Evolution of Membrane Fouling Revealed by Label-Free Vibrational

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Evolution of Membrane Fouling Revealed by Label-Free Vibrational Spectroscopic Imaging Wei Chen, Chen Qian, Weili Hong, Ji-Xin Cheng, and Han-Qing Yu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02775 • Publication Date (Web): 10 Aug 2017 Downloaded from http://pubs.acs.org on August 10, 2017

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Environmental Science & Technology

Evolution of Membrane Fouling Revealed by Label-Free Vibrational Spectroscopic Imaging

Wei Chen1, Chen Qian1, Wei-Li Hong2, Ji-Xin Cheng2*, Han-Qing Yu1* 1

CAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei, 230026, China

2

Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47906, USA

1

ACS Paragon Plus Environment


1

Membrane fouling is the bottleneck that restricts the sustainability of membrane

2

technology for environmental applications. Therefore, the development of novel

3

analytical tools for characterizing membrane fouling processes is essential. In this

4

work, we demonstrate a capability of probing the chemical structure of foulants and

5

detecting their 3-dimentional spatial distribution on membranes based on stimulated

6

Raman scattering (SRS) microscopy as a vibrational spectroscopic imaging approach.

7

The adsorption process of foulants onto membrane surfaces and their aggregation

8

process within membrane pores during the micro-filtration of protein and

9

polysaccharide solutions were clearly monitored. Pore constriction and cake layer

10

formation were found to be the coupled membrane fouling mechanisms. This work

11

establishes an ultrafast, highly sensitive, non-destructive and label-free imaging

12

platform for the characterization of membrane fouling evolution. Furthermore, this

13

work provides new insights into membrane fouling and offers a powerful tool for

14

membrane-based process exploration.

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INTRODUCTION

16 17

Water pollution and energy scarcity are among the most critical challenges of modern

18

society. The development of efficient and sustainable techniques for tapping

19

unconventional sources of water and energy is therefore of great importance. In recent

20

decades, membrane-based technologies have become a rising star in the areas of water

21

purification and sustainable energy regeneration.1-3 Membrane filtration systems, such

22

as microfiltration, ultrafiltration, nanofiltration, and forward/reverse osmosis, can

23

effectively remove colloidal particles and dissolved organics with excellent

24

disinfection capability for water cleaning.4,

25

membrane platforms, low-grade heat energy can be converted into mechanical work

26

for energy harvesting.5,

27

technologies is still limited because of membrane fouling, an accumulation of organic,

28

inorganic, or biological foulants on the membrane surface and/or inside the membrane

29

pores, which reduces membrane permeation flux, deteriorates product quality, and

30

increases energy consumption.7-9 Proteins and polysaccharides are the key

31

components of most substances in natural and engineered systems, e.g., dissolved

32

organic matter (DOM)8-10 and microbial extracellular polymeric substances (EPS)11, 12,

33

and are thus recognized as the main factor for membrane fouling. The mechanism

34

behind the membrane fouling induced by proteins and polysaccharides remains

35

unclear due to the complexity of fouling behaviours, coupled mechanical and

36

chemical kinetics, and the lack of effective analytical tools to sufficiently explore the

37

membrane fouling process.

6

5

Furthermore, taking advantage of

However, the widespread application of membrane

38

Membrane fouling is affected by a variety of factors, including membrane

39

materials and structure, feed properties, and operating conditions.5, 7, 8, 13-15 Different

3



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types of fouling forms have been proposed, including pore constriction, pore blockage,

41

and deposit-forming cake layers.7, 16-18 In addition to the conventional approaches,

42

various new techniques have been utilized to characterize membrane fouling

43

processes, such as ultrasonic time domain reflectometry,19,

44

spectroscopy,21,

45

imaging.23 Some of them can provide real-time information on membrane fouling,

46

such as change in porosity and fouling layer formation, but fail to discriminate the

47

different chemical compositions of foulants, which is considered crucial to the

48

evaluation of the roles of foulant species in fouling process.

