New Methods of Thermal Analysis and Chemical Mapping on a Micro and Nano Scale by Combining Microscopy with Image Analysis

Transcription

1 New Methods of Thermal Analysis and Chemical Mapping on a Micro and Nano Scale by Combining Microscopy with Image Analysis M. Reading 1, M. Morton 1, M. Antonijevic 2, D. Grandy 3, D. Hourston 3 and A Lacey 4 1 Cyversa, 223c Earlham Road, Norwich NR2 3RQ, UK 2 University of Greenwich, Faculty of Engineering and Science, Central Avenue, Chatham Maritime, ME4 4TB, UK 3 Loughborough University, Loughborough, Leicestershire LE11 3TU, UK 4 Heriot-Watt University, Edinburgh Campus, Edinburgh EH14 4AS, UK A new approach to thermal analysis is described that combines microscopy, a temperature controlled stage and image analysis. It is shown how images acquired using a hot stage microscope can be analysed by a robust algorithm that tracks how structures change as a sample is heated; we call this new method thermal analysis by structural characterisation or TASC. We present an overview of how it can be used to measure glass transitions and examples of its application to polymers and pharmaceuticals are given including an example of how it can be used to study the kinetics of devitrification. Because a discrete feature on a sample surface is analysed, TASC can be used to perform local thermal analysis or LTA. Examples of this are given including one performed on a nanoscale using atomic force microscopy. The same algorithm can be used to follow dissolution rates. It is shown how materials can be distinguished between using differences in dissolution kinetics and how this represents a new paradigm for mapping chemical composition called chemical imaging by dissolution analysis or CIDA. Future prospects and the implications for nanoscale chemical imaging are discussed. Keywords: Thermal analysis; local thermal analysis; TASC; thermomicroscopy; polymers; pharmaceuticals; Nano-TA; Nano-IR 1. Introduction Thermal methods comprise a family of techniques that have a wide range of applications in materials characterisation [1,2]. Thermomicroscopy, abbreviated to TM (also called hot stage microscopy or thermooptical analysis), is a wellestablished technique with a number of well-defined applications [1,2], notably characterizing how crystal structure changes with temperature. However, it is very limited in what it can achieve when analyzing samples that are filled and it cannot be routinely used for detecting glass transitions. We describe a new technique that overcomes these limitations; it relies on using image analysis to process videos made using TM with a digital camera. Thermal analysis by structural characterization (TASC) is a method based on characterising how a selected structure changes with temperature. Typically, TASC involves creating a feature on the surface of the sample, such as an indentation, and then quantifying how it transforms as the sample is heated. If the sample goes through a glass transition or melts then it usually flows due to the action of surface tension and the indentation disappears. In this way softening points can be detected and from these transition temperatures can be inferred. TASC can be carried out using multiple indentations (or other structures); multiple measurements on a homogeneous sample means averaging can be used. If the sample is heterogeneous then different transition temperatures at different locations can be measured (local thermal analysis or LTA). TASC can also be applied to intrinsic structures thus how crystals melt or dissolve can be characterized. How these techniques can be applied to polymers and pharmaceutical samples will be described including preliminary results that illustrate how TASC can be applied on the nanoscale using scanning probe microscopy (SPM). A further application of these concepts leads to a form of chemical mapping. The future prospects of this new paradigm for micro and nano analysis are discussed. 2. The TASC algorithm The TASC algorithm consists of first parameterising a designated structure then locating this structure within a designated area and quantifying the extent to which it conforms to its original form; in this way it tracks how the structure changes over time. Figure 1 demonstrates how a schematic representation of a hole or indentation is identified and located, thus in Figure 1c) the apex of the cone is the point where the centre of the target structure is located and its height is a measure of how similar it is to the designated structure. One type of experiment that employs this approach is to impose an indentation on the surface of a sample and measure how it changes: this is illustrated in Figure 2 which shows pictures taken with a hot stage microscope of four indentations that were imposed on the surface of a sample of filled polystyrene (see materials and methods below). As the temperature of the sample was increased to above its glass transition temperature the indentations disappear due to the action of surface tension. 1083

