Supporting Information Interplay of natural organic matter with flow rate and particle size on colloid transport: Experimentation, visualization and modeling Xinyao Yang a,1,*, Yimeng Zhang b,1, Fangmin Chen b, Yuesuo Yang a a Ministry of Education Key Lab for Eco-restoration of Contaminated Environment, Shenyang University, No. 21, South Wanghua Street, Shenyang 110044, China. b College of Resources and Environment, Sichuan Agricultural University, No. 211, Huiming Road, Chengdu 611130, China. 1 : these authors are co-first authors. *Corresponding author: Xinyao Yang (Email: yangxinyao@hotmail.com; Tel.: +86 24 62267041; Fax: + 86 24 62266983) S1
Table of Content PART I Table of Figure Captions 1.1. Figure S1 TEM and DLS measurement of 50 nm nanoparticles and 200 nm microspheres in the experimental solution. 1.2. Figure S2 Relationship between the measured optical signal of UV-vis spectrophotometer and the particle concentration. 1.3. Figure S3 Scanning Electron Microscope images of uncoated and Fe-oxide-coated sands. 1.4. Figure S4 Schematic illustration of the concept of shadow zone effect. 1.5. Figure S5 RSA model simulated breakthrough curves (BTCs) of the 200 nm microsphere and 50 nm nanoparticle at the same particle number concentration (distinct mass concentration) and at the same mass concentration (distinct particle number concentration). 1.6. Figure S6 Energy profile of DLVO interactions (electrostatic and van der Waals) operating between the sand and the nanoparticle or the microsphere. 1.7. Figure S7 Model simulation of experimental particle BTCs for quantifying site blockage by NOM. 1.8. Figure S8 Effect of pre-deposited small particle on blocking large particle. PART II Table of Table Captions 2.1. Table S1 Zeta potential and hydrodynamic particle size of nanoparticles and microspheres measured in the absence and presence of SRHA 2.2. Table S2 Particle collision efficiency for Triple Pulse Experiments with NOM PART III Quantification Procedure 3.1. Procedure to quantify the particle site blockage by NOM 3.2. Procedure to calculate the main effect of flow rate and particle size and their interaction factor 3.3. Matlab Code to generate the isobole curves 3.4. Procedure to calculate the change in BTC slope in response to the varying flow rate S2
PART I Supplement Figures Figure S1 TEM and DLS measurement of 50 nm nanoparticles and 200 nm microspheres in the experimental solution. TEM images show excellent stability of the 50 nm nanoparticle (A) and 200 nm microsphere (B). DLS data show very uniform size distribution of the 50 nm nanoparticle (C) and 200 nm microsphere (D) around the stated sizes. S3
Figure S2 Relationship between the measured optical signal of UV-vis spectrophotometer and the particle concentration. The tested concentration range covers the concentration of the nanoparticle and microsphere used for column experiments. S4
Figure S3: Scanning Electron Microscope (JEOL JSM 6500F Field Emission Scanning Electron Microscope) images of uncoated and Fe-oxide-coated sands. Both sands are well rounded to subangular with localized rough surfaces and cracking on individual grains. Sand dimensions between 120µm and 320µm. (A) Uncoated quartz sand (Sigma-Aldrich, Dorset, England) showing dimensions of representative grains. (B) Iron-oxide coated sand showing dimensions of representative grain sizes. (C) Detail of smooth surface on uncoated sand. (D) Detail of smooth surface on iron-oxide-coated sand. (E) Detail of rough surface on uncoated sand. (F) Detail of rough surface on Iron-oxide coated sand. Note the greater abundance of micron to sub-micronsized particles on coated sands. S5
Figure S4 Schematic illustration of the concept of shadow zone effect (modified from [1]). Due to the electrostatic and hydrodynamic interaction between a deposited and an approaching particle, the approaching particle could not deposit in the close vicinity of the deposited particle, but at a certain distance downgradient the deposited particle. The area inaccessible by the approaching particle due to the deposited particle is called shadow zone. S6
Modeling parameters Simulation 1 50nm (red ) Simulation 2 50nm (black) Simulation 3 200nm (blue) Particle diameter (nm) 50 50 200 Sand diameter (mm) 0.15 0.15 0.15 Bed thickness (cm) 7.50 7.50 7.50 Particle mass 0.16 10.40 10.40 concentration (ppm) Particle number 2.48 159 2.48 concentration ( 10 9 particles/ ml) Flow rate (ml/min) 1 1 1 Single collector efficiency 0.015 0.015 0.005 Excluded area parameter 20 20 21.7 Figure S5 RSA model simulated breakthrough curves (BTCs) of the 200 nm microsphere and 50 nm nanoparticle at the same particle number concentration (distinct mass concentration) and at the same mass concentration (distinct particle number concentration). S7
Figure S6 Energy profile of DLVO interactions (electrostatic and van der Waals) operating between the sand and the nanoparticle or the microsphere, calculated using formula from [2]. Model parameters: Zeta potential of microsphere=-60 mv (measured), zeta potential of nanoparticle=-38 mv (measured), Hamaker constant=3.22e-20 J [3], zeta potential of iron oxidecoated sand=-50 mv [3]. S8
Figure S7 Model simulation of experimental particle BTCs for quantifying site blockage by NOM: (A) microsphere (200 nm) at 1ml/min; (B) microsphere at 2 ml/min; (C) nanoparticle (50 nm) at 1 ml/min; (D) nanoparticle at 2 ml/min. S9
