroplastike. Z zgrajeno, cenovno ugodno postavitvijo za fotoluminiscenčno spektroskopijo smo obravnavali 27 različnih vzorcev. Primerjava zajetih spektrov fotoluminiscence je pokazala, da se spektri razlikujejo, še posebej za plastične in neplastične materiale. Prav tako se spektri razlikujejo za različne materiale kot tudi različne vrste plastike. Prisotnost barvil v plastičnih materialih in organski materiali na plastiki lahko bistveno vplivajo na spekter zaznane svetlobe, kar lahko predstavlja problem za prepoznavanje mikroplastike. Brez upoštevanja plastike z barvili in vzorcev z organskimi materiali je uspešnost razločevanja vzorcev plastike od vzorcev iz neplastičnih materialov z nevronskimi mrežami 99,3 %, za klasifikacijo različnih vrst plastičnih in neplastičnih materialov pa je uspešnost metode 63,1 %. Vzpodbudni rezultati kažejo, da je fotoluminiscenčna spektroskopija primerna metoda za detekcijo mikroplastike in bi z vidika hitrosti in lateralne ločljivosti lahko bila boljša rešitev za njeno zaznavanje v primerjavi z obstoječimi spektroskopskimi metodami. Ključne besede: mikroplastika, metode zaznavanja, fotoluminiscenčna spektroskopija, nevronske mreže IV

7 UNIVERSITY OF MARIBOR FACULTY OF NATURAL SCIENCES AND MATHEMATICS Ornik, J.: NOVEL CONCEPTS FOR THE DETECTION OF MICROPLASTICS Master s Thesis, University of Maribor, Faculty of Natural Sciences and Mathematics, Department of Physics, 2016 ABSTRACT: Microplastics are small pieces of plastic (smaller than 5 mm), which can be found in the environment and can be dangerous to living beings. It is expected that the abundance of microplastics will rise in the future. However, there are still no standard protocols for monitoring the microplastic abundance, which should include spectroscopic methods for an automated discrimination in order to produce reliable data. In this work we examined a new approach for microplastic detection based on the photoluminescence (PL) spectroscopy. To test the applicability of the proposed method a low-cost setup was built and characterized. The PL spectra from 27 different materials were collected and compared. The comparison of the spectra shows that the differentiation between samples is possible, especially between the plastic and non-plastic materials. Furthermore, the measured PL spectra also differ for different plastic types and other materials. However, the presence of dyes in plastic samples and incrustation of plastic samples by organic materials can affect the PL spectra and make the recognition troublesome. Disregarding organic materials and dyed plastic, the material differentiation based on the acquired PL spectra using neural networks resulted in 99.3 % accuracy when categorizing samples into plastic and non-plastic materials and 63.1 % accuracy when categorizing samples among different plastic and non-plastic materials. The promising results show that the PL spectroscopy of microplastics could outperform the spectroscopic methods used so far, by means of measurement speed and lateral resolution. Key words: microplastics, detection methods, photoluminescence spectroscopy, neural networks V

8 TABLE OF CONTENTS 1 INTRODUCTION MICROPLASTICS Environmental concerns Sample acquisition Preprocessing MicroPlastic Sediment Separator Detection of microplastics Raman Spectroscopy Fourier transform infrared spectroscopy Spectroscopic techniques and microplastics PHOTOLUMINESCENCE SPECTROSCOPY Photoluminescence Experimental setup Diode laser and excitation filter Longpass filters and laser light polarization Achromatic triplet lenses and optic fiber Construction and alignment of the setup Characterization of the setup and the problem encountered Acquisition and processing of the PL spectra RESULTS AND DISCUSSION Applicability for microplastic detection Acquired photoluminescence spectra Plastic and non-plastic materials Incrustation of plastic Dyed materials PL spectrum variations Sample differentiation Peak wavelength Neural networks CONCLUSION AND OUTLOOK RAZŠIRJEN POVZETEK V SLOVENSKEM JEZIKU... 55

9 1 INTRODUCTION Plastic is an all-around and everyday used material due to its broad applicability and low production costs. Since 1950 the worldwide plastic production has been increasing steadily, reaching 311 million metric tons in 2014 [1]. Its low price also contributes to a high disposal of plastic which often ends up in oceans. It is estimated that between 4.8 and 12.7 million tons of plastic entered the oceans in 2010 [2]. The concern of plastic litter in the marine environment is not new. In the 1980s it was shown that 80 % of marine litter is plastic and that the litter density is increasing exponentially [3]. Additionally, there is a less visible side to the plastic debris in the form of so called microplastics, which is a relatively new environmental threat. However, it has been shown that microplastics have been present in the oceans for at least 50 years [4]. Similar to the plastic litter, also the abundance of microplastic debris in the environment is increasing and will most likely increase further in the future [4]. It is quite generally accepted that plastic is a persistent material that is not biodegradable. Once it enters the environment it will likely stay there. From this point onward, the abundance can increase and there is no easy solution to this problem. Therefore, countries try to prevent or minimize plastic disposal of plastic by recycling and energy reuse. Despite the recent efforts in Europe (EU, Switzerland and Norway) to recycle, almost 31 % of the used plastic still ends up in landfills [5]. However, a recent publication reports that a bacterium was found that uses PET (polyethylene terephthalate) as its main energy and carbon source, degrading it into two benign monomers [6]. This increases the chances that there already exist bacteria that can degrade plastic or that these kind of bacteria might evolve with time. Nevertheless, there is still a long way before plastic could be considered biodegradable. Furthermore, even if it is considered biodegradable in the future, there will still be a need to monitor the abundance of plastic in the environment and to investigate its effect on it. A lot of research has recently been done on the abundance of microplastics, mainly in the marine environment by collecting samples from the water surface, water column or beaches and subsequently analyzing for microplastics [7]. The majority of samples were examined under a light microscope for plastic particles. In some cases further spectroscopic analysis was done to verify whether a particle is of a plastic origin or not, which drastically increases the accuracy of the determined abundance especially when particle size gets smaller [8, 9]. However, despite of a lot of research no standardized procedure has been developed to determine the abundance of microplastics. The procedure should also include spectroscopic methods [10]. Therefore, we tested photoluminescence (PL) spectroscopy as a potential new method for the detection of microplastics. 1

10 The Master s Thesis begins with a thorough description of the known effects of microplastics on the environment, the sampling process, and the presentation of the state of the art detection methods used so far (Chapter 2). In Chapter 3 we present the mechanism of photoluminescence and provide a detailed description of the cost efficient, self-built setup, which was used to determine, whether PL spectroscopy could be an appropriate method for the microplastic detection. Methods used for the PL spectra acquisition and its differentiation are presented at the end of this chapter. In Chapter 4 the results of the PL spectroscopy are presented and discussed, followed by the conclusions and the outlook of the future work in Chapter 5. 2

11 2 MICROPLASTICS Microplastics are defined as plastic particles smaller than 5 mm and are mainly considered an environmental problem. It is expected that the abundance of microplastics will rise in the future due to a gradual degradation of bigger plastic particles [11]. Additionally, microplastics enter the environment directly by accidental losses of virgin plastic pellets [12], by plastic fibers being washed out of clothes [13], or even from the everyday usage of cleaning products [14] and cosmetics [15]. Since microplastics are small, they do not affect the outlook of the environment, but that does not mean that it has no effect on it. In recent years a lot of studies have been done on microplastics especially by marine biologists. The focus of the work was mainly on potential threats to the environment, which are connected to the quantity and also to the type of microplastics in the environment. In this section we first provide an overview of the main environmental concerns of microplastics, then we present how samples are usually collected and preprocessed. Finally we review the techniques used for the determination of microplastics. 2.1 Environmental concerns The main concern about microplastics is how they affect living beings. Therefore, it is necessary to point out which threats microplastics pose to the environment and living beings. The different density of plastic particles, which can also change through the process of degradation, adsorption or desorption, overgrowth of micro-organisms and erosion of plastic can all affect their buoyancy. Therefore, microplastics can be found floating on the water surface, somewhere in the water column or sinking down to the sediment [7]. Microplastics can therefore be encountered by all marine species. Many additives that are introduced to plastics to improve their performance cause concern. Some of them are toxic and they can be released once the plastic particles enter the environment. Another threat is the hydrophobic nature of plastic, which tends to adsorb persistent organic pollutants (POPs) to concentrations of an order of magnitudes higher than in sediments found in the marine environment or seawater [16 19]. Because a lot of plastic debris is floating in the water column or on the surface of the water, it presents a transport vector for POPs [16, 17, 20] as well as for some species nesting on the debris [21]. A release of POPs from the microplastics after being ingested is a big concern and the effect of different POPs on the physical condition of different species. There is already some evidence that microplastics can enter the circulatory system and smaller particles (i.e. nanoplastics) can enter cells directly, but the effect has not been studied thoroughly [22, 23]. Research that was done on the effect of microplastics on animals, such as seabirds, mussels and lugworms will be presented shortly in the following paragraphs. 3

12 In a 10 day experiment lugworms were exposed to a sediment containing POPs contaminated plastic, which resulted in higher concentrations of contaminant in the tissue than in sediment [24]. Another 28 day long experiment with lugworms was performed [25]. Lugworms were exposed to three different concentrations of microplastics in the sediment. Microplastics were contaminated with PCBs (polychlorinated biphenyls). All three concentrations of microplastics in the sediment resulted in a higher concentration of PCBs in the tissue. Lugworms were also exposed to unplasticized polyvinyl chloride (UPVC, no plasticizers added to PVC) in a 5% concentration in the sediment [26]. The exposure caused a 50 % decrease in the total available energy reserve and significantly lowered the lipid reserves. It was confirmed that the reduced feeding activity was caused by the UPVC characteristics and not by the lower food proportions ingested due to the UPVC presence. An effect of a potential sediment contamination with the UPVC on Wadden Sea, where A. marina (lugworm) is a keystone species, was estimated. The effect of a contamination would result in 130 m 3 less sediment being continually ingested and passed in this area per year due to the reduced feeding activity. There are still many effects of microplastics on lugworms that need further research (e.g. transfer of pollutants into animals upon ingestion), for which more and better experiments are needed [27]. Additionally, to make these experiments more representative, better data on microplastic contamination is needed, which includes improved methods of microplastic detection. As mentioned before, plastic particles can also float in the sea column, making it accessible to filter feeders, which was demonstrated by analyzing the mussels digestive gut for microplastics [28, 29]. A different experiment [23] showed that high density polyethylene (HDPE) particles smaller than 80 µm were ingested by mussels and taken into cells and the lysosomal system only 3 hours after ingestion. Notable histological changes were noticed, which correspond with the inflammatory response. The effect of contaminated microplastics was also examined [30]. Virgin polyethylene (PE) and polystyrene (PS) particles (smaller than 1mm) were exposed to the pyrene (POPs) and adsorption was observed. Particles containing ng/g pyrene, which corresponded to the measured values on pellets from the sea, were offered to mussels. Pyrene was transferred and concentrated in their tissue. Concentrations in digestive glands were 3-times higher than those on the plastic particles, which clearly reflects the desorption after the ingestion. It was concluded that the pyrene-contaminated microplastics present a major hazard for mussels. Fish are also exposed to microplastics in the marine environment. A large number of fish (213) were equally distributed in three reservoirs: a control sample, virgin microplastics and contaminated microplastics [31]. The contamination of microplastics was achieved by exposing them to the seawater for 3 months. The results showed early warning signs of an endocrine disruption in fish exposed to a mixture of plastic and adsorbed contaminants, suggesting that the microplastics may change the functioning of the endocrine system in the marine animals. Another study was done on the effect of plastic 4

13 pellets, which were smaller than 5mm, to fish embryos [32]. In this case the ingestion of plastic was not of concern but the contaminants that might be released from the plastic were. Two types of pellets were used: the virgin and stranded ones, which were supposed to be contaminated. Both caused an abnormal development of embryos, though the virgin pellets caused more harm to embryos than the collected stranded pellets. However, it cannot be excluded that the contaminated pellets are not as dangerous or more than the virgin ones since there was no control of the contamination level. Seabirds also ingest floating or stranded plastic which can accumulate in their gut for a longer time. An experiment was done which showed that the half-life of plastic in seabirds (white-chinned petrels) is approximately one year [33]. An experiment with force feeding chicks, which are not seabirds but are easily available and representative for this study, with microplastics showed a slower weight gain [34]. Another study showed that Albatross collected in the northern hemisphere had a higher concentration of POPs than those from the southern hemisphere [35]. Furthermore, there was a positive correlation between the POPs concentration in the fat tissue and the plastic load. The effect of microplastics on humans is still unknown, but we are surely exposed to it in our everyday life. There are some indications that humans could ingest microplastics through the food web [28]. The use of microplastics in cleaning and hygiene products can also increase the microplastics availability for humans. The main concerns that have been expressed but not directly verified are connected to the plasticizers and other additives used in the plastic manufacture, which can affect the endocrine system and especially the reproductive functionalities. Some studies have been done correlating the semen quality and phthalate presence in it but the results are not consistent [36]. Experiments have been done mainly on rats and some other animals resulting in a reproductive disorder and negative effects on infants and present limited indicators for the health risk of humans [37]. Biomonitoring can be used to identify potential risks of plasticizers and other additives, but no direct and firm correlation has been proved so far [38]. Further research is needed. In summary, microplastics pose a threat to all living beings. To what extent it is still not completely clear, but there are some negative effects for sure. It has been pointed out that microplastics are entering the food web and could undergo trophic transfer. Humans are also exposed to small plastic pieces in food or drinks, but with rather unclear consequences. Additionally, microplastics are a potential transport vector for pollutants, which it adsorbs, and for some species, which nest on the floating microplastics. To better determine the effect of desorption of chemicals from the ingested and translocated microplastics, improved programs of monitoring are needed, which also includes improvement of quantification and detection methods [27]. Quantification of microplastics in the environment starts with the sample acquisition, which is discussed in the next subsection. 5

14 2.2 Sample acquisition In order to better understand the role of microplastics in the environment, determination of microplastic abundance in the environment is required. The marine environment was the main field of interest in the past, probably because of big quantities of litter entering the oceans and getting stranded back to the land, far away from its origin. Therefore, the most of the work on quantification of microplastic was done by the marine biologists by collecting samples from the sediment, sea surface and water column in different parts of the world. A review of 68 studies on different methodologies used for quantification and detection of microplastic was done in 2012 by Hidalgo-Ruiz et. al. [7]. The results of the work are shortly presented here to provide an overview on sample acquisition methods. Research objectives of the reviewed studies differed and can be divided into following groups: methodology, confirmation of the presence of microplastics, spatial distribution, temporal variability, contaminants and physical properties, and fragmentation process. With no standardized sample acquisition procedure, the sampling approaches differ from one investigation to another. The sampling approaches can be divided into three categories, selective, bulk, and volume-reduced, regardless of the part of marine environment examined. A direct extraction of the potential microplastic particles is the selective approach, which depends on the detection of particles on the spot mainly with the naked eye. Because microplastics are small, this approach is risky as microplastic can be overlooked. On the contrary, the idea of a bulk sample is to collect an entire volume of interest, which is later examined for microplastics. This way particles which might not be seen by a naked eye (i.e., they are covered or too small) are also collected. With the volume-reduced approach the volume of the bulk sample is reduced by sieving or filtering at the collection site. On the other hand with the bulk and volume-reduced approach further investigation of the sample takes place in a lab. The acquisition process differs for sediment and water sampling. For a direct extraction from the sediment, tweezers, spoons or just hands are usually used. Furthermore, three different approaches were used for collecting sediment samples: a linear or in line sampling usually along the strandline, areal collection and sampling into-depth. For the linear sampling mainly spoons and trowels were used, by areal approach quadrats were used and for into-depth sampling corers were used. In samples collected from the water surface nets with a different mesh size (0.053 mm to 3 mm) were used. By the water surface usually the top layers with thickness ranging from µm up to 25 cm were filtered with nets. In the water column examination mainly plankton nets were used and the depth of the water column varied from 1 to 212 m. Different and inconsistent sampling techniques and approaches are the reason for bad comparability of results. The sampling units in which the abundance results were reported (i.e., items per m 2, items per m 3, items per m strandline, items per kg sediment, grams per m 2 ) already show the connection to the sampling method used. This also indicates 6

15 that microplastics is a new topic and that there is no standardized procedure on how to collect samples. Sample acquisition is the first step in monitoring microplastic abundance in the environment. Unless the direct approach is used, which has many downsides, samples are transported to a lab, where they are further treated. The first step is to further decrease the sample volume and mass without losing microplastics, which will be described in the following subsection. 2.3 Preprocessing Before samples are examined for microplastics, their volume and mass is reduced so that the later sample inspection becomes easier and less time consuming. Scientists mainly used three different ways and their combination to reduce the sample size: sieving, filtrating and density separation [7]. The size of the sediment samples is usually decreased by density separation. The idea of density separation is based on the lower density of plastic material ( g/cm 3 ) compared to sand or other sediments (typical 2.65 g/cm 3 ). The sample is ideally put into a salt based solution with a typical density of 1.4 g/cm 3, which makes the microplastics float and the sediment drop to the ground after shaking and mixing the sample. The shaking process can be repeated several times to improve the results. A supernatant is then extracted for further processing, which is mainly followed by vacuum filtering. In some cases the sieving was performed before or after the density separation using sieves of different mesh sizes. Samples collected from the water surface or column were mainly directly examined because only floating particles are collected by nets, which already reduces the sample size. An extra density separation would not make much sense because only the floating particles had been collected in the first place. In some cases additional sieving and filtering was done to further decrease the sample size and to control the examined particle size. Reducing the sample size is crucial for later microplastic detection, however, it is important that it is done carefully to collect all or as many microplastics from the sample as possible, otherwise their microplastic contamination can easily be underestimated. An additional concern is also the contamination of samples from the airborne microplastics and unclean equipment. To avoid overestimation of microplastic the use of control samples is suggested. Density separation is a very useful method for the preprocessing of bulk sediment samples, because it eliminates the majority of the sediment and reduces the sample size. However, the reliability of the procedure depends on the density of the solution used [39] and on the constructed setup [40]. Expensive salts [39] are used to achieve high density solutions, which makes the whole process expensive and therefore unattractive. The optimization and assessment of reliability of the density separation approach was needed. 7