2 Fig. 1 The TASC algorithm allows a structure to be designated as a target, a) is a schematic representation of an indentation located in the top right corner. The algorithm locates the structure as being in the centre of the coloured area in b). The height of the apex, as shown in the 3D representation c), is a measure of how close the located structure conforms to the target structure. Fig. 2 Above there are screen shots of the software interface for a computer program carrying out the TASC analysis. Image a) comprises a number of elements including a picture taken with a hot stage microscope of a set of four indentations; the sample was a piece of filled polystyrene. Below this picture is a graph showing the output of the TASC algorithm as a function of time. The inset is a 3D representation of the results of the analysis in a form equivalent to that shown in Figure 1c). Images a), b) and c) show the results of heating the sample to above its glass transition temperature: a) is before the transition, b) is some intermediate point and c) after the transition is complete. The type of measurement illustrated in Figure 2 is well established [3]. However, this approach has not been adopted as a method for routinely measuring softening points (from which glass transitions can be inferred) because to do so requires a robust image analysis method that can be applied to a wide variety of samples. One important requirement for such a method is that it is able to distinguish any movement of the structure from changes in its size. Samples move in thermal analysis experiments for a variety of reasons including thermal expansion; however, this is generally not of interest in the type of experiment envisaged here. The objective is to characterise changes in the shape of the selected structure and the TASC algorithm is able to do this by tracking the target as illustrated in Figure 2: a feature is selected, in this case one of the indentations (framed by the red square), then, as the material softens, the indentation disappears and the event is characterised by a sigmoidal step change in the TASC output that can be analysed in the same way as conventional thermal analysis data. Figure 3 provides a comparison with DSC. The onset of the TASC measurement corresponds to the offset of the change in heat capacity thus, as might be expected, the two measurements probe different molecular motions. In the case of TASC cooperative motions leading to polymer chains moving over one another have to take place before the indentation starts to relax to form a smooth surface. Calorimetry is also sensitive to in-chain moieties acquiring additional degrees of freedom. These two methods are, therefore, highly complementary. Fig. 3 The graphs provide a comparison between differential scanning calorimetry (DSC), shown as diamonds, and the TASC result, shown as circles, for polystyrene (both normalised to their final value). The softening event measured by TASC occurs after the glass transition as measured by DSC. Because the TASC measurement is non-contact, it could be combined with DSC by using a hot stage DSC instrument such as the Linkam DSC600. This can be done without significantly compromising the calorimetric measurement. This work has yet to be done but there is the prospect that significantly more information could be routinely extracted from a DSC-TASC experiment than from DSC alone. 1084

3 3. Theory Fig. 4 A schematic diagram of an indentation. The various quantities are given in the legends The pressure inside a sample due to the surface curvature is approximately γ 2 H/ x 2 which is of size γh/r 2, where γ is the surface tension, H(x,t) is the local depression of the surface of the sample at position x on the sample's surface and time t, h(t) is a measure of the depth of the dimple (its maximum or average depth) at time t, and R gives an indication of the expanse of the depression (see Fig. 4). The gradient of pressure along the surface which drives flow inside the specimen is then γ 3 H/ x 3 and of size γh/r 3 For slow linear flow within the specimen, its velocity v and pressure p satisfy, in two dimensions, μ( 2 v/ x v/ y 2 ) ~ ( p/ x, p/ y) where μ = viscosity (this can be extended to three dimensions and the same basic result is achieved). This equation equates the pressure gradient with the viscosity times the second derivative of velocity, which can be expected to be of typical size V/R 2 if V is a typical speed in the sample. Balancing the pressure gradient, of size γh/r 3, with second derivative of velocity, of size V/R 