22

20

electrical impedance

and 3-dimentional (3D) optical coherence tomography (OCT)

49

Optical and spectroscopic techniques have been extensively used to explore the

50

chemical structure of foulants, and various modifications have been performed to

51

enhance the sensitivity and resolution of these techniques. For example, confocal

52

scanning laser microscopy was used to explore the fouling behavior of EPS

53

components on polycarbonate and mixed ester membranes, and a lower-molecular-

54

weight dextran was found to enhance fouling.24 Surface-enhanced Raman scattering

55

(SERS) has been used as a sensitive tool to probe the blocking of membrane pore at

56

initial fouling stages and to monitor the development of biofilms.25-28 However, these

57

techniques require fluorophores or SERS substrates, which themselves cause fouling.

58

Raman spectroscopy has been demostrated as an effective technique for real-time

59

fouling monitoring.29,

60

reflection spectroscopy, is a label-free method that has been used to reveal the

61

structural information of foulants and examine membrane porosity changing

62

profiles.31-33 However, this technique is time-consuming, offers poor spatial resolution

63

and lacks the ability to provide depth information. Recently, synchrotron Fourier

64

transform infrared mapping was applied as a novel approach for membrane fouling

30

Another spectroscopic technique, infrared attenuated total

4


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characterization, and has been proven to have a potential for both qualitative and

66

quantitative characterizations of membrane fouling layer.34 There remains a need for

67

the development of a fast and label-free analytical tool with a high sensitivity and

68

spatial resolution to reveal the real-time chemical information of foulants and

69

elucidate the fouling mechanisms.

70

Stimulated Raman scattering (SRS) microscopy, based on non-linear optics, is an

71

emerging vibrational spectroscopic imaging method with ultrafast, highly sensitive,

72

label-free and non-invasive characteristics, and has been recently applied for the

73

qualitative and quantitative analyses of tissue samples in biomedical engineering.35-38

74

Coupled with multivariate curve resolution-alternative least square (MCR-ALS)

75

analysis, hyperspectral SRS imaging is a powerful tool for investigating cellular

76

machinery,

77

microenvironments.36, 39, 40 With an SRS microscope, the penetration depth of samples

78

can be as far as 200 µm. While MCR-ALS analysis can discriminate proteins,

79

polysaccharides, and nucleic acids because of their characteristic Raman peaks, its

80

spectral resolution is above 20 cm-1, which may not be sufficient for resolving the

81

spectral contributions of protein and polysaccharide species in membrane fouling

82

processes. If integrated, however, SRS microscopy might become an elegant platform

83

for 3D chemical imaging of membrane fouling. However, SRS imaging has not been

84

applied in membrane studies yet.

disease

diagnoses,

and

drug

discovery

in

complicated

85

Therefore, the main objective of this work was to rapidly differentiate the

86

chemical compositions of membrane foulants and precisely detect their 3D

87

distributions in a label-free manner. A vibrational spectroscopy-based SRS

88

microscopic platform was established for non-invasive membrane fouling imaging.

89

The results can be applied to, but not limited to, membrane fouling study. Bovine

5



90

serum albumin (BSA) and dextran were respectively selected as the model proteins

91

and polysaccharides for membrane fouling tests. The capabilities of the SRS imaging

92

approach for spectroscopic, spatial and temporal characterization of membrane

93

fouling were evaluated. Also, the corresponding membrane fouling mechanisms

94

caused by proteins and polysaccharides were elucidated.

95 96 97

EXPERIMENTAL SECTION

98 99

Reagents and Filtration Procedures. Analytical grade BSA (66 kDa), phycocyanin,

100

dextran (70 kDa), and dextran-Fluorescein (FITC) were purchased from Sigma-

101

Aldrich, USA. BSA and dextran were dissolved in 1X PBS solution (pH 7.4) with

102

different mass ratios (1:0, 5:3, 1:1, 3:5, 0:1), and the total concentration was 1 g/L.

103

The membrane filtration system is illustrated in Figure S1. PVDF microfiltration

104

membranes (GVWP02500) with 0.22 µm-pore sizes and 25 mm-diameters were

105

purchased from Millipore Inc., USA. The effective filtration area was 4.1 cm2. A

106

stirring cell with a volume of 10 mL (Amicon 8200, Millipore Inc., USA) was used

107

for the filtration experiments. The flow rate and stirring speed were kept constant at

108

2.4 mL/min/cm2 and 250 rpm, respectively, during filtration. Prior to filtration, each

109

fresh membrane was primed by filtering with ultrapure water for 20 min. After the

110

filtration, the membrane was rinsed with deionized water and then clamped by two

111

microscopic slides for the subsequent SRS imaging.