16 A two-step separation of microplastics from the sediment was proposed. The key idea of this approach was to reduce the sample size using some other method before using an expensive high density salt solution for the density separation. In first case fluidization of the sample using cheap saturated NaCl solution with an air-induced overflow (AIO) was performed [39]. An air-generated turbulent flow was used to force the particles with lower specific weight to move to the top of a beaker quicker than the particles with higher specific weight. Then, additional NaCl solution was pumped into the beaker in order to overflow the top layer, which was then used in the density separation using a more expensive NaI solution. The original sample size was reduced by up to 80 % and the recovery rate of between 91 and 99 % for 1 mm sized microplastics was reported. The recovery rates were relatively high, but they were determined only for one size of microplastic particles. In another case, a setup was built for the first volume reduction step based on the principle of elutriation, which separates specifically lighter particles with lower specific weight from ones with higher specific weight using an upward stream of gas or liquid [41]. In this case water was pumped into the system to extract specifically lighter particles. Microplastics were extracted in the second step by density separation using NaI solution. The recovery rate reported for the PVC particles was 100 % and for fibers 98 %. However, in another study the setup was further optimized and despite the optimization much lower recovery rates were reported (50.2 %) [42]. This questions the applicability of the method. Another approach was to use a high density salt solution and to improve the setup design to make the recovery rates as high as possible [40]. In the next subsection we present what we think is the most developed density separation approach so far, which is also available as a commercial product called MicroPlastic Sediment Separator (MPSS) MicroPlastic Sediment Separator The MPPS was firstly named Munich Plastic Sediment Separator [40]. It is constructed to improve the extraction results of microplastics from the sediment samples, especially for the small microplastic particles ranging in size down to 40 µm. The aluminum device is assembled out of three main parts. A stirred sediment container with an electric motor and a vale is the part at the bottom of the MPSS. It provides space for 6 L of sediment, which can be stirred by running the electric motor. The valve enables liquid supply for the procedure as well as for the drainage of the separating solution. The second part is a canonically shaped standpipe which is attached to the top of the sediment container. It has no sharp edges so that no microplastics can get stuck. A reduced diameter of the standpipe on top enables a high concentration of floating particles on top, so that the extracted final volume is small. The third part is a dividing chamber with a ball valve, which is on the top of the construction. It has an integrated sample chamber of a volume of 68 ml, which is used to collect the reduced volume sample. The sample chamber can be closed and the whole dividing chamber removed and turned around for the vacuum filtration directly after the separation process. 8

17 The separation process starts by introducing a separation liquid to the MPPS. The use of ZnCl2 water solution with the density of 1.7 g/cm 3 is suggested. The liquid is introduced via the bottom valve. The dividing chamber is removed and the sediment sample is added from the top. The stirring should be conducted for at least 15 min and up to 12 hours. After that the solution is left for 1-2 hours so that the sediment settles down. Then the dividing chamber is placed back on top and the ball valve is opened. The additional separation fluid is then pumped through the bottom valve to the MPPS, which makes the floating particles rise with the fluid level into the sample chamber. Then the ball valve is closed again and the dividing chamber is removed for the vacuum filtration. In case of samples with high organic contents, the use of hydrogen peroxide and sulfuric acid is recommended to dissolve the organic matter present on the filter. After the filtration the sample is left on the filter for further analysis. The MPPS was tested by adding 10 different plastic types to the sediment. Plastic particles were divided into two groups depending on the particle size: large microplastics (LM, 1-5 mm) and small microplastics (SM, smaller than 1 mm). The LM ranged in size from 2 to 5 mm and SM from 40 to 310 µm. The sediment was collected from river Würm and, by rinsing and washing, made plastic-free. For the test on the recovery of LM, the sediment was spiked with 10 particles per plastic type (100 in total). The test was repeated three times. For the test on recovery of SM, the sediment was spiked with 0.1 g per plastic type. The recovery rate of the MPPS for LM was 100 % and for SM 95.5 %. The results show high recovery rates for a wide size range of microplastics. Furthermore, the recovery rate was determined for a classic density separation setup, which resulted in 99.7 % for LM and 39.8 % for SM. The lower recovery rate for SM was mainly due to the inefficient setup, because SM got attached to the setup. The MPSS separates microplastics from the sediment based on the density difference. Using more LM particles to test the MPPS would make the recovery results for LM more reliable. Using a sediment from a river could also result in an overestimation of the recovery rate, if the removal of the potential microplastics was not completely successful. The calculated recovery rates are high for LM and SM, which show that the density separation is an appropriate method. However, the success of this method especially for small microplastic particles, depends on the density separation setup. Once the density separation is completed, the volume reduced sample has to be examined for microplastics, which is the final step in the sample analysis. Methods for the detection of microplastics are described in the next section. 2.4 Detection of microplastics Examination of pre-processed samples for microplastics can be done in different ways. The simplest way is the examination of the samples with the naked eye, which is usually followed by examination with light microscope. The particles are compared to visual criteria: no cellular or organic structures should be visible, fibers should have constant 9

18 thickness, particles must show clear and homogenous colors; if they are transparent a further examination under a higher magnification and fluorescent microscope is needed [7]. It has been shown that the visual inspection with the naked eye or using a stereo microscope results in a lower number of identified microplastic particles as well as in misidentification, especially for microplastics smaller than 1 mm [8]. Reliable data on the abundance of microplastics is important, therefore spectroscopic methods were introduced to the field to confirm the plastic origin and if possible to determine the plastic type as well. The plastic type can be another valuable information for the assessment of the microplastic problem. The most commonly used spectroscopic methods are Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy. In the next subsections we shortly present their working principles, advantages and also some problems for microplastic detection Raman Spectroscopy When light propagates through a medium, it interacts with it by getting absorbed or scattered. Information about the sample can be extracted from the absorption of light. This is the key idea of infrared spectroscopy, which will be discussed in the next subsection. On the other hand light can be scattered by a molecule. The light is mainly elastically scattered, which means that the energy of the scattered light is equal to the energy of the incident light. In a smaller proportion inelastic scattering of light by molecules happens. One out of every light scattering events is inelastic [43]. The fundamentals of the Raman spectroscopy were paved in the 1920s by postulating inelastic scattering of light by Smekal and by first experimental observations by Krishnan and Raman, after whom (Sir C. V. Raman) it was named [43]. The Raman scattering can be imagined as a collision of a molecule in the initial state with energy E i and an incident photon with the energy of hν i, where h is the Planck constant and ν i is the frequency of the initial photon. After the collision, a photon with the energy hν f can be detected and the molecule ends up in a different state with the energy E f. If the photon energy after the collision is lower (Stokes shift), the excess energy may appear as vibrational, rotational or electronic energy of the molecule [44]. If the photon energy after the collision is higher (Anti-Stokes shift), then the molecule scattering the photon had to be in an excited vibrational state before the scattering event. However, since the state population can be described by the Boltzmann distribution, the probability of a molecule being in an excited state at room temperature is much lower than for the ground state [43]. Therefore, the anti-stokes scattering is less likely than the Stokes scattering. The process of Raman scattering is depicted in the energy level scheme in Figure 1, where energy levels correspond to the stationary eigenstates of the molecule. During the scattering process the molecule enters an intermediate state of energy E V = E i + hν i, which does not necessarily correspond to any stationary eigenstates and is referred to as a virtual state [44]. 10

19 The Raman spectroscopy is based on measuring the energy differences between the vibrational states. The vibrational states correspond to the vibrational modes of a molecule, which depend on the molecular structure. Therefore, molecules can be identified from the Raman spectrum. The number of vibrational modes (Q) depends on the number of nuclei in the molecule (N) and can be calculated as Q = 3N 6 for a nonlinear molecule, and Q = 3N 5 for a linear molecule [43]. However, not all vibrational modes are Raman active. The Raman active modes are modes that change the polarizability of the molecule, which depends on the symmetry of the molecule [44]. Other modes are IR active and can therefore be observed with the IR spectroscopy, which makes these two spectroscopic methods complementary. Figure 1. Energy levels schemes of the Raman scattering event. a) Stokes shifted Raman scattering event and b) anti-stokes shifted Raman scattering event. E i.is the energy of the initial vibrational state of the molecule before the scattering event and hν i is the energy of the incident photon. E f1 and E f2 are the energies of the final vibrational states of the molecule after the Stokes Raman scattering event and anti- Stokes Raman scattering event, respectively. The energy difference between the vibrational states and virtual states is not drawn in proportion. The incident light, which is scattered by the molecules in the sample, is usually a laser light in the visible part of the electromagnetic spectrum (e.g. argon laser line at 488 nm, which corresponds to 2.5 ev). The energy difference between the observed vibrational states (e.g. approximately 0.3 ev) is much smaller than the energy of the incident light and corresponds to the IR part of the electromagnetic spectrum. The scattered light is of similar energy (approximately 2.2 ev, which corresponds to approximately 564 nm) as the incident light and is usually observed in the visual spectrum as well. Therefore, it is essential that a filter is used in front of a detector to block the reflected incident light and elastically scattered light. Furthermore, because the valuable information is related to the vibrational energies, the energy of the detected light is subtracted from the energy of the excitation light (e.g. 2.5 ev). In this way the Raman spectra that are independent of the 11

20 excitation energy are extracted and usually presented as the intensity of the detected light as a function of wavenumbers. Wavenumbers are commonly used in the spectroscopic community and are defined as the reciprocal of the wavelength reported in cm 1 [43] Fourier transform infrared spectroscopy Another commonly used spectroscopic method is the Fourier transform infrared spectroscopy (FTIR). The key idea of infrared spectroscopy in general is to observe the absorption of light by the sample in the infrared (IR) region of the electromagnetic spectrum. The IR part of the spectrum is of an interest since it corresponds to the vibrational energies of molecules of many substances. The sample is illuminated with a light source of a broad spectrum and then the missing (absorbed) parts of the spectrum are analyzed. The first IR spectrometers used a prism or a grating to disperse the light. Introducing interferometers instead of dispersive elements in the setup and the Fourier transformation of the measured signal changed IR spectroscopy. Nowadays, the FTIR spectrometers are standard equipment of almost every analytical chemistry laboratory in the developed world [45]. The first step in the FTIR spectroscopy is to record an interferogram. In the case of the Michelson interferometer adapted spectrometer (Figure 2) the light from a broad IR source is split by a beam splitter. One beam is reflected by a fixed mirror and the other by a movable mirror. By changing the position of the movable mirror, the optical path difference between the two split beams is changed. One beam is therefore phase retarded with the respect to the other when they interfere after being recombined at the beam splitter. At a given retardation (i.e. mirror position) only those components of the broad IR spectrum interfere constructively, for which the wavelengths are a multiple of the optical path difference. By changing the mirror position different components interfere constructively and the intensity is recorded by the detector with respect to the optical path difference, which is represented in an interferogram. This way a Fourier transformation of the broad source s IR spectrum from the wavenumber domain into the optical path difference domain is recorded. The second step is the inverse Fourier transformation of the recorded interferogram, by which spectral information in the wavenumber domain is extracted. Normally an examination of a sample is desired, so after the beams are recombined they are focused on the sample. Components in the IR region of the beam, which correspond to the vibrational energies of the examined sample, are absorbed. The difference in the spectrum between the reference spectrum (no sample in the setup) and the measured spectrum of the sample is calculated and presented as the IR spectrum of the sample [45]. The main advantage of the FTIR spectrometers over the IR spectrometers with a dispersive element are shorter measuring times. In case of a dispersive element based IR spectrometer only one spectral component is detected at any moment. With increasing resolution more measuring intervals are needed, which means longer measuring time. On 12

21 the contrary, by the FTIR setup all wavelengths are measured the entire time, which is also called the multiplex advantage [45]. Therefore, it is possible to perform the transmission, reflection, or attenuated total reflection (ATR) measurements much faster using the FTIR spectrometers. Figure 2. Setup of Michelson interferometer adapted FTIR. Light of broad IR spectrum is split into 2 beams by a beam splitter, which ideally transmits and reflects 50 % of light. The beams are reflected by mirrors. The beam reflected from the moving mirror is delayed with respect to the other beam. After the beams are recombined they interact with the sample (transmission mode). Finally the intensity is recorded as function of mirror displacement. In case of reflection measurement the sample would be positioned in the location of the stationary mirror. Source: Depending on the problem we want to observe, there are three different types of FTIR setup, which can be used. The most straightforward way is the transmission setup, where the light transmitted through the sample is observed. Transmission setup cannot be used for highly absorbing materials. The absorbance depends on the type of the observed material as well as on its thickness, therefore usually samples have to be adjusted, i.e. cut into thin layers. In some cases it is also hard to mount the sample in the transmission setup and then the reflection setup is used. However, if samples have rough edges or are of irregular shapes, a reflection setup can be ineffective, due to the refractive errors [46]. With coated samples (e.g. drugs) we have another problem: the reflected light carries information only about the surface layer of the sample. The third approach is ATR. The IR beam is coupled into a crystal of high refractive index, which is then placed in contact 13

22 with the sample of a lower refractive index. At an incident angle higher than the critical angle no light is transmitted into the sample and the light is totally reflected from the crystal-sample interface. However, the evanescent field penetrates the sample. Therefore the totally reflected beam carries the information about the sample outer layer of thickness of the penetration depth of the evanescent field. Because the light is totally reflected, a better signal-to-noise ratio can be achieved with ATR than in the reflection mode. The FTIR spectrometers can be coupled with microscopes and are used to improve spatial resolution of the spectroscopy. The method is usually referred to as a micro FTIR spectroscopy Spectroscopic techniques and microplastics The research showed that many small particles that were considered to be of plastic origin by visual inspection turned out to be of natural origin or from the artificial cellulose-based polymers [10, 47]. The use of spectroscopic methods is therefore of huge importance for proper determination of the microplastic abundance and plastic type [7]. The most appropriate techniques used so far are FTIR and Raman spectroscopy [7]. The two methods can deliver in many cases complementary information about the sample [43], but usually only one of them is used, which seems to be sufficient for microplastic identification. The Raman spectroscopy can theoretically provide better spatial resolution (below 1 μm) than FTIR, due to the shorter wavelengths of the excitation light used. The spatial resolution is an important consideration for microplastic, due to its small size. However, a major drawback could be the interference of the Raman scattered light with the possible photoluminescence of the sample [48]. One way to avoid this would be the use of the FT- Raman spectrometers by which photoluminescence free spectra can be recorded [45]. However, the FT-Raman spectrometers are rarely used compared to the Raman spectrometers, mainly due to the high availability of the sensitive CCD detectors in the range of commonly used laser sources in the Raman spectrometers [45]. Also relatively long integration times (2s) are needed and the need for several co-scans of the sample [9] might present a limitation for further automatization of the microplastic detection by raster scanning samples. The FTIR spectrometers are widely used for the microplastic determination despite of a worse theoretical spatial resolution (approximately 10 μm [45]) due to longer wavelengths. One advantage of the FTIR over the Raman spectroscopy is, that it is not affected by photoluminescence. On the other hand, samples need to be completely dry, because the IR radiation gets absorbed by water. A problem might also occur when examining black particles, because they absorb the IR spectrum as well [48]. All three previously described modes of operation have been used for microplastic detection. The reflection mode enables an easy sample observation and also provides a possibility for the sample mapping [46]. However, the measured spectrum is largely affected by 14

23 irregular microplastics shapes, which is not the case for the ATR mode, which can be seen in Figure 3. The downside of the ATR mode is that it is time consuming because the ATR crystal has to be moved and put in a contact with every examined particle. The third used mode is the transmission mode, for which the use of an IR transparent sample holder (usually filter) is essential. Furthermore, in case of thicker and absorbing materials the transmission mode is not sensible [48]. Figure 3. Absorbance spectra of the low density polyethylene (LDPE): obtained by micro FTIR reflectance spectroscopy for a) granule shaped, b) square shaped fragment and by ATR-FTIR spectroscopy for c) granule shaped and d) square shaped fragment, respectively. Irregular shape of examined particles can drastically effect the spectra in reflection mode, which can be clearly seen by comparing spectrum in part a) to the other three spectra in the figure. Reprinted from [46] with permission from Elsevier. FTIR spectroscopy was used for chemical mapping and imaging of pre-processed samples. We believe that chemical mapping and imaging are the state of the art approaches used till now in the sense of automated detection process and reduced possibility of human errors in the process of microplastic identification. The mapping by the raster scanning of samples collected in the lagoon of Venice after being preprocessed was done by using the micro FTIR in the reflection mode [49]. The spatial resolution was 15

24 50 μm. Only 12 parts of the sample were selected for mapping as the best compromise between the reliability and workhours. The results are encouraging but the approach suffers from the long examination times, interference with the background signal from the filter and the refractive errors from irregular shapes of microplastics. Chemical imaging of a part of a sample or of the entire sample was performed using a focal plane array (FPA) based micro FTIR spectroscopy. Two different studies were done. In the first, the FPA based micro FTIR spectroscopy in the reflection mode with a spatial resolution of 25 μm was used [50]. Additionally, two co-scans and the spectral resolution of 16 cm 1 were selected. The imaging of the whole sample, which was placed on a filter with a diameter of 47 mm, was done in less than 16 hours. The imaging speed is equivalent to raster scanning a sample taking approximately 20 ms per scanning point, considering equal lateral resolution. The plastic particles were first exposed to a hydrogen peroxide (H2O2) to test the effect on the IR spectra. No major change in the spectrum was detected. The H2O2 treatment is suggested for the removal of the organic material from the sample, since presence of organic material can make the detection troublesome. The FPA based imaging method was validated on six different plastic types at a known location. Waste water samples were also spiked with plastic particles. Then they were centrifuged and extracted and finally treated with H2O2. The recovery rate was 98 % except for nylon6, which could not be detected. The results make the FPA based reflectance micro FTIR imaging combined with the H2O2 pretreatment an effective approach for the semi-automated microplastic identification from water samples. In the second study the adequacy of the FPA based micro FTIR chemical imaging was further characterized [51]. First, the database of most commonly used plastic types was created by acquiring spectra in the ATR mode for later comparison. Then the FPA based FTIR imaging was tested in the reflection and transmission mode on high density polyethylene particles of the size 0-80 µm. Different filters used for capturing preprocessed samples from the environment were also tested in both modes, since they present a sort of a sample holder. Although both modes provided 20 μm lateral resolution, the transmission mode resulted in better imaging results in combination with an aluminum oxide filter. The aluminum oxide filter showed the lowest absorption in the most important absorption bands for plastic recognition. Then the best imaging parameters were determined to minimize the required imaging time and to acquire spectra of a sufficient quality as: 20 μm lateral resolution, spectral resolution of 8 cm 1, spectral range cm 1 and 6 co-scans. With this set of parameters a square with an edge length of mm could be imaged in 645 min, which means that the raster scanning would have to take approximately 140 ms per scanning point, considering equal lateral resolution. This is longer than by the previously presented FTIR imaging. This is expected, because a better spectral resolution and more co-scans were selected. Finally, the imaging was successfully tested on different types of microplastic and samples from the environment as well. However, it needs to be pointed out that the approach was tested 16