2, therefore indicates that V~γ h/(μr). Now the indentation surface is a material boundary: it moves with the material velocity v. Thus, the rate of decrease of h has to be of size V: dh/dt ~ C 1 V ~ C 2 γh/(μr), where C 1 and C 2 are some numeric constants fixed by the geometry of the specimen and indentation. It follows that the depth of the dimple shrinks as h ~ constant exp( C 2 γ t /(μr)). The time constant is then μr/(γ C 2 ). This result demonstrates that the time constant for the relaxation process is a function of both the size and geometry of the indentation. Consequently, these two things must always be the same when making comparisons. The other variable is surface tension. This parameter is usually approximately a linear function of temperature and its effect must be taken into consideration. In the case of the polystyrene sample discussed above, the surface tension changes by less than 10% over the course of the transition region [4]. This is not significant compared to the orders of magnitude changes in viscosity that occur at the same time. This will often be the case but care must be taken when interpreting results. 4. Materials and Methods All experiments were conducted using a Linkam 600 stage and imaging station. IR spectra were measured using a Perkin-Elmer Spectrum 1 FTIR with an ATR accessory. SPM images were obtained with a Topometrix Explorer (Santa Barbra, US). TASC was applied to Indomethacin (Sigma-Aldrich UK), salicylic acid (Sigma-Aldrich UK) and StripSytrene (polystyrene filled with calcium carbonate) samples (Evergreen, Woodenville, WA, US). The TASC software used on-line on the Cyversa website at 5. Local Thermal Analysis The original method for local thermal analysis was developed by Hammiche, Pollock, Reading and their co-workers over a period of years (reviewed in [5], [6] and [7]). It employs a heated probe manipulated by a Scanning Probe Microscope (SPM). The tip is placed on a point on the surface of the sample and its temperature is increased linearly with time. When the temperature reaches the glass transition or melting temperature of the material immediately beneath the surface of the tip softens so the tip indents and this is detected by the control mechanism of the SPM; in this way local transition temperatures are measured. Originally a hand-made probe was used that had a tip with a contact area of microns. This was called micro-ta [5], Subsequently, micromachined probes were made that have a spatial resolution of nanometres [6,7]; the technique then graduated to nano-ta which is now an established technique marketed by leading manufacturers. TASC can be used for local thermal analysis because an indentation can be made anywhere on the surface of a sample; this is illustrated in Figure 5. Note that the measurement was based on optical micrographs; TASC can be used with any form of microscopy rather than being limited to scanning probe microscopy, thus its applications are potentially broader than the heated-tip method. TASC has certain advantages because it maintains a uniform 1085

4 temperature field whereas heated-tip LTA imposes acute temperature gradients in the vicinity of the point of contact of the probe with the sample surface. Fig. 5 An example of local thermal analysis using TASC. The optical micrograph of the sample suggests two regions of different appearance and possibly, therefore, different compositions. The points A and B at which the indentations were made are indicted in the image. The TASC results clearly demonstrate two different transition temperatures. It follows that there are compositional or structural differences between the two domains. The temperature gradients experienced when using the heated-tip method depend on a number of factors including the quality of the contact between the probe tip and the sample. The area of contact varies significantly as the probe moves over the sample when the size of the probe tip is of the same order or smaller than the length scale of the roughness of the sample surface; this then introduces errors. For example, a distribution of over 20 o C has been observed when measuring the melting point of paracetamol in a compact using a nanoscale probe ([7], see Fig.11) this is over an order of magnitude worse that when using conventional thermal methods. Furthermore, in addition to this lack of precision, there can be a lack of accuracy when the physical properties of the sample are different from those of the calibration standards. This does not mean that the heated-tip approach is of no value, on the contrary; an experienced user can, with care, obtain valuable information. However, TASC offers a more reliable and more accurate approach because of its use of a uniform temperature field and the fact conventional calibration methods