112

Raman Microspectroscopy. Spontaneous Raman measurements were conducted

113

using a spectrometer (Shamrock SR-303i-A, Andor Technology, Belfast, UK)

114

mounted to a microscope setup described in previous study.41 the pump laser was

6


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tuned to 707 nm with 100 mW power for Raman excitation. The Raman signal was

116

reflected to the spectrometer by a long-pass dichroic mirror (720DCSP, Chroma Co.,

117

USA). The spectrometer was equipped with a 1200 g/mm grating, and the integration

118

time was 30 s.

119

SRS Imaging Platform for Membrane Fouling Imaging. In the SRS system,

120

two beams at 798 and 1040 nm from a femtosecond laser (Insight Deepsee, Spectra

121

Physics Inc., USA) served as the pump and Stokes lasers for SRS excitation, and their

122

powers before entering the microscope were adjusted to 20 and 80 mW, respectively.

123

For the measurement of retinoic acid, the pump laser was tuned to 893 nm. The

124

Stokes beam was modulated by an acousto-optic modulator (1205C, Isomet Inc., USA)

125

at 2.3 MHz. A motorized optical delay stage (T-LS28E, Zaber Co., USA) was placed

126

in the pump light path to scan the delay between two beams. The pump and Stokes

127

beams were collinearly aligned by a dichroic mirror and chirped by four 12.7-cm long

128

SF57 glass rods, in place of two rods to improve the spectral resolution. The Raman

129

shift was thus controlled by the temporal delay of these two chirped beams. The

130

beams were sent to an upright microscope (IX71, Olympus Inc., Japan) with

131

customized galvo scanner, passing through a spectral focusing unit (SF57, Lattice

132

Optics Inc., USA). A 60X water immersion objective lens (N.A. 1.2, LUMPlanFL,

133

Olympus Inc., Japan) was used to focus the light onto the membrane sample. An oil

134

condenser (N.A. 1.4, U-AAC, Olympus Inc., Japan) was used to collect the signal.

135

The stimulated Raman loss of pump beam was detected by a Si photodiode (S3994-01,

136

Hamamatsu Inc., Japan) with a lab-built resonant circuit and extracted using a digital

137

lock-in amplifier (HF2LI, Zurich Instruments Inc., Switzerland). The Stokes signal

138

was blocked by a filter (825/150 nm, Chroma Technologies Co., USA) before the

139

photodiode.

7



140

The hyperspectral SRS imaging of membrane samples were recorded from

141

approximately 2800 to 3050 cm-1 over 70 frames. The imaging dwell time was 10 µs

142

per pixel, and each image consisted of 200 × 200 pixels with a pixel length of 0.6 µm.

143

Thus, the total acquisition time for each hyperspectral SRS image was approximately

144

1 min. For Z-stack imaging, the depth step was 2 µm; 11 slices were recorded for a

145

total penetration depth of 20 µm from the membrane surface.

146

Imaging Data Processing. The images were denoised and the signal fall-off at

147

the edge of the images was removed by FFT filter. All processes were performed

148

using Image J software (National Institute of Health, USA), and a 3D reconstruction

149

of image stacks was carried out using the volume viewer plugin. The display intensity

150

was adjusted for optimal image contrast for each individual image.

151

The original hyperspectral SRS stacks were decomposed by MCR-ALS analysis

152

to differentiate the membrane substrate and the foulants. The spectral profiles of the

153

components and their concentration matrices were retrieved by a Matlab-based MCR-

154

ALS toolbox. The concentration and spectrum values were set non-negative as

155

constraints, and 0.01% as the convergence criterium, ensuring that small deviations in

156

the initial estimations did not cause inconsistencies within the results. Detailed

157

descriptions about the procedures of MCR-ALS algorithm can be found elsewhere.42

158 159

RESULTS AND DISCUSSION

160 161

Development of SRS Imaging Platform. A schematic illustration of our membrane

162

fouling imaging platform is shown in Figure 1. The frequency difference between the