25 on particles smaller than 120 μm and the authors also warned that for thick particles the spectra might not be readable due to the total absorbance by the particles. The FTIR mapping and imaging were applied recently (in 2013 and 2015, respectively) to the microplastic problem. Both methods are available as commercial products. Current results are very promising, especially the FPA based imaging method, because it collects the data from many points simultaneously, which results in a better time efficiency of the examination. The imaging speed can also vary depending on the selected spectral range, spectral resolution and number of co-scan. Furthermore, the reflection and transmission mode suffer from some drawbacks. First of all, samples need to be completely dry to avoid absorbance. An additional problem of the transmission mode is absorption by thicker and black specimen. The reflection mode mainly suffers from the refractive errors due to the irregularly shaped particles. Therefore, we decided to investigate another possible method for the microplastic detection. Our idea is to check if different plastics show photoluminescence after being excited with a laser light of specific wavelength. Since the photoluminescence is also used as a spectroscopic method and can be observed rapidly after excitation [52], we hope that it could be an appropriate method for a fast and automated chemical mapping of pre-processed microplastic samples. 17

26 3 PHOTOLUMINESCENCE SPECTROSCOPY Luminescence is a phenomenon of material emitting light that does not result from heat (i.e. black body radiation). Therefore, it is sometimes referred to as cold light. Depending on the source of energy, luminescence can be generally classified as fluorescence, phosphorescence, chemiluminescence and triboluminescence [53]. Triboluminescence can be observed after a release of energy, when certain crystals are broken. With the other three forms of luminescence the energy is emitted by the relaxation of a material from a higher energy state into the ground or any other lower energy state. If a material is excited to a higher energy state by a chemical reaction, then this type of luminescence is called chemiluminescence. When a material is excited by an absorption of the electromagnetic radiation (e.g. by UV light), the luminescence phenomenon is called photoluminescence (PL) and can be, depending on the relaxation time, classified as fluorescence or phosphorescence. Fluorescence occurs, so to say, instantaneously after the excitation, whereas phosphorescence relaxation time is longer and can last for minutes or longer after the excitation. PL can carry valuable information about the observed material and is therefore used in many fields, for example in material science, agriculture, biology, medicine, pharmacy, and more [53]. Our idea for detection of microplastics is to illuminate parts of the sample by a laser light and observe the photoluminescence signal. We expect that plastic shows PL signal, which differs from other substances present in the microplastic samples. Furthermore, we hope that we could distinguish between different plastic types and reach sufficient spatial resolution for detection of microplastics. In this chapter we first present the mechanism of photoluminescence, then the constructed setup for sample examination and its characterization. Finally, the process of acquiring and manipulating the PL spectra is presented. The results acquired with the setup are presented in the next chapter. 3.1 Photoluminescence The basic mechanism of photoluminescence can be described via the Jablonski diagram (Figure 4) in which three stages are represented: absorption, system transitions and photoluminescence in a form of fluorescence or phosphorescence. There are two types of electronic states important for the understanding of the photoluminescence mechanism: singlets and triplets. A pair of particles with a spin of 1/2 can form three states for which the value of total spin angular momentum is 1, i.e. triplets, and a single state for which the value of total spin angular momentum is 0, i.e. singlet [54]. The electron in the excited orbital and the electron with the anti-parallel spin in the ground-state orbital are paired in the excited singlet state [55]. Vibrational states of a much lower energy are superimposed on these electronic states, which are indicated by numbers on the right hand side of every electronic state in Figure 4. Consider a molecule showing a photoluminescence signal, often called fluorophore [52], in a non-excited singlet state S0,0, where first index corresponds to the electronic and the second to the vibrational state. With a photon 18

27 absorption the fluorophore is excited to an excited singlet state (S1, S2, or higher), which happens on a femtosecond timescale. The absorption is followed by a quick internal conversion and vibrational relaxation (on a picosecond timescale) that brings the fluorophore to the S1,0 state. These two processes are non-radiative. There are three ways for a molecule to return back into the singlet state from the unstable, excited S1,0 state. The first possibility is, again, a non-radiative transition, the second is emission of a photon, i.e. fluorescence, which happens on a nanosecond timescale, and the third is a non-radiative transition to an excited triplet state. The transition to the triplet state is called intersystem crossing and occurs through the process of the spin-orbit coupling [56]. Once the molecule is in the triplet state it can undergo a non-radiative transition or emits a photon, which is called phosphorescence. The triplet states are more stable because the dipole radiative transitions between the triplet and the singlet state are forbidden. Therefore it takes more time (longer than a millisecond) for the phosphorescence to take place. Once the fluorophore is back in its ground state the whole process can be repeated. Figure 4. The Jablonski diagram of a photoluminescence process. In the upper part the energy levels of a fluorophore corresponding to the possible states of the fluorophore are sketched. Electronic states are labeled on the left side of the level. S 0 corresponds to the ground singlet state, and S 1, S 2 to the excited singlet states. T 1 corresponds to the excited triplet state. Vibrational states (numbers on the right side of the energy levels) are superimposed on both, the singlet and triplet states. The dotted arrows correspond to nonradiative transitions and full arrows to radiative transitions. In the bottom from left to right, the absorption, fluorescence, and phosphorescence spectra corresponding to the above transitions are plotted. Reprinted with permission from [56]. 19

28 Fluorescence normally occurs when the fluorophore is in the S1,0 state. For the fluorophore to reach this state, it has to undergo some internal conversion that usually results in the heat dissipation. The energy difference between the first excited state that the fluorophore enters after the excitation (e.g., S2,0) and the S1,0 state is reflected in the lower energy of the emitted light, which corresponds to longer wavelengths. Therefore the peak in the fluorescence spectrum is shifted to longer wavelengths with respect to the absorption spectrum. This shift is called the Stokes shift. The spectrum of phosphorescence is shifted to even longer wavelengths, which corresponds to the additional loss of energy in the intersystem crossing. Despite of the discrete energy levels of the states, the absorption and PL spectrum are broad and continuous, often referred to as the absorption and emission bands. One reason for the broad and continuous spectra is the high density of vibrational states on which rotational states are superimposed, which is described as homogeneous broadening. The second reason is the inhomogeneous broadening, which results from the movement of molecules in solutions and local imperfections in the structure of solids [56]. The PL will be observed, if the fluorophore is irradiated by light with wavelengths within the absorption band. The shape of the emission spectra, corresponding to the absorption band, is independent of the excitation wavelength [55]. However, the intensity of the PL depends on the excitation wavelength [57]. If the fluorophore is illuminated by light with a wavelength that gets highly absorbed, the intensity of the emitted light will also be higher than in the case of an excitation by light with wavelength that gets poorly absorbed. Therefore, PL is sometimes described as a resonant effect. The PL intensity also depends on the intensity of the excitation light. The higher the intensity of the excitation light, the higher the PL intensity will be, since more fluorophores in a sample will be excited and followed by PL. Again the change in the excitation intensity does not affect the shape of the emission spectrum. However, with high excitation intensities it is more likely that the fluorophores get damaged in a way that the whole absorption-internal conversion-pl cycle is permanently broken. This leads to a decrease in the PL intensity and is usually referred to as photobleaching. Since the PL spectrum is shifted towards the longer wavelengths with respect to the absorption spectrum, the PL can be easily observed by suppressing the excitation light. This is usually done by a combination of band pass and long pass filters. To determine the applicability of the PL for microplastic detection, we built a cost-efficient setup, which is described in the next section. 3.2 Experimental setup When planning the experimental setup we kept in mind our goal, which is an automated detection of microplastics. Additionally, we needed to make the setup cost-efficient for a potential later application. We therefore chose a diode laser of 405 nm as a light source, because they are cheap due to their widespread use in the Blu-ray technology. The short 20

29 excitation wavelength of the laser provides a sufficient lateral resolution of approximately 1 μm, which is important for microplastic detection. Furthermore, the use of a powerful laser makes it possible to illuminate samples with a light of high intensity. In this way a high enough number of fluorophores can be excited to detect the PL. We first concentrated on the volume samples for PL detection to determine if the PL spectroscopy, with a 405 nm wavelength excitation light source, can be used for the detection of plastic and its differentiation. For this purpose we built a setup (Figure 5), where the incident light beam, sample, and detector are all positioned in a line, which makes the alignment of the setup straightforward. This kind of a setup has already been used for PL observation of plastic (polyethylene) [58]. Keeping the final goal in mind, which is an automated detection of microplastic, the setup can easily be upgraded for raster scanning a sample by simply moving it. Furthermore, it can be adjusted in a way that the sample lies in the horizontal plane, which would make it easier to mount real samples. In the next subsections the most important components of the setup will be addressed and described. This will be followed by a description of the setup construction, alignment and the problem encountered. Figure 5. Sketch of the constructed setup for the PL observation (top view). The blue and red arrows indicate the propagation of laser and PL light, respectively. Pinhole 1 and pinhole 2 present the two positions of the pinhole used for the setup alignment. The pinhole is not present in the aligned setup. 21

30 3.2.1 Diode laser and excitation filter For the excitation we used a blue-violet diode laser of the class 3B, with following specifications provided by the manufacturer: the central wavelength of (405 ± 5) nm and a typical output power of (200 ± 20) mw. Additionally, a transistor (TTL) control of the laser is possible. The laser beam is collimated by an integrated triplet lens with a diameter of 5 mm. Firstly, we decided to further characterize the laser and measured the efficiency curve of the laser (Figure 6). The laser is operated within the range where the laser output power and the pump current are linearly dependent. This range starts from the threshold current (139 ma, which corresponds to 5 mw output power) at which the lasing starts and goes up to 220 ma corresponding to 210 mw, which is the specified output power of the laser. The output power can be calculated from the linear relationship between the output power and the pump current, which was acquired by fitting a line to the efficiency curve (Figure 6). Furthermore, the output power of the laser can be controlled by changing the pump current, which can easily be measured. Controlling the output power is important since the intensity of the PL depends on the excitation power. On the other hand, too much power can also damage the sample. Figure 6. Laser efficiency. The laser output power (P) as a function of the pump current (I). The linear relationship between the P and I (P = ΔP/ΔI I + P 0 ) was determined as: ΔP/ΔI = (2.55 ± 0.01) W/A and the P 0 = (351 ± 2) mw. Due to an encountered problem, which will be explained later, we introduced an excitation filter to the setup, which shapes the spectrum of the laser. The excitation filter used is a premium bandpass filter, which transmits a narrow band of light centered at 405 nm (Figure 7). The light with wavelengths outside this band is strongly attenuated, since the optical density (OD) is higher than 5. The spectrum of the laser light and the 22

31 effect of the excitation filter on it are presented in Figure 8. The effect of the filter on the laser spectrum seems to be negligible. This is due to the limited dynamic range of the spectrometer. The excitation filter is crucial for the reliability of the setup, as it will be shown later. In the next subsection two additional filters are presented, which are used to prevent the excitation light to enter the detector. Figure 7. The transmission (T, black curve) and optical density (OD, blue curve) of the excitation filter as a function of wavelength. The source of data: Figure 8. Spectrum of the laser light (intensity (I) as a function of wavelength (λ)) without (black) and with (red) the excitation filter. 23

32 3.2.2 Longpass filters and laser light polarization The use of filters that effectively block the excitation light is essential for the observation of the PL, since it is expected for the PL to be much weaker than the excitation laser light [58]. In our setup we used two longpass filters to block the excitation light from reaching the detector. We chose a 25 mm round dichroic longpass mirror with a cut-off at 425 nm, which means that it reflects wavelengths shorter and transmits wavelengths longer than 425 nm. This mirror is designed for the reflection at an incident angle of 45. Dichroic mirrors are produced by a controlled deposition of multiple alternating thin layers of material with low and high refractive index on a transparent substrate. A precise production is of outmost importance. Since the dichroic mirror alone did not suffice to block the excitation laser light from the detector, an additional colored longpass filter made of the Schott glass with a 420 nm cut-off wavelength was introduced into the setup. The transmission curves of the two filters are presented in Figure 9. The total transmission of the dichroic mirror differs for the p- and s-polarized light. The s-polarized incident laser light is preferred, because it is better suppressed at wavelengths around 405 nm (the main component of the laser light). The polarization of the laser light is determined by a proper mounting of the laser with respect to the dichroic mirror. Figure 9. Transmission (T) of the two longpass filters used in the setup as a function of wavelengths (λ). The black line presents the transmission of the colored filter and the red line presents the transmission of unpolarized light for the dichroic mirror (DM). The green and the blue lines present the total transmission of the p- and s-polarized light, respectively. The total transmission of the polarized light is calculated as a product of the reflectivity and transmission, since the excitation laser light is first reflected and then transmitted by the dichroic mirror in order to reach the detector. The source of data: The p- and s- polarizations of light are defined with respect to the plane of incidence. The p-polarized light has the electric field parallel to the plane of incidence and the s-polarized 24

33 light has the electric field perpendicular to the plane of incidence. The polarization can be distinguished by the existence of the Brewster angle. For the p-polarized light, there exists an angle of incidence at which no light is reflected (the Brewster angle). In order to determine the polarization of light we modified the setup (Figure 10). By introducing a linear polarizer of an unknown polarization direction to the setup, we made sure that the transmitted light was either p-polarized or s-polarized with respect to the incident plane of the sample, which was in this case a sheet of PET. The sample was mounted in a way that its plane of incidence was identical to the incident plane of the dichroic mirror. The linear polarizer was mounted in a way that the majority of light was transmitted at the current position of the laser. While the angle of incidence was being changed by turning the sample, the intensity of the reflected light was observed on the screen. At a certain angle no light was seen on the screen and at larger and smaller angles the reflected light was visible. This made it clear that at the selected position of the laser, the light was strongly p-polarized. Because we favored the s-polarized light with respect to the dichroic mirror, due to its transmission characteristics, we flipped the laser by 90 so that the laser light was strongly s-polarized. Figure 10. Sketch of the adjusted setup for the determination of the laser light polarization (top view). 25

34 3.2.3 Achromatic triplet lenses and optic fiber In the setup we used two identical lenses. The first one (sample lens, see Figure 5) was used to focus the collimated laser light into a small dot on the sample. If the sample showed PL, the PL light originated form the same small dot on the sample and propagated through space without any preferred direction. PL light source can therefore be ideally considered as a point source and by the correct alignment of the setup, the same lens, which focuses the laser light on the sample, collimates the PL light. This ideal case holds for thin lenses (no aberrations) and if the sample is positioned directly in the focus of the lens. The second one (fiber lens, see Figure 5) is used to focus the collimated PL light and couple it into the optic fiber, which guides the light to the detector. The selected lenses are achromatic triplets, which means that they are constructed from three parts in a way to avoid chromatic aberrations. Since the PL can span over a range of wavelengths it is important that lenses used for collimating and focusing do not show chromatic aberrations. The used lenses exhibit a small focal length shift (Δf) within the desired range and a constant, high transmission for the visible light (Figure 11). Additionally, they provide good on-axis as well as off-axis performance. Figure 11. Transmission (T) and focal length shift (Δf) as a function of wavelength (λ) of light for the achromatic triplet lenses used in the setup. The source of data: When choosing the lenses for the setup, two additional properties of lenses were considered, beside the achromatic correction. First, we wanted to collimate as much PL light as possible. Considering the excited part of the sample that exhibits PL as a point source, a lens with a large diameter and a short focal length was required. The other lens, however, was used for coupling the light directly into a multimode optic fiber with a numerical aperture of For both applications identical lenses were chosen with a diameter of 25 mm and focal length of 40 mm. Considering the way the dichroic mirror 26

35 was mounted, the outer part of the collimated beam was blocked by the mirror holder and a beam with waist diameter of approximately 13 mm reached the fiber lens. The calculation shows that the beam waist in the focal point of the lens is smaller than 2 µm and the divergence is smaller than 0.2. Therefore, by the precise positioning of the multimode optic fiber in the focal point of the fiber lens, all light, which reaches the lens, is successfully coupled into the fiber. The optic fiber is coupled to the LR1 compact spectrometer from ASEQ instruments. The spectral range of the spectrometer is nm with the resolution better than 2 nm Construction and alignment of the setup A stable and precise alignment of optical components of the PL setup was one of the essential tasks to enable reliable measurements of a variety of samples and to avoid possible effects of a misalignment of the setup on the results. The first construction was far from perfect with many errors but already produced some results. However, the setup was realigned several times, after some strange behavior had been noticed and additional or different components were introduced (e.g. an excitation filter, a different color filter, new silver mirrors). The sketch of the final setup is in Figure 5. The setup was constructed on a breadboard, which makes the construction easier due to precisely and regularly spaced holes with screw threads, to which posts can easily be mounted. We decided to use posts with a height of 75 mm. The height seemed to be appropriate for an easy access and not too high for possible vibrations to have a significant effect. To these posts, different holders for lenses, mirrors, and filters could be easily mounted. The first task was to determine the reference height. We defined the reference height as the center of the lens mounted on a post. Because the posts of the two lenses are directly screwed to the breadboard, this height is fixed. Less flexibility also provides less components to be misaligned. The reference height was measured with a ruler and determined as (101 ± 1) mm. After determining the reference height, the diode laser was mounted at a similar height. Two mirrors were then introduced to the setup to set the desired height and direction of the laser light. For this purpose two points in space at the desired height and desired locations were needed (a line is uniquely defined by two points). First, the pinhole was mounted on a 50 mm post, which could be mounted on a post holder. Then the height of the pinhole was set as accurately as possible to 101 mm using a ruler and a post collar, which makes possible fixing the height of the post with respect to the post holder and also small height adjustments. Two post holders were placed as far away as possible on the breadboard on the desired line of breadboard holes. The key idea to achieve a good alignment, which was followed during the setup construction, is to always try to make the distances as long as possible between the optical elements for the alignment of the setup, but make the distances as short as possible when mounting the elements. This way, small angular misalignments of optical elements at long distances result in even smaller misalignments at shorter distances. The height of the laser beam was set with this in mind. 27