can be used. TASC can also be used on a nanoscale by replacing the optical microscope with an SPM; Figure 6 illustrates this along with optical TASC for comparison; the difference in size of the indentation in the two cases or over two orders of magnitude. Given that theory predicts that the size of the structure influences its behaviour the degree of agreement is striking. Understanding the reasons for this requires further work. 6. Glass Transition Kinetics An important aspect of drug formulations is bioavailability; having the drug in an amorphous form can enhance this because amorphous material usually dissolves faster than the crystalline form [2]. The stability of amorphous pharmaceuticals therefore becomes an important issue and this can be studied by analysing the kinetics of the glass transitions of these materials [8]. In any study of this type the time scales over which measurements can be made is an important consideration. Often the dynamic range is limited; for example the sensitivity of DSC decreases dramatically when slow heating rates are used and fast heating rates pose problems because of the response times of these instruments and difficulties associated with temperature gradients within the sample. Using multiple types of calorimeter can extend the range, but few laboratories have the necessary equipment and expertise. Fig. 6 The application of TASC on a nanoscale is demonstrated by this example using polystyrene. The SPM images a) and b) (size 1μx1μ) show how an indentation changes moving from 90 o C to 120 o C. Graph c) shows the results from TASC applied to the SPM images, shown as circles, and optical TASC shown as squares (size of indentation 200μ), both experiments were carried out at 1 o C/min. 1086

5 The dynamic range of some DMA s can appear broad but achieving good results for any given sample over many decades is, in practice, difficult. Probably dielectric spectroscopy can achieve the greatest range amongst conventional techniques, but this cannot be applied to all systems and conductivity and space charges can complicate the interpretation of results. TASC offers the prospect of being able to acquire data over a very broad range of heating rates because its sensitivity is not affected by how fast or slowly the sample is heated. A thin sample can be used and thus heating rates can be very high. Also samples can be thermally treated off-line so very long time scales become practical. Figure 7 gives results for amorphous indomethacin. The sample was heated above its glass transition before the tool was placed on the sample to make the indentations (the same pattern of four indentations as illustrated in Figure 2 was used); it was then rapidly cooled before the tool was removed. Consequently, at the start of the experiment, the sample contained no frozen-in stress or plastic deformation and the reversion to a smooth surface was a pure relaxation event. Figure 7 shows data across almost five orders of magnitude. The departure from Arrhenius behaviour is very clear over this broad dynamic range. The fastest rate was limited by the performance of the stage that was used; much faster rates are feasible. Using off-line analysis, for example placing a sample in an oven and increasing its temperature by one degree every day after taking a photograph of the sample, could extend the lower heating rate substantially. If a year could be devoted to a study and a faster stage is used then perhaps eight orders of magnitude might be possible. 7. Thermal Dissolution Analysis The methods discussed so far involve creating a structure on the surface of a sample. TASC can also be applied to an intrinsic structure. Thus, for example, melting events can be seen using bright field microscopy. However, a more interesting application is following the dissolution behaviour of crystals. Figure 8 shows in 8a) a micrograph of a set of crystals surrounded by water as the solvent. The crystals dissolved and this process was followed by the TASC algorithm as shown in 8b). A linear heating rate was applied to ensure that the experiment did not take too long. It is striking that the dissolution plots are close to linear. This underlines that the dissolution process is not primarily governed by Arrhenius kinetics although we would expect that the diffusion of solute away from the crystal would follow this behaviour. It is the case that the rate of dissolution is a function of crystal size i.e. larger crystals take longer to dissolve than smaller ones. To characterise this aspect of the process a linear equation was fitted to each plot up to 0.8 of the normalised value, see Figure 9a); this is a measure of the rate of dissolution. The slopes were plotted against the surface area of each crystal to obtain Figure 9b). As stated above, this