163

pump and Stokes pulses was centred at 2916 cm-1, i.e., the stretching vibration of C-H

164

bonds, which are prevalent in membrane filtration systems. The system Raman shift

8


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with respect to the pump-Stokes pulse delay was calibrated before SRS imaging by

166

using a set of known Raman peaks of standard chemicals, including dimethyl

167

sulfoxide and methanol, after normalizing with the two-photon absorption signal of

168

Rhodamine 6G (Figure S2). A linear fit between delay stage and Raman shifts

169

indicated that these two laser pulses were linearly chirped. Using a 1951 USAF

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resolution test grid, the spatial resolution of our imaging system was estimated to be

171

0.50 µm in the x direction and 0.57 µm in the y direction (Figure S3).

172

A spontaneous Raman spectrum of standard retinoic acid (RA) is presented in

173

Figure 2a. An isolated RA vibrational resonance was observed at 1580 cm-1 with a

174

9.6 cm-1 full width at half maximum (FWHM). Figure 2b shows the comparison of

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RA SRS spectral profiles collected using the present system with 4 rods and using a

176

previous setup with 2 rods; their FWHMs were 17.2 and 28.5 cm-1, respectively. The

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FWHM of RA in the SRS spectra was wider than that in the spontaneous Raman

178

spectra. This was attributed to a high order chirp for SRS technique, which sacrificed

179

spectral resolution as a tradeoff for a higher sensitivity. The spectral resolution of our

180

modified system was estimated to be 14 cm-1, which was greatly improved compared

181

with the original setup (25 cm-1).43 Such a resolution is sufficient for interrogating the

182

molecular vibrations of chemical species in membrane fouling process.

183

To validate the feasibility of SRS imaging for membrane fouling, the spontaneous

184

Raman and SRS spectral profiles of polyvinylidene fluoride (PVDF) membrane, BSA,

185

and dextran were compared. The spontaneous Raman spectra of these three species

186

(Figure 2c) showed strong signals in the C-H vibration region with distinct

187

differences in both shape and peak position. PVDF polymer exhibited a stretching

188

vibration of -CH2- framework at 2969 cm-1, whereas the C-H bond stretching

189

vibrations of proteins and polysaccharides were located at 2920 and 2885 cm-1,

9



190

respectively. In the SRS spectra (Figure 2d), the peaks were broadened, but the

191

positions remained the same when compared with the spontaneous Raman spectra.

192

Furthermore, the peak shapes were substantially different. These comparisons are the

193

foundation for discriminating different foulants and the membrane substrate in the

194

SRS imaging of membrane fouling.

195

SRS Imaging of BSA/Dextran-Induced Membrane Fouling. Hyperspectral

196

SRS imaging of the pure and BSA/dextran filtrated membranes was carried out. The

197

SRS spectral profiles of the pure PVDF, BSA- and dextran-filtrated membranes at the

198

end of the filtration period (100 min) are shown in Figure 3a. Compared with the

199

pure PVDF membrane, which only exhibited one characteristic peak of PVDF at 2969

200

cm-1, additional peaks appeared at 2910 and 2890 cm-1, for the BSA- and dextran-

201

filtrated membranes, respectively. These results suggest that foulants adsorbed or

202

deposited onto the surface or inside the membranes during filtration.

203

The SRS imaging of the pure PVDF membrane at 2969 cm-1 showed uniformly

204

distributed membrane pores (Figure 3b), indicating the capability for morphological

205

characterization of membrane surfaces by SRS imaging. After BSA filtration, the SRS

206

imaging of BSA foulant and membrane substrate were displayed at 2920 and 2969

207

cm-1 (Figure 3c), respectively. The BSA foulants were thready and unevenly

208

distributed on the membrane, indicating that the foulants aggregated at the membrane-

209

solution interface. The membrane substrate was agglomerated, suggesting the collapse

210

and contraction of membrane pores due to foulant adsorption. In comparison, the

211

dextran foulants exhibited a much weaker intensity and smaller size. Furthermore, the

212

agglomeration of membrane substrate was less severe, as shown in Figure 3d. Since