36 The positions of the pinhole in the sketch of the setup (Figure 5) gives the reader an idea on where they were placed. Since at this point only the laser and two mirrors were mounted on the breadboard, the distance between the pinhole positions (pinhole 1 and pinhole 2 in the sketch) were longer with respect to the distance between mirror 2 and the dichroic longpass mirror. The laser beam was aligned by repeating the following sequence: change the tilt of mirror 1 to make the beam go through the center of the pinhole 1 and then change the tilt of mirror 2 so that the beam goes through the center of the pinhole 2. This procedure was repeated many times so that the beam finally propagated through the center of the pinhole at both locations without changing the tilt of any mirrors. This way it was certain that the beam was at the desired height and propagated in the desired direction. The described procedure of directing the beam is called a beam walk. The reference height (height of the pinhole) might not have been correct, because of the systematic and random errors affecting the determination of the reference height. Therefore, two additional silver mirrors were introduced to get the beam at a different desired height and direction. The new desired direction was parallel to the normal to the focal lens plane and centered at the center of the lens. The lens was positioned at its final location (sample lens in Figure 5) and it was made sure that the normal to the lens plane was parallel to the breadboard using a water scale. First, the height of the beam was set to the pinhole height by a beam walk using the additional mirrors and the pinhole. Then the lens, which could be easily mounted back into the position, was used to focus the aligned beam at the reference height. In this case, the lens should focus the beam exactly in the center of the closed pinhole positioned in the focal plane. Since it did not, this meant that the reference height of the pinhole was set wrong and needed to be readjusted. This was done by changing the height of the pinhole so that the beam was focused through the center of the pinhole. Then the lens was removed and the height of the beam was set by a beam walk. With a couple of repetitions both the height of the beam and the pinhole were set to the correct height. Then the additional mirrors were removed and the beam alignment with mirrors 1 and 2 was repeated due to the new, corrected pinhole height. This way the beam was parallel to the breadboard at the desired height and direction. The last part of the construction of the setup was the positioning of the dichroic longpass mirror. It was mounted on a 45 mirror holder and positioned on a linear translation stage to enable precise positioning. The height was positioned to the reference height using a ruler, because the precision of approximately 1 mm sufficed due to a larger surface of the mirror. Then, the two positions of the pinhole were set on the line defined by the positions of the lens and the dichroic mirror, the first as close as possible to the dichroic mirror and the second as far away as possible. The linear translation stage was used to move the dichroic mirror back and forth in the direction of the incoming laser beam from mirror 1 (Figure 5) in order for the laser beam to hit the pinhole in the position closer to the mirror. Then the pinhole was positioned in the more distant position and the tilt was adjusted so that the beam hit the center of the pinhole. The procedure was repeated until the beam 28

37 propagated through the center of the pinhole at both positions without changing the tilt or the position of the dichroic mirror. Finally, the other focusing lens was introduced to the setup as close to the dichroic mirror as possible to reduce possible alignment errors. Between the dichroic mirror and the focusing lens an additional color filter was introduced, because the dichroic mirror does not attenuate enough of the reflected laser light from the sample. In the end an improvised sample holder was introduced to a three axis translation stage and the optic fiber, which is used to couple the PL signal to the spectrometer. The optic fiber was mounted to a 3 axis linear translation stage. The stages made it easier to position the sample or optic fiber in the focal point of the two lenses. There was also enough space between mirror 2 and the dichroic mirror to introduce an ND filter when needed. Additionally, there was enough space to introduce the excitation filter, after encountering some problems while characterizing the setup Characterization of the setup and the problem encountered After several realignments, the first test measurements were done. The light with longer wavelengths, unlike the wavelength of the excitation laser light, could be easily detected, which was expected due to the nature of the PL. A quick testing of the setup on several different plastic volume samples showed that the detected light, which was expected to be of PL origin, was of relatively low intensity (long integration times and high excitation powers needed) and of similar spectrum ranging from 420 to 460 nm. The similarity of spectra raised some concerns for the applicability of the setup for microplastic detection and plastic differentiation. Despite the problem, we decided to characterize the setup taking into account its sensitivity to possible misalignment of the sample in the focal plane and the possible effect of the tilt of the sample on the PL light intensity. With small displacements (smaller than 2 mm) of the sample from the focal plane, the signal was still easily detectable. Then the tilt of the sample was changed so that the normal of the sample plane was not parallel to the normal of the plane of the sample lens. Considering that the PL should have no preferable direction of the emission of light, the intensity of the signal should not change drastically at least for small angles (smaller than 10 ). However, a drastic change in intensity of the detected signal was observed by changing the tilt of the sample, which indicates that the detected light has a preferable direction of propagation with respect to the normal of the sample plane. Additionally, the sample lens was removed. If the observed light had no preferable direction of propagation, a much weaker signal or no signal at all should be detected. However, this did not happen. Furthermore, by slightly tilting the sample (less than 1 ) the signal could not be detected anymore. This led to the conclusion that the observed light has to be the reflected and backscattered excitation laser light due to its preferable direction of propagation. However, the laser was not expected to emit light of the observed wavelengths (420 to 460 nm, Figure 8). Because 29

38 of the limited dynamic range of the spectrometer, it is possible that the measurement presented in Figure 8 fails to show the laser emitting light with wavelengths up to 460 nm. Furthermore, there are two longpass filters used in the setup, which efficiently block the main laser component but transmit light with longer wavelengths (longer than 420 nm). Therefore, we decided to introduce the excitation filter, which was described above, to shape the laser light spectrum in a way that the light of wavelengths longer than 420 nm is strongly attenuated (Figure 7). The effect of the excitation filter on the setup was tested by placing a silver mirror, which is expected not to show PL, in the focal plane of the sample lens. The spectrum of the detected light was then observed (Figure 12). When the excitation filter was used, no potential PL light could be observed, even with high excitation power of approximately 100 mw being used. Because of the high power used and high reflectance of the silver mirror, the main component of the laser light could be noticed at long (800 ms) integration times. When the excitation filter was not installed to the setup, a broad spectrum light at longer wavelengths (longer than 420 nm) could be noticed, even though it was expected that the silver mirror would not show photoluminescence. This confirms that what we observed before was laser light and not PL light and that the use of the excitation light is essential in order to observe the PL using this setup. Figure 12. Spectra of the reflected light from the silver mirror placed at the sample position with (black line) and without (red line) the excitation filter. The excitation power of approximately 100 mw was used. The integration time differed: 800 ms when the excitation filter was installed and 2.4 ms when the excitation filter was removed. After solving the described problem, the sensitivity of the setup to the misalignment of the sample was determined. The sensitivity was tested on a white paper which shows bright PL light at low excitation powers (lower than 1mW) and short integration times 30

39 (10 ms). Furthermore, it shows no noticeable photobleaching on a time scale of a couple of minutes. It is important that the intensity of the detected signal mainly changes because of the possible misalignment and not because of the photobleaching. The optimal (infocus) position was defined at the position of the sample, for which the maximum intensity could be observed. The sample can be moved from the in-focus position closer to the lens, which we call the back focus position (BF) and denote with negative distance values. The second possibility is to move the sample further away from the lens, which we call the front focus position (FF) and denote with positive values. One quarter of the maximum intensity could be observed by the position of the sample from -4 mm to 2 mm with respect to the in-focus position. This is a relatively good result, because a microplastic sample could be raster scanned without refocusing from point to point, considering that microplastics are smaller than 5 mm. The effect of the angular misalignment smaller than 20 did not cause any difference in the intensity of the detected signal. By measuring flat volume samples, bigger angular misalignments were not expected and therefore not considered. The results of the two tests are shown in Figure 13. Figure 13. Characterization of the setup. a) Effect of out-of-focus position (Δf) of the sample on the peak intensity (I, black) and the position of the peak (λ peak, blue). b) Effect of incidence angle of the excitation laser light with respect to the sample plane (θ) on the peak intensity (I, black) and the position of the peak (λ peak, blue). 3.3 Acquisition and processing of the PL spectra In order to determine the adequacy of the PL spectroscopy as an appropriate method for the microplastic detection, the PL spectra from a set of different samples needed to be acquired. Being consistent while acquiring the PL spectra was important in order to achieve reliable and reproducible results. Therefore, a standard measuring procedure, which is described in this section, was used to collect and process the PL spectra. First, an approximate position of the sample and the optic fiber was determined. A sample that exhibited an intense PL light (white paper) was mounted to the three axis translation 31

40 stage and positioned approximately in the focal plane of the lens. Then the sample was illuminated by the excitation laser at low powers and the PL was detected. The positions of the optic fiber and the sample were changed until the intensity of the signal could not be increased anymore. In this way the coupling of the PL into the spectrometer was optimized for the following samples, which emitted less intense PL light. The following procedure was done for every sample. The sample was first mounted on the sample holder and positioned approximately in the lens focal point. If no PL was detected, both the integration time and the excitation power were increased, until some signal was detected. Then small adjustments to the position of the sample were made, so that the PL s intensity was highest. The position of the optic fiber was also checked, however, usually no changes to the position were needed. The PL signal was then recorded while the lights were off in order to observe only the PL light emitted by the sample. Several PL spectra from different parts of the sample were collected for every sample. Additionally, a signal was recorded at the chosen integration time for the selected sample while the excitation laser light was blocked and other conditions unchanged. In this way we recorded the spectrum of the possible background radiation, which was later subtracted from the PL signal to avoid any artifacts in the signal. After recording several (usually 10) PL spectra and the background spectrum, the data was processed using the Matlab software. For many samples long integration times (longer than 500 ms) were needed to detect the PL signal, which resulted in a noisy signal. Smoothing the spectra helped filter out noise. This was done in Matlab using an integrated Savitzky-Golay filter function, which takes two arguments: the frame size and the polynomial order. The frame size of approximately 10 nm was selected and the second order polynomial was chosen, after testing the filter function on polynomials of the order 1, 2 and 3 and frame sizes from 5 to 60 nm. The effect of different frame sizes and polynomial orders on the smoothening of the signal is presented in Figure 14. A polynomial of the first order provided a good fit for broad peaks, but could not sufficiently fit narrower peaks (Figure 14a), unless a short frame size was used. However, a short frame size resulted in worse overall smoothing. The second order polynomial could fit narrower peaks for wider frame sizes (Figure 14b) and the third order polynomial did not show any significant improvement. Therefore, the second order polynomial was selected. The frame size of 10 nm provided a better smoothing than 5 nm frame size and still sufficiently fitted the narrow peaks (Figure 14b). Wider frame sizes did not fit the narrower peaks as well as the 10 nm frame, therefore a 10 nm frame width was selected. The smoothed spectra were normalized to values between 0 and 1, so that they could be easily compared despite different intensities of the detected PL light. The intensity of the PL differed between the samples and already between different points of the same sample. Also the time between the exposure of the sample to the laser light and the start of the spectrum acquisition differed from one scan to another. This also resulted in different PL 32

41 intensities due to the photobleaching. The normalization was therefore necessary. The smoothed and normalized spectra acquired from the same sample were averaged and the standard deviation for every wavelength was calculated. From 8 to 10 spectra, from different locations, were acquired per sample. The results which are presented in the next chapter are the averaged spectra from the same sample. Figure 14. Smoothing of the acquired PL spectra (intensity (I) as a function of wavelength (λ)) using Savitzky-Golay filter function with a) the first order polynomial fit and b) the second order polynomial fit. The red, blue, dark green, and magenta color curves correspond to 5 nm, 10 nm, 15 nm, and 20 nm frame size, respectively. The black curve presents the unsmoothed (original) signal. 33

42 4 RESULTS AND DISCUSSION In this chapter the results of the PL spectroscopy, while keeping in mind the applicability for microplastic detection, are presented and discussed. In the first section of this chapter applicability of the setup and methods used for microplastic detection are discussed. In the second section the acquired and processed PL spectra are presented. This is followed by a discussion of the observed PL spectrum variations in the third section. Finally, in the fourth section the differentiation between the spectra according to the peak position and the pattern recognition using neural networks is presented. 4.1 Applicability for microplastic detection The goal of this work is to check if the PL spectroscopy is a suitable method for microplastic detection and differentiation. Therefore, it is important that the acquired results are representative for the detection of microplastics. The testing of the method was done using the previously described setup (see Section 3.2) on 27 different sample types in order to acquire the PL spectra. The list of all the examined sample types, with the long name of the samples, and corresponding measuring parameters are presented in Table 1. The setup was designed in a way to mount volume samples, because the majority of the available samples were rectangular volume samples. The samples did not meet the microplastic criteria, except for the PVDF (polyvinylidene fluoride) granules which were smaller than 5 mm. The thickness of the volume samples differed from 0.40 mm to 9.96 mm. All the examined plastic samples except for the polyethylene (PE), which was 9.96 mm thick, were thinner than 5 mm at least in one dimension. In this way the size criterion of microplastics at least in one dimension was met. Furthermore, the calculated spot size of the focused laser light in the focal plane is approximately 10 micrometers, so only a small part of the sample was illuminated at the time of each measurement. Examined parts of the samples were smaller than 5 mm in all three dimensions and therefore theoretically fitted the microplastic size condition. Since microplastics can vary in size from the micrometer level (or even smaller) up to 5 mm, it is important that the method can detect microplastics of all sizes. For instance, FTIR in the transmission mode suffers from the total absorption of the IR light by the sample and is therefore unable to detect bigger microplastics. Using the constructed setup for PL spectroscopy it can be used in a way that every sample is positioned in the focus of the sample lens, so that the highest intensity of the PL light can be detected. However, this would be unpractical for raster scanning a sample if the refocusing should be done for every point. Nevertheless, the characterization of the setup showed (see Section 3.2.5) that there is a range of 6 mm, within which one quarter of the maximum PL intensity can still be detected. This indicates that this kind of setup could be used for raster scanning microplastic samples without refocusing. 34

43 Table 1. A list of examined samples with their names, short names (abbreviations) used in the Thesis, and scanning parameters, where P e, t i, and I peak are the excitation power, integration time, and recorded peak intensity, respectively. Short name Name Pe [mw] ti [ms] Ipeak [au] Ipeak/ti [au/100 ms] PET polyethylene terephthalate PP polypropylene PE polyethylene PC polycarbonate nylon nylon PMMA poly(methyl methacrylate) PS polystyrene PVDF polyvinylidene fluoride blue cable tie green cable tie red cable tie white cable tie sand wood Echinocardium Echinocardium cordatum Posidonia Posidonia oceanica Lithophyllum Lithophyllum racemus Monodonta Monodonta turbinata Mytilus Mytilus galloprovincialis Neverita Neverita josephina Sepia Sepia officinalis green glass red algae on PET white paper yellow paper yellow paper It is important that the examination of the preprocessed microplastic samples is not time consuming, in order to monitor microplastic abundance, for which a lot of samples need 35

44 to be acquired and examined. An important information for the applicability of the PL spectroscopy for the microplastic detection is also the integration time and the excitation power needed to detect the PL light. Ideally, excitation light of low and constant intensity for all samples and short integration times would be needed. The low excitation intensity would make it easy to use, since it would be less dangerous for the eyesight and the constant PL intensity would mean that no power adjustment is needed. The short integration times are essential for a quick raster scanning of a sample. The approximate observed values of the measuring parameters (Table 1) for our setup suggest, that rather different excitation powers and long integration times (up to 800 ms for some plastic materials) are needed. In order to perform as fast as the FPA based FTIR imaging, integration times around 100 ms are needed. However, short integration time (2.4 ms) and low excitation power (0.25 mw) were needed for the detection of PL from a white paper. This clearly indicates that using an appropriate excitation light with respect to the material of interest, the PL spectroscopy can be really fast. The problem of high and different excitation powers and long integration times could be solved by introducing a more sensitive detector with a larger dynamical range. Furthermore, in the first step the power of the signal could be measured and then the integration time could be set. Another solution would be to find a light source, which would excite plastic particles much more than the one used. It seems that a light with shorter wavelengths could be more appropriate, since at least for the PE a much more intense emission of the PL light was measured after the excitation with light with shorter wavelengths ( nm) [59]. In this way, ideally, really short integration times (approximately 1 ms) and low powers (lower than 1 mw) would suffice for the microplastic detection. 4.2 Acquired photoluminescence spectra Twenty-seven different materials were examined in order to determine whether PL spectroscopy can be used for the detection of microplastics and its differentiation. In the following subsection the processed spectra (see Section 3.3) are presented and compared in order to determine whether plastic samples can be separated from samples of nonplastic samples according to the exhibited PL spectrum. In the second subsection the effect of plastic incrustation by organic materials on the PL spectrum is presented. This is followed by the observed effect of dyes used in the materials on the PL spectrum, which is discussed in the third subsection Plastic and non-plastic materials The most important question that needed to be answered was, whether plastics can be distinguished from non-plastic materials based on the exhibited PL spectrum. Therefore, we first present spectra corresponding to 18 different samples, 9 of which correspond to the plastic materials and 9 correspond to the materials of a non-plastic origin. The samples were acquired from different sources, so the exact characteristics of the plastic samples 36