measure of the rate of dissolution is related to the size of each crystal and it is expected that the rate should be a monotonic function of an appropriate measure of their sizes and geometries. Different shapes of crystal will dissolve in different ways. Thus an elongated crystal with a given mass will dissolve faster than a roughly spherical one because of the differences in surface area to volume ratio. Ideally we would measure these parameters but this is not possible using 2D images as in this case (though 3D images can be obtained with the right equipment; this is discussed below). As an approximate representation of size, the surface area for each crystal was plotted against the slope of its dissolution rate; this is shown in Figure 9b). We see by inspection two markedly different behaviours. It is clear that there are two categories of materials as distinguished by their dissolution kinetics; one of these is shown in red the other in green. Fig. 7 TASC data for polystyrene. The sample was heated at rates of, from left to right, C/min C/min, 2 0 C/min, 20 0 C/min and 150 o C/min as shown in a). The temperature at the mid-point for each heating rate was taken and used for the Arrhenius plot b). The method used was the iterative procedure described in [11]. We can then return to the original micrograph and place each crystal in the appropriate category. This is done using colour coded squares in Figure 9c). In this way, we have determined there are two different types of material within the field of view of the microscope and we have determined the location of each type. We can take this analysis a stage further using chemical analysis of the sample and solvent. Figure 9a) indicates that almost all of the green material has dissolved by circa 25 o C. We can carry out a parallel experiment where the sample (that contains the two materials) is placed in water (with the same solid to liquid ratio as the TASC experiment with the same sparseness in terms of the 1087

6 distribution of the crystals), at 25 o C we can filter out the undissolved material then analyse the material captured by the filter paper and analyse the solvated material (after removing the solvent); in both cases we can analyse them using FTIR spectroscopy. Fig. 8 Thermal dissolution analysis (TDA) of crystals in a DSC pan a). The pan was full of water as the solvent, and the heating rate was 5 o C/min. The disappearance of the crystals was tracked by the TASC algorithm and the results are given in b). Fig. 9 This Figure presents an analysis of the data shown in Figure 8. A line was fitted to the TASC data for each crystal, see a). The slope of the line is a measure of the rate of dissolution. These were then plotted against the surface area of each crystal to obtain b). It is expected that the dissolution rate is a monotonic function of crystal size though scatter is expected because of the irregularities of their shape. Two classes of behaviour are seen; the points are plotted in red and green to indicate the different groups. Each crystal can then be assigned to its group and this is shown in c). Fig. 10 The overall weighted and normalised TASC data are plotted against temperature in a). The crystals classified as being in the green group have almost entirely dissolved after 30 sec. as indicated by the dashed line, while only a relatively small amount of the material classified as red has dissolved. At this point the sample was filtered and an IR spectrum was taken of the solid retained by the filter, biii), and of the filtrate after removal of most of the solvent by rotary evaporation, bi). A spectrum of salicylic acid was used as a comparison, biv); this identified the red material as being salicylic acid. A sample of sucrose was dissolved in water and most of the solvent was removed using a rotary evaporator, the spectrum from this material, bii) confirmed the green material as being sucrose. Figure 10 shows how the chemical nature of each sample can be identified. This then represents a potential new paradigm for mapping chemical compounds. In the case illustrated above isolated crystals were used but, in principle, any type of sample could be analysed in this way. The requirements are a) that there be a form of microscopy that can follow the rate at which materials on the surface dissolve so that different materials can be categorised by their different 1088