Figure S8 Effect of pre-deposited small particle on blocking large particle. For a collector precoated with small particles, the jamming limit (inverse of the excluded area parameter) ( θ l ) of the larger particle decreases as the size ratio of the larger-to-smaller particles (dl/ds) increases, suggesting the blocking effect of smaller particle on the larger particle increases with increasing size difference between the small and the large particles.(modified from [4]) S10
PART II Supplement Tables Table S1 Zeta potential and hydrodynamic particle size measured in the absence and presence of SRHA* SRHA-free SRHA-present size(nm) zeta(-mv) size(nm) zeta(-mv) 200nm 205 60 200 56 50nm 60 38 54 39 *SRHA alone cannot be measured Table S2 Particle collision efficiency for Triple Pulse Experiments with NOM * Experiment 200 nm 50 nm Flow rate (ml/min) Pulse start Pulse 1 (bf NOM) Pulse end Pulse 3 (aft NOM) Pulse start Pulse end 1 0.35 0.27 0.24 0.19 2 0.36 0.26 0.20 0.15 1 0.23 0.10 0.10 0.05 2 0.33 0.17 0.16 0.06 *Calculated using the filtration theory of [5] S11
PART III Quantification Procedure 3.1. Quantifying the site blocking by SRHA [6] Step 1: Fitting of the RSA model generated BTC to the 1 st experimental particle BTC to determine the model calibration parameters (single collector efficiency and excluded area parameter). Step 2: Using the same parameters, run the RSA model for a prolonged period. Meanwhile, move the second experimental particle BTC laterally along the x-axis (leftwards or rightwards) until the inflection point (the meeting point of the rapidly rising limb and the gradually rising limb of the BTC) intercepts the model generated BTC. Step3: Integration of the model-generated BTC for the interval between the end (maximum) of the 1 st BTC and the start (inflection point) of the relocated second experimental particle BTC to estimate the number of particles that needs to be deposited to generate the response as observed in the second experimental particle BTC. Step 4: Integration of the experimental BTC of NOM to calculate the amount of NOM adsorbed in the experiment. Step 5: As the number of deposited particles predicted by the RSA model in Step 3 blocks the same amount of particle sites as the NOM mass adsorbed in pulse 2. Equating the two gives the number of particle sites blocked per unit NOM mass. 3.2. Procedure to calculate the main effect of flow rate and particle size and their interaction factor [7] Number of particle sites blocked per μg SRHA ( 10 8 ) Flow rate Particle size 50 nm 200 nm 1 ml/min 7.38 51.80 2 ml/min 10.50 59.50 The above table data was used to plot the straight lines in Figure 3 and to calculate the three factors as below: Main effect of flow rate= (10.50-7.38)/2+(59.50-51.80)/2=5.41 Main effect of particle size= (51.80-7.38)/2+(59.50-10.50)/2=46.71 Interaction factor= (7.38+59.50)/2-(10.50+51.80)/2=2.29. 3.3. Matlab Code to generate the isobole curves The code below was compiled in Matlab to generate the isoboles in Figure 3. a=load('f:\data.txt'); S12
diameter=a(:,1); flow=a(:,2); site=a(:,3); x=diameter; %particle diameter y=flow; %flow rate z=site; % blocked number of sites per unit OM mass [x1,y1]=meshgrid(50:250,1:6); z1=griddata(x,y,z,x1,y1,'v4'); [C,h]=contour(x1,y1,z1,site); set(h,'showtext','on') xlabel('particle size (nm)') ylabel('flow rate(ml/min)') saveas(gcf,'isolobe.tif') xlswrite('isolobe',z1) 3.4. Procedure to calculate the change in BTC slope in response to the varying flow rate (1) For the first pulse BTC of the microsphere: BTC slope at 1 ml/min: BS1=(0.16-0.11)/pulse length=0.05/pulse length; BTC slope at 2 ml/min: BS2=(0.36-0.25)/pulse length=0.11/pulse length; Percentage increase in BTC slope: BS =(BS2-BS1)/BS1 100%=120%. (2) For the first pulse BTC of the nanoparticle: BTC slope at 1 ml/min: BS1=(0.14-0.02)/pulse length=0.12/pulse length; BTC slope at 2 ml/min: BS2=(0.18-0.02)/pulse length=0.16/pulse length; Percentage increase in BTC slope: BS =(BS2-BS1)/BS1 100%=33%. References: [1] Ko, C. and Elimelech, M. The "shadow effect" in colloid transport and deposition dynamics in granular porous media: measurements and mechanisms. Environmental Science & Technology 2000, 34 (17), 3681-3689. [2] Elimelech,M.,O'Meila,C.R., 1990.Kinetics of deposition ofcolloidal particles in porous media. Environmental Engineering Science 24, 1528 1536. [3] Truesdail,S.E., Lukasik,J.,Farrah,S.R.,Shah,D.O. and Dickinson,R.B. Analysis of bacterial deposition on metal (hydr)oxide-coated sand filter media. Journal of Colloid and Interface Science 1998, 203, 369-378. S13
[4] Weronski, P. Application of the extended RSA models in studies of particle deposition at partially covered surfaces. Advances in Colloid and Interface Science 2005, 118, 1-24. [5] Tufenkji, N. and Elimelech, M. Correlation equation for predicting single-collector efficiency in physicochemical filtration in saturated porous media. Environmental Science & Technology 2004, 38, 529-536. [6] Yang, X.; Flynn, R.; von der Kammer, F.; Hofmann, T. Quantifying the influence of humic acid adsorption on colloidal microsphere deposition onto iron-oxide-coated sand. Environmental Pollution 2010, 158 (12), 3498-3506; DOI: 10.1016/j.envpol.2010.03.011. [7] Montgomery, C.,Douglas, Runger, C.,George, Eds. Applied statistics and probability for engineers. ; John Wiley & Sons, Inc.: 2003. S14