45 was not clear. For polypropylene (PP), PE, nylon6, nylon66 and PET at least two different samples from different producers were acquired and no significant difference in the spectrum was noticed. However, a higher number of better defined samples would be needed in order to determine whether possible additives added to the plastic do or do not affect the PL spectrum. The samples of non-plastic origin were collected from the marine environment, except for the sand. In order to make sure that the sand is not contaminated with microplastics, a fine granulated ( mm) quartz sand, usually used in construction, was used. The sand was put into a test tube made out of glass and the PL signal was measured, after making sure that no significant PL light originating from the test tube can be detected. Other samples, which were collected from the marine environments, were the outer part of seashells or sea snails (Monodonta turbinate (Monodonta), Neverita Josephina (Neverita), Mytilus galloprovincialis (Mytilus)), the cuttlebone (Sepia officinalis (Sepia)), small parts of a sea potato (Echinocardium cordatum (Echinocardium)), and pieces of algae (Lithophyllum racemus (Lithophyllum)) and seagrass (Posidonia oceanica (Posidonia)). The spectra are shown in Figure 15. Figure 15. Smoothed and normalized average spectra (intensity (I) as a function of wavelength (λ)) of the detected PL light from a) different plastic samples and from b) different samples of non-plastic origin. The error of the measurement is indicated as the shaded area around each line. Spectra correspond to the materials listed in the legends in the figure. 37

46 The spectra in Figure 15 are average spectra from several different location from the same sample. Additionally, the error calculated as standard deviation at every recorded point in the spectrum is indicated as the shaded area around each line. The procedure of the PL spectrum acquisition and processing is described in Section 3.3. The spectra are partially shaped by the transmission of the longpass filters used in the setup (Figure 9). Their cutoff wavelength is defined by 420 and 425 nm and by 435 nm they transmit more than 80 % each and the transmission does not get much higher (Figure 9). By observing the spectra in Figure 15, a distinct peak at (460 ± 2) nm can be noticed for several plastic samples (PP, PE, polycarbonate (PC), poly(methyl methacrylate) (PMMA), and polyvinylidene fluoride (PVDF)). In spectra corresponding to PC and PMMA an additional peak at approximately (432 ± 2) nm can be noticed. Such distinct peaks were unexpected, due to the PL spectra being generally described as broad spectra. Considering that the Raman spectroscopy is also used for the microplastic detection and that the PL sometimes makes its use troublesome, the Raman shifts with respect to the excitation laser light wavelength ((403 ± 2) nm) were calculated and compared to the possible vibrational energies, which could correspond to the samples. The (460 ± 2) nm and (432 ± 2) nm peaks correspond to the Raman shift of (3100 ± 200) cm 1 and (1700 ± 200) cm 1 expressed in wavenumbers, respectively. According to the plastics structure and literature values of typical vibrations [43], the former shift most likely corresponds to C-H2 and C-H vibrations and the latter to the C-C vibrations. Therefore, we assume that the two observed narrow peaks originate from the Raman scattering. Observing the spectrum corresponding to the PC, another peak can be noticed around 650 nm. The origin of this peak is unknown to us. Considering that the PL describes two different phenomena: fluorescence and phosphorescence (see Section 3.1), the additional peak might correspond to the latter. Another goal of these measurements was to check if the acquired PL spectra differ enough to separate materials of plastic origin from materials of non-plastic origin and if differentiation between the types of plastic is possible. Comparing the spectra in Figure 15, it can be noticed that spectra corresponding to plastic materials have the maximum value of intensity at shorter wavelengths than spectra corresponding to materials of non-plastic origin. Since it is difficult to compare 18 spectra in a single plot, the most similar spectra, considering the plastic and non-plastic origin, are plotted in Figure 16. Considering the microplastic problem, it is essential that the PL spectra acquired from the plastic samples differ from the spectra corresponding to sand, because sand particles are present in the collected sediment samples despite the density separation. Furthermore, wood, which generally floats on water, can also be found in the marine environment and therefore it is important that it can be differentiated from plastic. From Figure 16 it can be seen that the PL spectra corresponding to the sand and wood clearly differ from the PL spectra corresponding to the plastic samples. Additionally, the most similar PL spectra 38

47 corresponding to plastic and non-plastic origin are plotted in order to determine if they can be differentiated. From Figure 16 can also be seen that the most similar spectra still differ and that the maximum intensity value corresponds to different wavelengths for different samples. Therefore the wavelength of the maximum intensity was determined and considered for differentiation between different samples, which is discussed in Section 4.4. Figure 16. Smoothed and normalized average spectra (intensity (I) as a function of wavelength (λ)) of the detected PL light from a) all examined plastic samples (black curve), sand (red curve), and wood (green curve), and from b) different types of plastic and other materials of non-plastic origin, which seem to be similar. Spectra correspond to the materials listed in the legends in the figure Incrustation of plastic Microplastics can stay in an environment for a long time, therefore it is quite common that it can get incrusted by different organic materials. The incrustation of plastic particles by organic materials can make microplastic recognition troublesome. This seems to be true for the PL spectroscopy, as well, considering the PL spectrum corresponding to the red algae on a plastic bottle, which is presented in Figure 17. Considering that plastic bottles are most commonly made out of PET, the presence of algae drastically changes the recorded PL spectrum (Figure 17). An additional broad peak appears at longer wavelengths (around 580 nm) with respect to the peak position corresponding to PET. Furthermore, a bigger variation in the spectrum (bigger errors) can be noticed. This is a consequence of the two peaks varying intensity, which is relative to the location on the sample. This indicates that the peaks are of different origin, one (at shorter wavelengths) corresponding to the bottle (plastic) and the other (at longer wavelengths) corresponding to the red algae. Nevertheless, the peak at shorter wavelengths does not entirely match the PL spectrum corresponding to PET, despite being likely that the bottle is made out of it. However, the peak at shorter wavelengths could be a bit shifted to the longer wavelengths due to the superposition with the broad peak corresponding to the red algae. This could make the identification of plastic 39

48 troublesome, but not impossible, especially if the signal could be decomposed into two single peaks. Figure 17. Smoothed and normalized average spectra (intensity (I) as a function of wavelength (λ)) of the detected PL light from PET (black curve) and red algae on PET (red curve). The error of the measurement is indicated as the shaded area around each line. Considering the final application of microplastic detection, the samples are preprocessed and often chemicals are used to dissolve organic materials, because other spectroscopic methods also suffer from organic incrustation and is therefore usually solved by its removal. However, before using chemicals for the removal of organic materials from samples, a potential effect of chemicals on the samples and the exhibited PL spectrum should be studied. Another problem encountered in microplastic detection by PL spectroscopy are different dyes used in plastic production to ensure a desired color of the plastic. The effect of dyes on the PL spectra corresponding to the plastic samples as well as to paper and glass samples was observed and is presented in the next subsection Dyed materials Plastic products come in many different colors, for which dyes used in the production process of the product are responsible. The PL spectra from plastic samples of different colors were acquired in order to study a potential effect of dyes on the spectra. Cable ties from the same manufacturer and made out of nylon66 were examined. The results are presented in Figure

49 Figure 18. Smoothed and normalized average spectra of the detected PL light (intensity (I) as a function of wavelength (λ)) from cable ties of different colors made out of nylon66 and the nylon66 sample. The blue, green, red, and magenta curves present spectra of the detected PL light from the blue, green, red, and white cable tie, respectively. The black curve presents the PL spectrum corresponding to the nylon66 sample. The error of the measurement is indicated as the shaded area around each line. The PL spectrum corresponding to the white plastic sample matches the PL spectrum, which corresponds to nylon66, completely. The PL spectra corresponding to the green and red colored plastic samples differ the most from the PL spectrum corresponding to nylon 66. For the green sample only a peek in the green part of the spectrum can be noticed. Furthermore, the intensity per 100 ms integration time of the detected PL light was much higher than for the other cable ties and nylon66 (see Table 1), which suggests that this peak entirely corresponds to the dye. In the PL spectrum of the red cable tie an additional broad peak appears at longer wavelengths (around 620 nm) with respect to the peak position corresponding to the nylon66. Again, a bigger variation in the spectrum (bigger errors) can be noticed. This is again a consequence of the two peaks varying intensity, which is relative to the location on the sample. However, the first peak at around 460 nm matches the peak in the nylon66 PL spectrum. An effect of the blue dye used on the PL spectrum with respect to the nylon66 sample can be noticed as a narrower peak at approximately 460 nm and as an additional peak at approximately 680 nm. It is unclear what the origin of the additional peak is. It might, similar as with PC, correspond to the phosphorescence of the dye used in the blue cable tie. The effect of the sample color was also observed when examining a paper of different color. The PL spectra corresponding to the last four examined samples, three different paper samples, and a green glass bottle, are presented in Figure 19. Similar to the dyed plastic, the use of dyes in the examined papers also affected its PL spectrum. The PL 41

50 spectrum corresponding to the yellow paper 1 sample shows the maximum intensity value at approximately the same wavelength (440 nm) as the white paper sample, but the peak corresponding to the yellow paper 1 is wider. In the spectrum corresponding to the yellow paper 2 sample, an additional peak at approximately 540 nm can be noticed. Again, a bigger variation in the spectrum (bigger errors) can be noticed. This is again a consequence of the two peaks varying intensity, which is relative to the location on the sample. The paper samples emitted a relatively bright PL light with respect to other samples (short integration times needed), especially the white paper, for which the lowest excitation power was sufficient and the shortest integration time was needed (see Table 1) in order not to exceed the intensity range of the spectrometer. Figure 19. Smoothed and normalized average spectra of the detected PL light (intensity (I) as a function of wavelength (λ)) from paper of different colors and green glass bottle. The black, red, and blue curves present spectra of the detected PL light from white paper, yellow paper 1, and yellow paper 2, respectively. The magenta curve presents the PL spectrum corresponding to the green glass bottle. The error of the measurement is indicated as the shaded area around each line. Finally, the last sample examined was the green glass bottle. The transparent glass of the test tube, which was used for sand mounting, did not emit any detectable PL light. Nevertheless, a green glass was examined. The PL spectrum corresponding to the examined green glass clearly differs from the PL spectra corresponding to the plastic samples. However, a small additional peak at 540 nm can be seen, which could also correspond to the green dye in the sample. Different colors resulting from the dyes used in a sample are not the only thing that can affect the PL spectrum. It seems that photobleaching, samples moved from the in-focus 42

51 position, and their irregular shapes can have an effect on the shape of the PL spectrum. These effects are presented and discussed in the following section. 4.3 PL spectrum variations It is important that the PL spectrum from the same material does not change while performing measurements in order to be useful for the determination of the material type. Some rather unexpected changes to the PL spectrum were noticed while observing photobleaching, changing the sample position and acquiring spectra form an irregular shaped sample. The variations in the shape of the PL spectra corresponding to the PE are presented in this section. Photobleaching is a decrease of the intensity of the PL light emitted from the sample. It is caused by an irreversible damage to the fluorophore by the excitation laser light, so that the fluorophore cannot further absorb laser light and emit PL light. The expected effect of photobleaching on the PL signal was therefore only on the intensity of the PL light and not on the shape of the spectrum. However, a change in spectral shape was observed while the intensity was decreasing (Figure 20). Figure 20. The effect of photobleaching on the PL spectrum (intensity (I) as a function of wavelength (λ)) of PE. Smoothed and normalized spectra of the detected PL light from the PE at five different times after the exposure to the excitation laser light, where in a) the original spectra and in b) the spectra with the supposed Raman peak removed and then normalized are presented. The black, red, blue, dark green, and magenta curves correspond to 0 s, 1 s, 2s, 30 s, and 60 s exposure time, respectively. The reason for the rather unexpected result is the presence of an additional narrow peak at 460 nm. As previously discussed (Subsection 4.2.1), the additional peak seems to correspond to Raman scattering and is therefore of a different origin than the rest of the spectrum, which corresponds to the PL. This can be clearly seen, if the spectra are normalized to the maximum value after cutting away the supposed Raman peak. In this case the effect of photobleaching on the shape of the PL spectrum seems to be negligible (Figure 20). This also confirms that the additional peak at approximately 460 nm is not of the PL origin and therefore supports the assumption of being the Raman scattered light. 43

52 Finally, the effect of photobleaching is not expected to cause severe problems in the final application, because the samples will be exposed to excitation laser light for an equal and short amount of time. However, potential photobleaching of the microplastics caused by the exposure to the sunlight might still pose a problem for material classification due to a different shape of the spectrum. A similar, but not that distinct effect on the PL spectrum was also observed when the sample was moved from the sample lens focus plane, which is shown in Figure 21. By positioning the sample in the sample lens focal plane, the emitted PL light is collimated by the lens. If the sample is positioned away from the lens focus plane, the PL light is not properly collimated, which cause reduced intensity of the detected light, because less light is coupled into the optic fiber by the fiber lens (see Section 3.2) and guided to the detector. The in-focus position was defined as the position of the sample at which the maximum intensity was detected. There are two possible out-of-focus positions. In the first position the sample is closer to the lens than the optimum position, which we call the back focus (BF) and denote with negative values of distance from the in-focus position. The second is the position of the sample further away from the lens than the optimum position, which we call the front focus (FF) and denote with positive values of distance from the in-focus position. Figure 21. The effect of the out-of-focus position of the PE sample on its PL spectrum (intensity (I) as a function of wavelength (λ)). Smoothed and normalized average spectra of the detected PL light from the PE sample, where in a) the original spectra and in b) the spectra with the supposed Raman peak removed and then normalized are presented. The PL light spectrum was acquired for three different positions of the sample: black, red, and blue curves correspond to in-focus (0 mm), front focus (FF, 3 mm), and back focus (BF, - 3 mm) positions, respectively. The error of the measurement is indicated as the shaded area around each line. The effect of the out-of-focus position on the intensity of the detected PL light was discussed in the Subsection Due to the use of achromatic lenses, a difference in the shape of the PL spectrum was not expected as a result of the out-of-focus positon of the sample. However, there is a noticeable change in the PL spectrum due to the out-of-focus 44

53 position of the sample (Figure 21). It seems that the reason is not the achromatic aberration of the lens, but the change in the relative intensity of the supposed Raman peak with respect to the PL part of the spectrum. This was confirmed by simply cutting the supposed Raman peak away and renormalizing the spectrum. This further confirms that the additional peak at approximately 460 nm is not of the PL origin and therefore supports the idea of being the Raman scattered light. Microplastic particles were expected to be of different sizes and shapes, therefore it was important to check whether the irregular shapes can affect the PL spectrum. Furthermore, it was reported, as discussed in Section 2.4.3, that irregular shapes of microplastic particles pose a potential problem for their detection. A smaller piece was torn away from the volume sample of PE in order to check the effect of irregular shapes. A similar effect, as in the case of photobleaching, on the shape of the PL spectrum was observed (Figure 22). Figure 22. Smoothed and normalized average spectra (intensity (I) as a function of wavelength (λ)) of the detected PL light from PE of irregular shape, where in a) the original spectra and in b) the spectra with the supposed Raman peak removed and then normalized are presented. Spectrum of the PL light originating from pointy edges of the sample and from the flat parts of the sample is represented by the black and red curves, respectively. The error of the measurement is indicated as the shaded area around each line. The particle of PE was smaller than 1 cm and of irregular shape with pointy edges and flat parts as well. A difference in the PL spectrum was noticed, when the PL spectrum was acquired from the pointy edges with respect to the flat part of the sample (Figure 22), which showed a similar PL spectrum to the one corresponding to the volume sample of PE. Again, the removal of the supposed Raman peak resulted in a better match between the two spectra, though not as good as in the case of photobleaching and out-of-focus position. The reason for this is probably that simply cutting away the supposed Raman peak is not the most appropriate method to remove the effect of Raman scattering on the shape of the spectrum. 45

54 The presence of the supposed Raman peaks in the acquired spectra seems to be the reason for the change in the shape of the spectrum by photobleaching, the out-of-focus position, and irregular shapes of the sample. The effects can be canceled out by simply cutting the supposed Raman peak. A rather simple approach seems to work well. However, the reason for the change in the shape could also be the result of superposition of several effects. For instance, small changes in the focal length with respect to the wavelength of the lenses used could cause some changes to the spectrum when the distance from the lens is changed (out-of-focus position). However, it seems that the changes observed in the spectrum are mainly due to the different origin of the narrow peak at 460 nm, which corresponds to Raman scattering. The observed changes in the PL spectrum can affect the material differentiation based on the spectral shape. Since samples were not always excited for the same time before capturing the spectra and they might have been positioned a bit out of focus, the possible spectral variations appear in all the acquired spectra. In the following section an analysis for sample differentiation based on PL spectrum is presented. The spectra corresponding to the irregular shaped particle and original spectra (no Raman peak removal) were used in this analysis, therefore the spectral variations were included in the differentiation analysis. 4.4 Sample differentiation The main idea of this work is to check if the plastic can be differentiated from non-plastic materials by using the constructed setup for the PL spectroscopy and if it could be used for microplastic detection. As discussed in the previous section, the PL spectra acquired from plastic materials differ from the spectra corresponding to samples of non-plastic samples (Figures 15 and 16). In this section, dyed materials are not considered, because a further study of the effect of dyes on the PL is needed. Spectral variations discussed in previous section are considered, since they cannot be avoided when acquiring the PL spectra. In the first subsection the peak wavelength analysis is presented and followed by the pattern recognition with neural networks in the second subsection Peak wavelength The wavelength at which the maximum value of the PL light intensity was detected (peak wavelength) differs for different materials. Therefore, a simple analysis was done by determining the wavelength of the maximum intensity for all samples shown in Figure 15. The peak wavelength was determined for every acquired spectrum separately. In Table 2 the maximum, minimum and average value of the peak wavelengths are listed. The result of the peak wavelength analysis alludes that the plastic samples could be differentiated from samples of non-plastic origins from the PL spectra. The peak wavelengths corresponding to the plastic samples range from to nm and the peak wavelengths corresponding to the samples of non-plastic origin range from 46