7 dissolution kinetics, and b) a means of analysis that can follow the appearance and disappearance of different materials in the solvent and the undissolved residue. By correlating these behaviours, each component and its location could be identified and mapped. This is potentially a general method of analysing and locating materials on a surface. It enables the analytical power of chromatography as well as spectroscopy to be used for chemical imaging. We have shown here how it can be done on the scale of microns, but the principle could be applied on the nanoscale using scanning probe microscopy. We call this chemical imaging by dissolution analysis or CIDA. 8. Overview and Future Prospects We have presented a brief review of the different types of measurement that can be made by combining a digital microscope with a temperature controlled stage. These include local thermal analysis, glass transition kinetics and the ability to study dissolution rates. By combining dissolution analysis with chemical analysis of the solutes and residues, materials with different compositions and physical states can be mapped. This offers some interesting prospects for the future particularly when applied to nanoscale imaging using scanning probe microscopy. Our interest is in real world samples including thick samples that cannot be sectioned or where sectioning is undesirable because it would change the sample s structure. A technique we have developed to meet this need is photothermal IR microscopy [7,9 and 10] using a scanning probe microscope with top-down illumination (a requirement for dealing with thick samples). It was the first such technique to achieve nanoscale spatial resolution (it has now been commercialised as Nano-IR2 TM by Analsys Instruments). We have also demonstrated local depth profiling [7] thus offering the prospect of nanoscale IR tomography. However, achieving true tomography necessitates solving the inverse problem and this remains a significant challenge. CIDA is a completely different approach but it also offers the possibility of nanoscale tomography. As material is dissolved from the surface material beneath the surface is exposed that can in turn be dissolved. Using an AFM, this process can be followed in great detail and, as successive layers are dissolved (possibly using a series of different solvents) a 3D image of the sample could be constructed. If we take the example given above, if the green domains (sucrose) had had a core-shell structure with sucrose on the surface of a core of salicylic acid then there would have been a clear break in the TSA plots as the slope of the curve changed when all of the sucrose had been removed. Reanalysing from the break point would show the core material to have the same dissolution kinetics as the salicylic acid. Analysis of the solvent would confirm its identity. From this information the structure could reconstructed in 3D. This type of experiment will be the subject of future work. It should be noted that it is preferable to use 3D not 2D data. Scanning probe microscopy provides 3Dimages but optical microscopy typically does not. However, using multiple images with different focal planes, often called z stacking, 3D imaging can be achieved in some cases. References [1] Weidemann H.G, Felder-Casagranda S., Thermomicroscopy, in Brown M.E. editor, Handbook of Thermal Analysis and Calorimetry, Elsevier, Chapter 10. [2] Vitez I.M., Newman A.W., Thermal Microscopy: in Reading M., Craig Q.M., editors, Thermal Analysis of Pharmaceuticals, CRC Press, Chapter 7 [3] Gall K, Kreiner P, Turner D, Hilse M, Shape Memory polymers for microelectromechncial systems. J. Microelectromech. Syst. 2004; 13(3) [4] Wu S., Surface and interfacial tensions of polymer melts II. Poly(methyl methacrylate), Poly (n-butyl methacrlate) and Polystyrene, J. Phys. Chem. 1970; 74(3): 632 [5] Hammiche A., Reading M., Pollock H.M., Song M., Hourston D.J., Localized thermal analysis using a miniaturized resistive probe, Rev. Sci. Instrum. 1996; 67: [6] Gorbunov V.V., Grandy.D., Reading M., Tsukruk, Micro and nanoscale localized thermal analysis in: Menzel R.B., Prime B., editors, Thermal analysis of polymers: fundamentals and applications, Wiley, 2008; [7] Dai X., Moffat J.G., Wood J., Reading M., Thermal scanning probe microscopy in the development of pharmaceuticals, Advanced Drug Delivery Reviews 2012; 64: [8] Vyazovkin S., Dranca I., Comparative Relaxation Dynamics of Glucose and Maltitol, Pharmaceutical Research. 2006; 23: [9] Hammiche A., Bozec L., German M.J., Chamlmers J.M., Everall N.J. Poulter G., Reading M., Grandy D.B., Martin F.L., Pollock H.M, Mid-infrared microspectroscopy of difficult samples using near-field phtothermal micro-spectroscopy (PTMS), Spectroscopy; 2004; 19(2): [10] Hill G.A., Rice J.H., Meech S.R., Craig D.Q.M., Kuo P., Vodopyanov K., Reading M., Submicrometer infrared surface imaging using a scanning-probe microscope and an optical parametric oscillator laser, Opt. Lett.; 2009; 34, [11] Dollimore D, Reading M, Application of Thermal Analysis to Kinetic Evaluation of Thermal Decompostion, in Winefordner JD editor, Treatise on Analytical Chemistry, John Wiley & Sons, 1993, Part