55 462.9 to nm. A rule of thumb could be that the spectra with peak intensity at wavelength shorter than correspond to plastic samples. Nevertheless, the two peak wavelength ranges are not clearly separated and they intersect for the PC and Sepia sample, which means that this simple approach does not suffice completely for the differentiation of plastic and non-plastic materials. However, the spectra also differ in the peak shape and size. Already spectra corresponding to Sepia and PC, for which the peak wavelengths seem to coincide, clearly differ one from another (Figure 15). Additionally, the values of peak wavelengths for different plastic types coincide and the method could not be used for plastic type classification. Therefore, neural networks (NN) were also tested for differentiation of the samples, which is discussed in the next subsection. Table 2. The minimum, maximum and average value of the wavelength, at which the maximum intensity of the PL light was detected (peak wavelength), and the number of spectra used in the analysis for 18 different samples. The first 9 samples listed in the upper part of the table are plastic. Sample Minimum peak Average peak Maximum peak Number of wavelength [nm] wavelength [nm] wavelength [nm] spectra PET PP PE PC nylon nylon PMMA PS PVDF sand wood Echinocardium Posidonia Litophyllum Monodonta Mytilus Neverita Sepia Neural networks NN consist of simple elements, which work in parallel, and are called neurons. The neurons form layers and are connected to neurons in other layers. The connections are called weights. Neurons can have an additional parameter called bias, which is added to the output signal of the neuron. By setting the values to weights and biases, NN can be used for different tasks. One of them is pattern recognition. Therefore, the idea is to use 47

56 the NN to recognize patterns in the PL spectra and according to the recognized pattern determine to which material they correspond. The concept of NN is to mimic how human brain works, in which a lot of neurons with many more inter-neuron connections process the input signal and recognize the previously observed and learned situation. As we, humans, make mistakes, so can NN when it comes to the recognition, because the input signal is usually different from what has been observed before. In general, a large enough NN (high enough number of neurons) can be trained to perfectly recognize patterns from the signals used for training (teaching). However, this can lead to misidentification by independent input signals, which were not used for training, because the NN recognized patterns in the process of training, which are not general to the classification problem. Therefore a size of the NN has to be chosen with care. Matlab s Neural Network toolbox for pattern recognition was used with a default twolayer neural network construction. In the output layer there are as many neurons as there are classes in which the input data can be classified (in our case the number of different materials). The number of neurons in the first layer (hidden layer), to which input data (spectra) is fed has to be set. The transfer function used in the hidden layer is a sigmoid function which returns values between -1 and 1. At first, the weights and biases of the neurons are randomly set and then optimized in the process of training the NN. A training set of data is fed to the neural network providing the input data (in our case the PL spectra) and the correct output data (in our case corresponding material). The NN was trained using the scaled conjugate gradient training function. The training function changes the weights and biases of the neutrons in order to minimize the error between the NN output and the correct value. Therefore this kind of NN is called a supervised network. The error is determined using a performance function (e.g., mean absolute difference between the right value and the NN output value). A so called cross-entropy function was used as the performance function, which heavily penalizes outputs that are extremely inaccurate. After the NN is trained on a set of data, an independent set of data is needed to test the performance or accuracy of the trained NN. Therefore, a set of independent data is used to feed the NN and the output is compared to the correct, known value. In our case the material can be either correctly recognized or incorrectly recognized. Therefore, the accuracy can be expressed in percentage. The initially chosen values of weights and biases affect the accuracy of the training, therefore many repetitions with different random initial values of weights and biases were performed. An additional parameter which determines the accuracy of the NN is its hidden layer size. The hidden layer sizes and initial weights and biases were varied and the accuracy was calculated until it could not be improved anymore. The set of spectra listed in the Table 2 except the ones corresponding to Neverita were used for training the NN. The spectra corresponding to Neverita were not used for training 48

57 the NN but for testing the accuracy, because the spectra are almost identical to the spectra corresponding to Echinocardium. Therefore, it was expected that the spectra corresponding to the Neverita samples would be recognized as spectra corresponding to Echinocardium, which were used for training. In total 166 spectra corresponding to 17 different samples, where each sample corresponds to a different material, were used to train the NN. The accuracy of the trained NN was calculated on an independent set of spectra. For this reason, additional 144 spectra were acquired from samples of 9 different, known materials. Adding 5 spectra corresponding to the Neverita sample makes in total 149 spectra, which are all listed in Table 3. The sand samples used for the calculation of the accuracy were collected at 5 different beaches and shipped to us. They were of different colors and could also contain different materials (e.g. microplastics). Table 3. A list of known samples used for the calculation of the accuracy of the trained NN. Material Number of samples Number of spectra nylon6 1 5 nylon PE 5 40 PET 2 10 PMMA 2 10 PP 2 10 PVDF 5 5 Neverita 1 5 Sand 5 54 wood 1 5 Two different types of neural networks were constructed. The first (NN1) was designed in a way that it only determines if the spectrum corresponds to a sample of plastic or nonplastic origin and the second (NN2) was designed to differentiate among all 17 samples, for which the corresponding spectra were used for training the NN. In the NN1 only one neuron in the hidden layer sufficed to reach 99.3 % accuracy for differentiation between the plastic and non-plastic samples. This confirms that it is relatively straightforward to differentiate among plastic and non-plastic samples based on their PL spectra, as already suggested by the peak wavelength analysis. For the NN2 a hidden layer with 23 neurons produced the accuracy of 63.1 % for the classification of the independent spectra into the 17 classes. Additionally, the accuracy for the differentiation between the plastic and nonplastic samples was calculated for the NN2 as well, and resulted in the same accuracy as NN1, i.e %. More detailed results of the NN differentiation are presented in Table 4. The two NN were trained on the spectra corresponding to 17 different sample types and then tested on spectra corresponding to 25 independent samples of 10 known materials. The sand samples were also assumed to consist only of sand. However, they could have 49

58 contained other particles as well. The acquired spectra were first visually compared to the known sand spectra and then, if they did not look completely different, they were assumed to be sand in the process of accuracy calculation. The more detailed results (Table 4) show that 20 out of 54 spectra corresponding to sand samples were identified as wood (3), Echinocardium (1), Litophyllum (15) and Posidonia (1). This suggests that the sand samples collected at different beaches also contained non-sand particles, which is expected. However, it makes the accuracy of the results questionable as well, since it is not certain that this spectra really correspond to sand or other particles commonly found in the marine environment. Table 4. The detailed accuracy results of the neural network recognition for the NN1 and NN2. For NN1 the rows correspond to the 2 possible classes, in which the independent spectra could be classified. For NN2 the rows correspond to the 17 possible classes, in which the independent spectra could be classified. The actual (correct) classes of the independent spectra are presented in columns. The green colored cells correspond to the correct classification, yellow to incorrect classification, but with correct plastic or nonplastic classification. The red cells correspond to completely wrong classification. The last row corresponds to the total amount of independent spectra per known class. NN Plastic Non-plastic NN nylon nylon PC PE PET PMMA PP PS PVDF Echinocardium Litophyllum Monodonta Mytilus Posidonia Sand Sepia wood Total A larger set of known samples of different materials and more acquired spectra should be used to train the NN, and known, well defined samples to test the accuracy. Also a more balanced set of independent samples should be chosen, each class consisting of 50

59 approximately the same number of spectra, in order not to be biased when calculating accuracy. A better performance could be expected this way. Finally, it has to be pointed out that the dyed materials were not considered in this sample differentiation analysis. More research is needed to confirm the suitability of the PL spectroscopy and NN pattern recognition of the acquired PL spectra for the detection and differentiation of microplastics. Nevertheless, the first results are promising and offer a 63.1 % accuracy for differentiation between 17 different materials and 99.3 % for plastic and-non-plastic origin-classification of materials. It therefore has a high potential for microplastic detection and its classification. 51

60 5 CONCLUSION AND OUTLOOK The presence of microplastics in the environment is a relatively new concern compared to the bigger plastic litter pieces floating in the oceans. Being smaller than 5 mm makes it harder to notice and detect, but that does not mean that their presence is not dangerous. It has already been shown that it can pose a threat to different living beings as well as humans. However, the exact effects of microplastic contamination have not been determined yet. Further research is needed, which should also include better monitoring of the microplastic abundance. The investigations done so far have shown that it is necessary to implement spectroscopic methods into the future standard operation protocols for microplastic detection in order to get reliable results on its abundance and the type of plastic. The marine environment was the focus of the majority of investigations. Samples were collected from the water surface, water column and sediment. Especially with sediment samples a reduction of the sample volume is needed, which is mainly done with the density separation. The volume reduced samples are then examined for potential microplastics. The most advanced approaches for its detection used so far included the Raman and FTIR spectroscopy, which are available as the end products from different producers. The FTIR spectroscopy has already been used for automated raster scanning and imaging of preprocessed microplastic samples. Despite the encouraging results it still suffers from some drawbacks (e.g. refractive errors, total absorbance and long measurement times). In order to make the monitoring of microplastic abundance more reliable, we investigated new methods which could be used for microplastic detection. The photoluminescence (PL) spectroscopy was considered as a potential method for microplastic detection. A cost efficient setup was built in order to acquire the PL spectra of different plastic and non-plastic materials. Despite mainly using thin volume samples, the microplastic criterion (particles smaller than 5 mm) was met, considering that the calculated beam size of the excitation light on the surface of the sample positioned in the focal plane was approximately 10 µm. The characterization of the setup showed that at least one quarter of the maximum PL intensity can still be detected if the sample position is varied in a range of 6 mm. This indicates that this kind of setup could be used for raster scanning microplastic samples without refocusing. Considering that the light with relatively short excitation wavelength compared to the IR spectroscopy was used, a better lateral resolution (approximately 1 µm) than in case of IR spectroscopy (approximately 10 µm) can be achieved. Therefore, particles ranging in size from approximately 1 µm and up to 5 mm could be detected using this kind of setup. In order to enable quick examination of microplastic samples, the method for detection of microplastic has to be fast. The problem that we faced was that different materials showed PL of different intensity and plastics in particular showed the PL of low intensity. Therefore long integration times, up to 800 ms, and high excitation powers were needed. 52

61 The FPA based FTIR imaging resulted in an imaging speed equivalent to the raster scanning of a sample with 20 ms to 140 ms per point by the same lateral resolution. To solve the problem of long integration times when using the PL spectroscopy a more sensitive detector with larger dynamic range and an improvement of the setup could suffice. Another solution to this problem would be the use of a light source with shorter wavelengths for which a better absorption and a more intense PL emission is expected. Using the setup, the PL spectra were acquired from 27 different materials (Table 1). The spectra corresponding to plastic samples differ from the spectra corresponding to materials of non-plastic origin (Figures 15 and 16). Additionally, for some plastic samples distinct narrow peaks were observed, most commonly at 460 nm. These peaks were identified as Raman scattered light. Raman peaks can have an effect on the shape of the acquired spectrum, however they could also be used for the plastic identification since they were only present for the plastic samples. On the other hand, this clearly shows that the emission of the PL light is a drawback for the Raman spectroscopy. The spectra corresponding to plastic samples show the peak intensity values at lower wavelengths than samples of non-plastic origin (Table 2). This encouraged us to use neural network for the classification of unknown samples according to the exhibited PL spectra. Two types of neural networks were constructed: one to determine whether a spectrum corresponds to a plastic or a non-plastic sample (NN1) and the other (NN2) to also classify the material. They were trained on a set of 166 spectra and the accuracy was calculated on the recognition rate of 149 independent spectra which were not used for training. The accuracy of the NN1 was 99.3 % and 63.1 % for the NN2. The first results are promising, however it needs to be pointed out that the neural networks should be trained and tested on a bigger set of data, which would make the use of NN more reliable. While differentiating and classifying samples according to the acquired PL spectra, only the spectra corresponding to the samples, which were not dyed, were used. It was shown that dyes can change the PL spectrum drastically (Figure 18). The effect of dyes that are used in plastics on the PL spectrum should therefore be studied in a greater detail. Furthermore, an incrustation of plastic samples can affect the PL spectra (Figure 17), as well. However, both the Raman and FTIR spectroscopy suffer from the same problem. In order to solve it, a removal of organic contents using chemicals is suggested. From the work presented in this Master s Thesis, we conclude that PL spectroscopy has great potential for microplastic detection, however, being the first, according to our knowledge, to use PL spectroscopy for microplastic detection, we think there are many possible improvements to the method and much more investigation needs to be done before the final application. However, it has been shown that clear PL spectra can be acquired with low excitation powers (0.25 mw) and short integration times (2.4 ms), which indicates that PL spectroscopy could outperform FTIR spectroscopy, by means of measurement speed. 53

62 We also considered additional methods for the detection of microplastics. We tested micro-ct imaging for the microplastic detection in sediment samples. A sample prepared out of pure sand and two different plastic granules of a size of approximately 5 mm were imaged (Appendix A). The first results are encouraging and the plastic granules were clearly distinguishable from sand and one from another. According to the results presented in this thesis, further investigation of the PL spectroscopy and improvements to the microplastic detection method are planned, as it seems that the excitation laser light with shorter wavelengths ( nm) would be more appropriate. However, a set of most relevant materials for the microplastic detection should be characterized before introducing a new light source. Ideally, the new light source would result in a PL emission of approximately the same intensity for different materials and spectrum of the emitted light would clearly differ among samples. Furthermore, the non-plastic materials and the dyes used in plastic production would ideally not be excited. In this way the PL emission from the plastic samples containing different dyes could also be characterized. Using the excitation light of lower intensities, which would excite plastic samples to emit a more intense PL light, would also result in the Raman scattered light being too weak to be detected. The Raman scattering happens relatively rarely compared to the elastic scattering and therefore high excitation light intensities are needed in order to observe Raman scattering. In this way spectral variations would be avoided. Technical improvements of the setup (e.g. introduction of a more sensitive detector) can improve the performance as well. Furthermore, a large set of plastic materials from different producers as well as a lot of samples of non-plastic origin should be examined after determining the optimal excitation light wavelength and implementing a better detector. This way a dedicated device for fast raster scanning of samples with an automated detection and classification of microplastics using neural networks could be constructed. 54

63 6 RAZŠIRJEN POVZETEK V SLOVENSKEM JEZIKU Plastika je poceni in vsestransko uporaben material. Proizvodnja plastike se je vse od leta 1950 povečevala in v letu 2014 znašala 311 milijonov ton [1]. Veliko produktov za enkratno uporabo je narejenih iz plastike ravno zaradi nizke cene. Ti izdelki po uporabi pristanejo v smeteh. Mnogo odpadkov se znajde tudi v vodotokih, ki odpadke ponesejo v morja in oceane. Ocene kažejo, da je v letu 2010 v oceanih pristalo med 4,8 in 12,7 milijoni ton plastike [2]. V osemdesetih letih prejšnjega stoletja so pokazali, da je 80 % vseh odpadkov v oceanih plastike in da količina odpadkov raste eksponentno [3]. Ko se plastika znajde v okolju, tam tudi ostane, saj je biološko nerazgradljiva. Zaradi vplivov okolja večji kosi plastike sicer erodirajo in s časom postanejo vse manjši in manjši, kar pa ne pomeni, da za okolje niso več nevarni. Delci plastike, manjši od 5 mm, se imenujejo mikroplastika. Mikroplastika predstavlja okoljevarstveni problem, ki je predvsem obravnavan v povezavi z vplivi na morsko okolje. Zaradi majhnosti delcev mikroplastike, le-ti ne skazijo izgleda krajine toliko kot večji kosi odpadkov, a to ne pomeni, da niso nevarni za okolje. Zaradi različne gostote različnih tipov plastike, ki se lahko tudi spremi zaradi npr. adsorpcije ali preraščanja z različnimi organizmi, lahko mikroplastika plava na površju morja ali nekje v vodnem stolpcu ali pa postane del sedimenta [7]. Tako je mikroplastika na voljo za različne tipe morskih organizmov, ki jo zaradi majhnosti lahko pomotoma zaužijejo. Večino mikroplastike živa bitja s časom tudi izločijo, a raziskave so pokazale, da lahko zaužitje mikroplastike zmanjša prehranjevalno aktivnost [26], povzroči vnetja in da zelo majhni delci (t. i. nanoplastika) lahko vstopijo direktno v celice [22, 23]. Dodatno skrb vzbujajo tudi aditivi, ki se dodajajo plastiki v procesu proizvodnje, saj se lahko sprostijo v okolje ali ob zaužitju mikroplastike tudi v organizem. Plastika dobro adsorbira persistentne organske polutante (POPi), katerih koncentracija na plastiki je lahko za red velikosti večja kot v sedimentu [16 19]. Ob zaužitju mikroplastike se lahko POPi sprostijo in preidejo v tkivo [24, 25, 30]. Po drugi strani pa lahko morski tokovi prenašajo plavajočo mikroplastiko v oddaljene biotope in z njo tudi adsorbirane polutante [16, 17, 20] in organizme, ki gnezdijo na delcih mikroplastike [21]. Tudi ljudje smo izpostavljeni mikroplastiki, ki vstopa tudi v prehranjevalno verigo in se lahko znajde celo v hrani [28]. Raziskave kažejo, da bi mikroplastika lahko bila potencialno nevarna tudi za ljudi, a je potrebno še veliko dodatnih raziskav, da se prepričamo, kako škodljiva je lahko. Da bi lahko ocenili, kako nevarna je mikroplastika za okolje in organizme, ki v njem živijo, je pomembno vedeti tudi, koliko mikroplastike v okolju sploh je [27]. Zaradi naraščajoče uporabe plastike in postopnega razpadanja večjih kosov plastike lahko pričakujemo, da se bo količina mikroplastike v okolju povečevala [4, 11]. Obstajajo tudi načini, da mikroplastika direktno zaide v okolje, kot na primer ob nenamernih izgubah 55

64 tovora z ladij [12], ob pranju oblačil [13], ob uporabi čistilnih sredstev [14] in tudi higienskih pripomočkov [15], ki vsebujejo mikroplastiko. Dodatno skrb vzbuja tudi biološka nerazgradljivost plastike, kar pomeni, da se v okolju ne razgradi na manjše organske gradnike. Dobro vest pa prinaša najnovejša raziskava [6], ki opisuje bakterije, ki uporabljajo PVC (polivinil klorid) kot glavni vir energije in ga razgradijo na benigne monomere. Ta raziskava vzbuja upanje, da obstajajo, ali pa da se bodo s časom razvili organizmi, ki učinkovito razgradijo plastiko na nenevarne snovi. Kljub vzpodbudnim novicam pa bo še vedno potrebno spremljati količine mikroplastike, za kar pa bo potrebno razviti tudi ustrezne nadzorne programe. Določanje količine mikroplastike je do zdaj potekalo na različne, neustaljene načine [7]. Večina raziskav se je nanašala na morsko okolje, in sicer na morsko gladino, morski stolpec in sediment. Načine zbiranja vzorcev lahko razdelimo na tri tipe. Prvi je selektivno vzorčenje, pri katerem se pobere delce direktno iz okolja, za katere se zdi, da bi lahko bili plastičnega izvora. Drugi je masovno vzorčenje, pri katerem se zajame del sedimenta ali vodnega stolpca ali vodne gladine. Tretji pa je zajetje dela sedimenta, vodnega stolpca ali vodne gladine, ki se ga prečisti s sitom ali mrežo, tako da se ohrani le želen, manjši del vzorca (prečiščen vzorce). Zbrani vzorci se nato navadno prepeljejo v laboratorij, kjer so nadalje obdelani in analizirani. Za selektivno zbrane vzorce je le še potrebno določiti, ali je posamezen delec plastičnega izvora ali ne. Za masovne in prečiščene vzorce sedimenta pa je iskanje delcev mikroplastike dolgotrajen postopek zaradi velike količine sedimenta, v katerem se lahko nahajajo tudi delci mikroplastike. Takšni vzorci se zato navadno obdelajo, tako da se odstrani večji del sedimenta. Najbolj pogosto uporabljena tehnika je ločevanje glede na gostoto [7]. Gostota plastike je med 0,8 in 1,4 kg/l, medtem ko je gostota sedimenta navadno višja, in sicer približno 2,7 kg/l. Izkazalo se je, da je dobra konstrukcija naprave za razločevanje na podlagi gostote ključna za uspešno razločevanje [40]. Na voljo je tudi komercialni aparat, imenovan MPSS, ki zanesljivo in uspešno loči mikroplastiko od sedimenta. Prečiščen vzorec je nato potrebno dodatno analizirati in določiti količino mikroplastike. Prečiščene vzorce so analizirali na različne načine. Najenostavnejši je z golim očesom in uporabo stereo mikroskopa ob upoštevanju vizualnih kriterijev. Za analizo vzorcev se uporabljajo tudi spektroskopske metode, da lahko z večjo gotovostjo potrdijo, ali gre za mikroplastiko ali ne. Najbolj pogosto uporabljeni metodi sta ramanska spektroskopija in infrardeča spektroskopija s Fourierevo transformacijo (FTIR). Uporaba spektroskopskih metod je ključnega pomena za pravilno prepoznavanje mikroplastike [10]. Za nadzor količine mikroplastike v okolju je potrebna analiza velikega števila vzorcev z različnih krajev. Zato je ključnega pomena, da so uporabljene metode zanesljive in da omogočajo hitro in čim bolj avtomatizirano obdelavo vzorcev. FTIR je že bil uporabljen za avtomatizirano rastrsko skeniranje in slikanje vzorcev mikroplastike. Rastrsko 56

65 skeniranje je bilo zelo dolgotrajno [49], medtem ko je slikanje vzorcev z uporabo niza detektorjev (focal point array) bilo precej hitrejše [51]. Vzorec kvadratne oblike s stranico dolžine 10,54 cm so uspešno analizirali v 10,75 ure pri lateralni ločljivosti 20 µm. Takšna hitrost slikanja je enakovredna rastrskemu skeniranju vzorca, kjer bi za analizo vsake točke potrebovali približno 140 ms, upoštevajoč enako lateralno ločljivostjo. V podobni raziskavi [50] so dosegli še višje hitrosti analiziranja vzorcev, in sicer hitrost ekvivalentno rastrskemu skeniranju, pri kateri bi potrebovali približno 20 ms za vsako točko, upoštevajoč enako lateralno resolucijo, ki je bila v tem primeru 25 µm. Kljub uspešni uporabi FTIR spektroskopije za avtomatizirano analizo vzorcev pa je potrebno izpostaviti tudi njene slabosti. Vzorec mora biti popolnoma suh, saj voda močno absorbira IR svetlobo, večji delci lahko tudi absorbirajo preveč IR svetlobe in jih zato ni mogoče zaznati. Delci nepravilnih oblik lahko popačijo zajet spekter, kar onemogoča uspešno prepoznavanje. Zato smo se odločili, da preizkusimo fotoluminiscenčno spektroskopijo kot metodo za hitro in učinkovito zaznavanje mikroplastike. Fotoluminiscenca (PL) je pojav, pri katerem material izseva svetlobo po predhodni absorpciji svetlobe. Proces fotoluminiscence lahko predstavimo z diagramom Jablonskega (slika 4), v katerem so prikazana možna diskretna energijska stanja, v katerih se lahko molekula nahaja. Za opis fotoluminiscence so pomembna singletna in tripletna stanja sklopljenih elektronov. Proces fotoluminiscence se začne z absorpcijo svetlobe. Molekula v osnovnem, nevzbujenem singletnem stanju absorbira vpadno svetlobo in preide v prvo, drugo ali višje vzbujeno singletno stanje. Za vsako singletno stanje je možnih več različnih vibracijskih energijskih stanj molekule. Nato molekula preide v najnižje vzbujeno singletno stanje preko procesov interne konverzije, pri katerih se energija sprosti v obliki toplote in ne z izsevanjem svetlobe. Temu lahko sledi neposreden prehod v osnovno singletno stanje, tako da izseva svetlobo, kar se imenuje fluorescenca. Fluorescenca poteka relativno hitro, in sicer v približno eni nanosekundi po absorpciji svetlobe. Izsevana svetloba je daljših valovnih dolžin zaradi izgube energije pri notranjih prehodih. Druga možnost za relaksacijo v osnovno stanje je najprej prehod iz prvega vzbujenega singletnega stanja v energijsko nižje tripletno stanje. Prehod iz tripletnega stanja v singletno stanje je prepovedan, zato je to stanje stabilno in traja dlje časa (več kot 1 ms), da se molekula relaksira in izseva svetlobo. Ta proces se imenuje fosforescenca. Svetloba izsevana v procesu fosforescence je še pri daljših valovnih dolžinah kot pri fluorescenci, saj se dodatna energija izgubi pri prehodu iz singletnega v tripletno stanje. Zaradi velike gostote možnih energijskih stanj je absorpcijski spekter zvezen, kar pomeni, da obstaja pas valovnih dolžin svetlobe, ki jo material absorbira. Če material obsevamo s svetlobo z valovno dolžino iz absorpcijskega pasu, stečejo procesi absorpcije, interne konverzije in izsevanja. Z izbiro svetlobe valovnih dolžin, ki se bolje absorbira, je verjetnost, da posamezno molekulo vzbudimo, večja, kar se kaže tudi v povečani intenziteti izsevane PL svetlobe. V kolikor vzbujamo material s svetlobo večje intenzitete, vzbudimo večje število molekul, kar se kaže tudi v povišani intenziteti PL svetlobe. Lahko pa se tudi zgodi, da se molekula v celotnem procesu poškoduje tako, da ne more več 57

66 absorbirati in izsevati PL svetlobe, kar se pozna kot padanje intenzitete s časom izpostavljenosti materiala svetlobi. Zato je včasih PL opisana kot resonančni pojav. Podobno kot absorpcijski spekter je tudi spekter PL zvezen zaradi velike gostote energijskih stanj molekule. Za določitev uporabnosti PL za zaznavanje mikroplastike smo postavili merilni sistem, ki je prikazan na sliki S1. Podobna postavitev je že bila uporabljena za analizo spektra fotoluminiscence plastike (polietilena) [58]. Prednost takšne postavitve je v tem, da sta vzorec in detektor na isti premici, kar naredi poravnavo postavitve enostavnejšo in tudi omogoča poznejšo nadgradnjo za rastrsko skeniranje, enostavno s premikanjem vzorca. Slika S1. Merilni sistem za fotoluminiscenčno spektroskopijo (pogled od zgoraj). Modre puščice predstavljajo razširjanje laserske svetlobe, rdeče pa svetlobe fotoluminiscence. Za vzbujanje vzorcev smo se odločili uporabiti cenovno ugoden diodni laser, ki izseva svetlobo z valovno dolžino 405 nm. Z uporabo dveh srebrnih zrcal usmerimo kolimirano lasersko svetlobo v željeno smer in na določeno višino. Lasersko svetlobo smo usmerili tako, da vpade na dikroično dolgoprepustno zrcalo pod kotom 45. Izbrano dikroično zrcalo odbije svetlobo z valovnimi dolžinami, krajšimi od 425 nm in prepusti svetlobo z 58

67 daljšimi valovnimi dolžinami. Odbito svetlobo nato z lečo fokusiramo v majhno piko na vzorcu. Ta svetloba vzbudi osvetljeni del vzorca, ki nato izseva PL svetlobo. Ista leča, ki je fokusirala lasersko svetlobo, nato kolimira PL svetlobo, ki se nato razširja skozi dikroično dolgoprepustno zrcalo. Ker je PL svetloba daljših valovnih dolžin kot laserska svetloba, s katero smo vzbujali vzorec, jo dikroično zrcalo prepusti. Nato PL svetloba vpade na lečo, ki svetlobo fokusira in sklopi v optično vlakno, ki vodi PL svetlobo do spektrometra. Izkazalo se je, da vzorci odbijejo toliko laserske svetlobe, katere del dikroično zrcalo tudi prepusti, da jo zaznamo z detektorjem. Pri vzorcih, ki izsevajo šibko PL svetlobo, je to onemogočalo njeno zaznavanje zaradi omejenega dinamičnega obsega detektorja. Zato smo morali vgraditi dodatni barvni dolgoprepustni filter, ki prepusti svetlobo valovnih dolžin, daljših od 420 nm. Pri testiranju postavitve smo ugotovili, da je potrebno vgraditi še en dodatni filter (laserski filter), ki prepusti zgolj svetlobo valovnih dolžin (405 ± 5) nm in tako bolje definira spekter laserske svetlobe. Z uporabo opisane postavitve smo analizirali več različnih vzorcev. Za vsak vzorec smo zajeli več (od 8 do 10) spektrov PL svetlobe z različnih delov vzorca. Tako smo se želeli izogniti morebitnim lokalnim posebnostim v vzorcu in preveriti, koliko se lahko spekter PL svetlobe spreminja od mesta do mesta na vzorcu. Nato smo zajete spektre računalniško obdelali s programskim paketom Matlab. Najprej smo jih zgladili z uporabo integrirane funkcije Savitzky-Golay, ki sprejme dva argumenta: širino okna in red polinoma. Izkazalo se je, da okno s širino 10 nm in polinom drugega reda najbolje zgladita zajete spektre in hkrati ohranita vse pomembne dele spektra (slika 14). Nato smo spektre normirali na vrednosti med 0 in 1, tako da so bili lažje primerljivi. Nazadnje smo spektre, ki pripadajo istemu vzorcu, povprečili in izračunali standardno deviacijo. Po opisanem postopku je bilo analiziranih 27 različnih materialov, ki so podani v tabeli 1. Večina vzorcev ima obliko kvadra. Vzorci so bili tanjši od 5 mm, le vzorec polietilena je bil debelejši (9,96 mm), kar pomeni, da ustrezajo velikostnemu kriteriju mikroplastike vsaj v eni dimenziji. Premer grla laserskega snopa, s katerim vzbujamo vzorec, je bil približno 10 µm. Na ta način so bili vzbujeni deli vzorcev, manjši od 5 mm in tako teoretično ustrezajo kriteriju za mikroplastiko, kar pomeni, da so meritve kljub uporabi večjih vzorcev reprezentativne za mikroplastiko. Ob preverjanju postavitve smo ugotovili, da zaznamo eno četrtino maksimalne intenzitete izsevane PL svetlobe, če spreminjamo oddaljenost vzorca od leče v obsegu 6 mm. To pomeni, da bi s takšnim tipom postavitve lahko rastrsko analizirali vzorec z mikroplastiko, ne da bi bilo potrebno spreminjati razdaljo med vzorcem in lečo, saj je mikroplastika po definiciji manjša od 5 mm. Uporaba svetlobe s kratkimi valovnimi dolžinami (405 nm) za vzbujanje vzorcev pri fotoluminiscenčni spektroskopiji v primerjavi z valovnimi dolžinami svetlobe, ki se uporablja pri FTIR spektroskopiji (2,5-10 µm), omogoča, da je lahko dosežena tudi boljša lateralna ločljivost v primerjavi 59

68 s FTIR spektroskopijo. Z uporabo fotoluminiscenčne spektroskopije je lateralna ločljivost približno 1 µm, medtem ko je pri FTIR spektroskopiji približno 10 µm. To tudi pomeni, da bi s takšno postavitvijo za fotoluminiscenčno spektroskopijo lahko zaznali mikroplastiko, velikosti od 1 µm do 5 mm. Pomemben podatek za primernost fotoluminiscenčne spektroskopije za zaznavanje mikroplastike predstavljajo parametri zajemanja spektrov, ki so podani v tabeli 1. Za dosego hitrih analiz vzorcev so potrebni čim krajši integracijski časi (ti), a plastični vzorci so izsevali relativno šibko PL svetlobo v primerjavi z nekaterimi drugimi materiali. Za njeno detekcijo so tako bili navadno potrebni relativno dolgi integracijski časi (daljši od 500 ms) in visoki svetlobni tokovi (100 mw). Da bi dosegli primerljivo hitrost analiziranja vzorcev kot pri slikanju s FTIR spektrometrom, bi potrebovali integracijske čase, ki so krajši od 100 ms. Takšne čase bi lahko dosegli z nadgradnjo postavitve, na primer z uporabo bolj občutljivega detektorja, ki bi imel tudi večji dinamični obseg. Z večjim dinamičnim obsegom bi zagotovili zmožnost analiziranja vzorcev, ki izsevajo PL svetlobo z različno intenziteto, ne da bi spreminjali svetlobni tok svetlobe, s katero vzbujamo vzorec. Še boljša rešitev bi lahko bila uporaba svetlobe s krajšo valovno dolžino za vzbujanje vzorcev, saj je pričakovano, da bi takšna svetloba bolj uspešno vzbudila predvsem plastične vzorce, kar pomeni PL svetlobo z večjo intenziteto in posledično krajše integracijske čase. Glavni cilj te naloge je bil preveriti, ali lahko na podlagi spektroskopije PL razločimo plastične materiali od neplastičnih materialov. Na sliki S2 je prikazanih 18 spektrov, ki pripadajo 18 različnim materialom, od katerih jih je 9 plastičnih in 9 neplastičnih. Pri krajših valovnih dolžinah na obliko spektra odločilno vpliva prepustnost obeh dolgoprepustnih filtrov, ki ne prepustita svetlobe valovnih dolžin, krajših od 420 oz. 425 nm. V nekaterih spektrih, ki pripadajo plastičnim materialom, je moč opaziti razločne ostre vrhove, ki smo jih identificirali kot ramanske vrhe, ki najverjetneje pripadajo C-H in C-C vibracijam. Ramanski vrhi vplivajo na obliko spektrov (slike 20 do 22), a so lahko tudi uporabni za identifikacijo plastičnih materialov, saj smo jih opazili samo v spektrih PL svetlobe plastičnih vzorcev. Po drugi strani pa to tudi pomeni, da lahko izsevanje PL svetlobe predstavlja problem za uporabo ramanske spektroskopije za zaznavanje mikroplastike. Ker je težko primerjati 18 spektrov v enem grafu, smo ločeno primerjali najbolj podobne spektre in pa PL spektre plastičnih materialov s PL spektri peska in lesa (slika 16). Spektri, ki pripadajo plastiki, se izrazito ločijo od spektrov, ki pripadajo pesku in lesu, medtem ko so bolj podobni spektrom, ki pripadajo nekaterim drugim neplastičnim vzorcem. Kljub večji podobnosti se hitro opazi, da imajo spektri, ki pripadajo plastičnim vzorcem, vrh pri krajših valovnih dolžinah kot spektri, ki pripadajo neplastičnim materialom. Zato smo se odločili narediti enostavno analizo, pri kateri za vsak spekter določimo valovno dolžino vrha (tabela 2). Spektri pa se ne razlikujejo le po valovni 60

69 dolžini vrha, ampak tudi po širini in obliki, kar je porodilo idejo, da poskusimo kategorizirati vzorce z uporabo nevronskih mrež za prepoznavanje vzorcev. Slika S2. Zglajeni in normalizirani povprečni spektri (intenziteta (I) v odvisnosti od valovne dolžine (λ)) PL svetlobe pripadajoče a) različnim plastičnim vzorcem in b) različnim neplastičnim vzorcem. Napaka meritve je prikazana kot senčena površina okoli posamezne krivulje. Imena vzorcev so napisana v legendah. S programskim orodjem Matlab smo pripravili dva različna tipa nevronskih mrež. Naloga prvega (NN1) je bila določiti, ali spekter pripada plastičnemu materialu ali neplastičnemu, naloga drugega (NN2) pa je bila še klasifikacija materiala. Za učenje nevronskih mrež smo uporabili 166 spektrov 17 različnih materialov. Njihovo natančnost pa smo preverili in izračunali na 149 neodvisnih spektrih (niso bili uporabljeni za učenje) 10 različnih materialov. Klasifikacija je bila uspešna, če je bil prepoznan pravilen material, drugače pa neuspešna. Natančnost smo zato izračunali v odstotkih. Za NN1 je natančnost znašala 99,3 %, za NN2 pa 63,1 %. Rezultati so obetajoči, a je potrebno izpostaviti, da je za zanesljivo prepoznavanje vzorcev z nevronskimi mrežami potrebnih čim več vhodnih podatkov, v našem primeru spektrov, za uspešno učenje. Pomembno je tudi izpostaviti, da v analizi z nevronskimi mrežami niso bili uporabljeni spektri plastike, ki vsebuje barvila. Barvila lahko namreč bistveno vplivajo na spekter fotoluminiscence (slika 18) in s tem otežijo prepoznavo tipa plastike. Podobno težavo predstavljajo tudi organske snovi na vzorcih, kar smo opazili pri analizi plastenke, preraščene z rdečimi algami (slika 17). Prisotnost organskih snovi pa ne predstavlja 61

70 problema samo za fotoluminiscenčno spektroskopijo, ampak tudi za ramansko in FTIR spektroskopijo. Kot rešitev se navadno uporabijo kemikalije, ki razgradijo biološke materiale. Z opravljenim delom v sklopu te magistrske naloge smo ugotovili, da je fotoluminiscenčna spektroskopija metoda z velikim potencialom za hitro in natančno analiziranje vzorcev mikroplastike. Glede na naše vedenje smo prvi, ki smo uporabili to metodo za zaznavanje mikroplastike, zato je potrebno izpostaviti, da je možnih še veliko izboljšav in potrebnega veliko dela, preden bo na voljo končen produkt. Kljub temu pa lahko trdimo, da je lahko fotoluminiscenčna spektroskopija hitra metoda, v kolikor je vzorec efektivno vzbujen, saj zadostujejo zelo kratki integracijski časi (2,5 ms) in nizka gostota svetlobnega toka (0,25 mw) za zajem spektra. To tudi pomeni, da je lahko ta metoda tudi hitrejša od FTIR spektroskopije, saj so bili časi analize vzorcev enakovredni rastrskemu skeniranju, pri katerem je bilo potrebnih približno 20 oz. 140 ms za vsako točko. Poleg fotoluminiscenčne spektroskopije menimo, da je za zaznavanje mikroplastike primerno tudi CT slikanje. Pripravili smo testni vzorec, tako da smo v čisti pesek dodali granule dveh različnih tipov plastike, velikosti približno 5 mm. CT slikanje testnega vzorca (priloga A) je pokazalo, da lahko s CT slikanjem razločimo plastiko od peska in celo posamezna tipa plastike med sabo. Prvi rezultati CT slikanja potrjujejo naša predvidevanja, da bi CT slikanje lahko bila primerna metoda za zaznavanje mikroplastike v vzorcih sedimenta. Glede na vzpodbudne rezultate fotoluminiscenčne spektroskopije je načrtovano tudi nadaljnje delo s konkretnimi izboljšavami. Najprej je predvidena uporaba drugega svetlobnega vira za vzbujanje vzorcev. Glede na fotoluminiscenco polietilena se zdi, da bi svetloba s krajšimi valovnimi dolžinami ( nm) lahko bolj učinkovito vzbujala plastične vzorce [59]. Še pred tem bi bilo potrebno natančneje določiti primerno valovno dolžino, za kar bi bilo potrebno analizirati set najpogostejših plastičnih materialov. V najboljšem primeru bi novi svetlobni vir vzbudil različne materiale približno enako dobro in spektri izsevane PL svetlobe bi se bistveno razlikovali za različne materiale. Še bolje bi bilo, če nov svetlobni vir sploh ne bi vzbudil neplastičnih materialov in pa barvil, ki se pogosto uporabljajo za barvanje plastike. Tako bi lahko enostavno zaznali in določili tip plastike, tudi za plastiko, ki vsebuje barvila. Bolj učinkovito vzbujanje vzorcev bi tudi pomenilo, da bi bila potrebna nižja intenziteta svetlobe, s katero vzbujamo vzorce. Zato ne bi več zaznali ramansko sipane svetlobe, saj je ramansko sipanje relativno redek pojav v primerjavi z elastičnim sipanjem. Na ta način tudi ramanski vrhi ne bi več vplivali na obliko spektra zaznane svetlobe. Tudi nadgradnja postavitve, na primer z uporabo bolj občutljivega detektorja, bi lahko izboljšala primernost metode. Po optimizaciji postavitve bi bilo potrebno analizirati 62

71 veliko različnih plastičnih in neplastičnih materialov. Tako bi zgradili podatkovno bazo, s katero bi lahko učinkovito naučili nevronske mreže klasificirati materiale na podlagi spektrov PL svetlobe in zagotovili hitro, natančno in avtomatizirano analiziranje vzorcev mikroplastike. 63

72 BIBLIOGRAPHY [1] Global production of plastics 2014 Statistics. Retrieved , from /. [2] J. R. Jambeck, R. Geyer, C. Wilcox, T. R. Siegler, M. Perryman, A. Andrady, R. Narayan and K. L. Law, Plastic waste inputs from land into the ocean, Science 347, 768 (2015). [3] P. G. Ryan and C. L. Moloney, Marine litter keeps increasing, Nature 361, 23 (1993). [4] R. C. Thompson, Y. Olsen, R. P. Mitchell, A. Davis, S. J. Rowland, A. W. G. John, D. McGonigle and A. E. Russell, Lost at sea: where is all the plastic?, Science 304, 838 (2004). [5] PlasticEurope, Plastic - the Facts Retrieved from final_plasticsthefacts_nov2012_en_web_resolution.pdf. [6] S. Yoshida, K. Hiraga, T. Takehana, I. Taniguchi, H. Yamaji, Y. Maeda, K. Toyohara, K. Miyamoto, Y. Kimura and K. Oda, A bacterium that degrades and assimilates poly(ethylene terephthalate), Science 351, 1 (2016). [7] V. Hidalgo-Ruz, L. Gutow, R. C. Thompson and M. Thiel, Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification, Environ. Sci. Technol. 46, 3060 (2012). [8] Y. K. Song, S. H. Hong, M. Jang, G. M. Han, M. Rani, J. Lee and W. J. Shim, A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples, Mar. Pollut. Bull. 93, 202 (2015). [9] R. Lenz, K. Enders, C. A. Stedmon, D. M. A. Mackenzie and T. Gissel, A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement, MPB 100, 82 (2015). [10] C. Wesch, A. Barthel, U. Braun, R. Klein and M. Paulus, No microplastics in benthic eelpout ( Zoarces viviparus ): An urgent need for spectroscopic analyses in microplastic detection, Environ. Res. 148, 36 (2016). [11] A. L. Andrady, Microplastics in the marine environment, Mar. Pollut. Bull. 62, 1596 (2011). [12] C. J. Moore, Synthetic polymers in the marine environment: A rapidly increasing, long-term threat, Environ. Res. 108, 131 (2008). [13] M. A. Browne, P. Crump, S. J. Niven, E. Teuten, A. Tonkin, T. Galloway and R. Thompson, Accumulation of microplastic on shorelines woldwide: Sources and sinks, Environ. Sci. Technol. 45, 9175 (2011). 64

73 [14] M. R. Gregory, Plastic scrubbers in hand cleansers: a further (and minor) source for marine pollution identified, Mar. Pollut. Bull. 32, 867 (1996). [15] L. S. Fendall and M. a. Sewell, Contributing to marine pollution by washing your face: Microplastics in facial cleansers, Mar. Pollut. Bull. 58, 1225 (2009). [16] J. P. G. L. Frias, P. Sobral and A. M. Ferreira, Organic pollutants in microplastics from two beaches of the Portuguese coast, Mar. Pollut. Bull. 60, 1988 (2010). [17] L. M. Rios, C. Moore and P. R. Jones, Persistent organic pollutants carried by synthetic polymers in the ocean environment, Mar. Pollut. Bull. 54, 1230 (2007). [18] C. M. Rochman, E. Hoh, B. T. Hentschel and S. Kaye, Long-term field measurement of sorption of organic contaminants to five types of plastic pellets: Implications for plastic marine debris, Environ. Sci. Technol. 47, 1646 (2013). [19] E. L. Teuten, S. J. Rowland, T. S. Galloway and R. C. Thompson, Potential for Plastics to Transport Hydrophobic Contaminants, Environ. Sci. Technol. 41, 7759 (2007). [20] Y. Mato, T. Isobe, H. Takada, H. Kanehiro, C. Ohtake and T. Kaminuma, Plastic Resin Pellets as a Transport Medium for Toxic Chemicals in the Marine Environment, Environ. Sci. Technol. 35, 318 (2001). [21] E. R. Zettler, T. J. Mincer and L. a. Amaral-Zettler, Life in the plastisphere : Microbial communities on plastic marine debris, Environ. Sci. Technol. 47, 7137 (2013). [22] M. a Browne, A. Dissanayake, T. S. Galloway, D. M. Lowe and R. C. Thompson, Ingested Microscopic Plastic Translocates to the Circulatory System of the Mussel, Mytilus edulis ( L.) Ingested Microscopic Plastic Translocates to the Circulatory System of the Mussel, Mytilus edulis ( L.), Environ. Sci. Technol. 42, 5026 (2008). [23] N. von Moos, P. Burkhardt-Holm and A. Koehler, Uptake and Effects of Microplastics on Cells and Tissue of the Blue Mussel Mytilus edulis L. after an Experimental Exposure, Environ. Sci. Technol. 46, 327 (2012). [24] E. L. Teuten, J. M. Saquing, D. R. U. Knappe, M. a Barlaz, S. Jonsson, A. Björn, S. J. Rowland, R. C. Thompson, T. S. Galloway, R. Yamashita, D. Ochi, Y. Watanuki, C. Moore, P. H. Viet, T. S. Tana, M. Prudente, R. Boonyatumanond, M. P. Zakaria, K. Akkhavong, Y. Ogata, H. Hirai, S. Iwasa, K. Mizukawa, Y. Hagino, A. Imamura, M. Saha and H. Takada, Transport and release of chemicals from plastics to the environment and to wildlife., Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 2027 (2009). [25] E. Besseling, A. Wegner, E. M. Foekema, M. J. Van Den Heuvel-Greve and A. a. Koelmans, Effects of microplastic on fitness and PCB bioaccumulation by the lugworm Arenicola marina (L.), Environ. Sci. Technol. 47, 593 (2013). [26] S. L. Wright, D. Rowe, R. C. Thompson and T. S. Galloway, Microplastic 65

74 ingestion decreases energy reserves in marine worms, Curr. Biol. 23, R1031 (2013). [27] M. A. Browne, S. J. Niven, T. S. Galloway, S. J. Rowland and R. C. Thompson, Microplastic moves pollutants and additives to worms, reducing functions linked to health and biodiversity, Curr. Biol. 23, 2388 (2013). [28] L. Van Cauwenberghe and C. R. Janssen, Microplastics in bivalves cultured for human consumption, Environ. Pollut. 193, 65 (2014). [29] A. Mathalon and P. Hill, Microplastic fibers in the intertidal ecosystem surrounding Halifax Harbor, Nova Scotia, Mar. Pollut. Bull. 81, 69 (2014). [30] C. G. Avio, S. Gorbi, M. Milan, M. Benedetti, D. Fattorini, G. D Errico, M. Pauletto, L. Bargelloni and F. Regoli, Pollutants bioavailability and toxicological risk from microplastics to marine mussels, Environ. Pollut. 198, 211 (2015). [31] C. M. Rochman, T. Kurobe, I. Flores and S. J. Teh, Early warning signs of endocrine disruption in adult fish from the ingestion of polyethylene with and without sorbed chemical pollutants from the marine environment, Sci. Total Environ. 493, 656 (2014). [32] C. R. Nobre, M. F. M. Santana, A. Maluf, F. S. Cortez, A. Cesar, C. D. S. Pereira and A. Turra, Assessment of microplastic toxicity to embryonic development of the sea urchin Lytechinus variegatus (Echinodermata: Echinoidea), Mar. Pollut. Bull. 92, 99 (2015). [33] P.. Ryan and S. Jackson, The lifespan of ingested plastic particles in seabirds and their effect on digestive efficiency, Mar. Pollut. Bull. 18, 217 (1987). [34] P. G. Ryan, Effects of ingested plastic on seabird feeding: Evidence from chickens, Mar. Pollut. Bull. 19, 125 (1988). [35] S. Tanabe, M. Watanabe, T. B. Minh, T. Kunisue, S. Nakanishi, H. Ono and H. Tanaka, PCDDs, PCDFs, and Coplanar PCBs in Albatross from the North Pacific and Southern Oceans: Levels, Patterns, and Toxicological Implications, Environ. Sci. Technol. 38, 403 (2004). [36] J. D. Meeker, S. Sathyanarayana and S. H. Swan, Phthalates and other additives in plastics: human exposure and associated health outcomes., Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 2097 (2009). [37] C. E. Talsness, A. J. M. Andrade, S. N. Kuriyama, J. a Taylor and F. S. vom Saal, Components of plastic: experimental studies in animals and relevance for human health., Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 2079 (2009). [38] H. M. Koch and A. M. Calafat, Human body burdens of chemicals used in plastic manufacture., Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 2063 (2009). [39] M.-T. Nuelle, J. H. Dekiff, D. Remy and E. Fries, A new analytical approach for 66

75 monitoring microplastics in marine sediments., Environ. Pollut. 184, 161 (2014). [40] H. K. Imhof, J. Schmid, R. Niessner, N. P. Ivleva and C. Laforsch, A novel, highly efficient method for the separation and quantification of plastic particles in sediments of aquatic, Limnol. Oceanogr. Methods 10, 524 (2012). [41] M. Claessens, L. Van Cauwenberghe, M. B. Vandegehuchte and C. R. Janssen, New techniques for the detection of microplastics in sediments and field collected organisms., Mar. Pollut. Bull. 70, 227 (2013). [42] X. Zhu, Optimization of elutriation device for filtration of microplastic particles from sediment, Mar. Pollut. Bull. 92, 10 (2015). [43] E. Smith and G. Dent, Modern Raman Spectroscopy - A Practical Approach (John Wiley & Sons, Chichester, 2004). [44] W. Demtröder, Laser Spectroscopy Volume 2, 4th ed. (Springer, Berlin, 2008). [45] P. R. Griffiths and J. A. de Haseth, Fourier Transform Infrared Spectrometry, 2nd ed. (John Wiley & Sons, Hoboken, 2007). [46] J. P. Harrison, J. J. Ojeda and M. E. Romero-González, The applicability of reflectance micro-fourier-transform infrared spectroscopy for the detection of synthetic microplastics in marine sediments, Sci. Total Environ. 416, 455 (2012). [47] F. Remy, F. Collard, B. Gilbert, P. Compere, G. Eppe and G. Lepoint, When Microplastic Is Not Plastic: The Ingestion of Artificial Cellulose Fibers by Macrofauna Living in Seagrass Macrophytodetritus, Environ. Sci. Technol. 49, (2015). [48] M. G. J. Löder, Methodology Used for the Detection and Identification of Microplastics A Critical Appraisal (Springer, Springer ebook, 2015). [49] A. Vianello, A. Boldrin, P. Guerriero, V. Moschino, R. Rella, A. Sturaro and L. Da Ros, Microplastic particles in sediments of Lagoon of Venice, Italy: First observations on occurrence, spatial patterns and identification, Estuar. Coast. Shelf Sci. 130, 54 (2013). [50] A. S. Tagg, M. Sapp, J. P. Harrison and J. J. Ojeda, Identification and Quantification of Microplastics in Wastewater Using Focal Plane Array-Based Reflectance Micro-FT-IR Imaging, Anal. Chem. 87, 6032 (2015). [51] M. G. J. Löder, M. Kuczera, S. Mintenig, C. Lorenz and G. Gerdts, Focal plane array detector-based micro-fourier-transform infrared imaging for the analysis of microplastics in environmental samples, Environ. Chem. 12, 563 (2015). [52] J. R. Albani, Fluorescence Spectroscopy (Blackwell Publishing, Oxford, 2007). [53] C. E. White and R. J. Argauer, Fluorescence Analysis A Practical Approach (Marcel Dekker, New York, 1970). 67

76 [54] N. Zettili, Quantum Mechanics Concepts and Applications, 2nd ed. (John Wiley & Sons, Chichester, 2009). [55] J. R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd ed. (Springer Science+Business Media, New York, 2006). [56] A. J. W. G. Visser and O. J. Rolinski, Basic Photophysics. Retrieved , from [57] I. Johnson, The Molecular Probes Handbook: A Guide to Fluorescent Probes and Labeling Technologies, 11th ed. (Thermo Fisher Scientific, Waltham, 2010). [58] K. Grabmayer, G. M. Wallner, S. Beißmann, J. Schlothauer, R. Steffen, D. Nitsche, B. Röder, W. Buchberger and R. W. Lang, Characterization of the aging behavior of polyethylene by photoluminescence spectroscopy, Polym. Degrad. Stab. 107, 28 (2014). [59] S. Tóth, M. Füle, M. Veres, I. Pócsik, M. Koós, A. Tóth, T. Ujvári and I. Bertóti, Photoluminescence of ultra-high molecular weight polyethylene modified by fast atom bombardment, Thin Solid Films 497, 279 (2006). 68

77 APPENDIX A CT IMAGE OF THE MICROPLASTIC SEDIMENT TEST SAMPLE A glass bottle was filled with pure sand and spiked with 10 PVDF and 15 PS granules, which were approximately 5 mm in diameter. The sample was imaged using a micro-ct device. The result of the micro-ct imaging is presented in Figure1. It was possible to clearly distinguish between the plastic granules and sand. Furthermore, a differentiation between the two types of plastic granules was also possible. These preliminary results make the micro-ct imaging a promising technique for the microplastic detection, especially from sediment sample. However, additional research is needed in order to determine the final applicability of the method and its limitations in detecting microplastics. Figure1. The CT image of the prepared sample containing sand and two types of plastic granules. The green objects correspond to the PS granules and the purple ones to the PVDF granules. The micro-ct imaging was done by F. & G. Hachtel GmbH & Co. KG, D Aalen. 69