213

the spectral intensity of the membrane substrate is a function of the local porosity of

214

the membrane, a higher absorbance indicates a lower porosity.31, 33 Therefore, the SRS

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intensities of PVDF membrane at 2969 cm-1 before and after the filtration of

216

BSA/dextran solution (Figure 3c, d, and e) were compared (Table S1). Clearly, after

217

the membrane fouling, the SRS intensity of PVDF increased, indicating the decrease

218

in membrane porosity and the occurrence of pore blockage. Thus, pore constriction

219

was mainly responsible for the membrane fouling during the individual filtration of

220

BSA or dextran. Furthermore, the BSA-fouled PVDF membrane exhibited a much

221

higher intensity than the dextran-fouled membrane, suggesting more severe

222

membrane fouling caused by BSA than by dextran. This result is confirmed by the

223

pure water permeability change before and after the filtration, and the flux declined by

224

90% and 85% after the filtration of BSA and dextran, respectively.

225

To obtain the thickness of the fouling layer, we performed SRS imaging of the

226

fouled membranes along the z-direction to a depth of 20 µm. Cross-sections along the

227

xz plane and 3D reconstructed images of BSA/dextran are shown in Figure 4. The

228

display images of PVDF substrate with moderate intensities along this depth range

229

(Figure S4) demonstrate the capability of this SRS imaging method for depth studies.

230

The BSA and dextran foulants were mainly distributed on the membrane surface

231

within 10 µm, and the BSA foulant exhibited a slightly deeper penetration depth than

232

the dextran foulant. These results suggest that SRS imaging could successfully reveal

233

the 3D spatial distribution of foulants on membranes in a label-free manner.

234

SRS Imaging Coupled with MCR-ALS Analysis of Membrane Fouling

235

Caused by BSA and Dextran Mixture. To explore the ability of SRS imaging for

236

discriminating membrane foulants with different chemical structures, we imaged the

237

membrane fouling caused by a BSA and dextran mixture at the end of the filtration

238

period (100 min). Since the Raman bands of BSA and dextran were close, only two

239

peaks appeared in the SRS spectral profiles of the fouled membrane (Figure 5a). The

11



240

Raman peak at 2969 cm-1 was attributed to the PVDF substrate, whereas the peak

241

centered at 2905 cm-1 was an overlap of BSA and dextran foulants. Lorentz peak

242

fitting results showed three major peaks at 2900, 2910, and 2970 cm-1, corresponding

243

to the C-H Raman bands of dextran, BSA, and the PVDF substrate, respectively. Thus,

244

the identification of foulants was impossible simply based on the direct spectra

245

information. To resolve the overlapping peaks and identify specific chemicals and

246

extract useful structural information, MCR-ALS was applied to analyze the

247

hyperspectral SRS imaging data. The SRS spectral profiles of the pure PVDF, BSA,

248

and dextran were used as the input spectra. After decomposing the original imaging

249

data, three output SRS spectral profiles of the components were generated (Figure

250

5b), which perfectly matched the SRS spectral profiles of the pure PVDF, BSA, and

251

dextran. These results suggest that the spectral information of different foulants and

252

the membrane substrate could be successfully differentiated by coupling hyperspectral

253

SRS imaging with MCR-ALS analysis.

254

The concentration maps of these three components are shown in Figure 5c. The

255

agglomeration of membrane substrate was more considerable and the aggregates of

256

dextran foulants were enlarged when compared with those obtained after the

257

individual filtration of BSA or dextran. These results indicate more severe membrane

258

fouling caused by the filtration of the BSA and dextran mixture than those by their

259

individual filtration. The cross-section views of the BSA and dextran foulants on the

260

membrane along xz-plane and the corresponding 3D reconstructed images of the

261

foulants and membrane substrate are shown in Figure S5. The foulants were primarily

262

distributed on the membrane surface to a 10 µm depth, similar to that observed during

263

the individual filtration of BSA or dextran. The 3D image of the PVDF substrate

264

exhibited no signal at the top of the surface, implying that a thin cake layer

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(approximately 2 µm thick) was formed after the filtration of the BSA and dextran

266

mixture.

267

Influence of BSA/Dextran Mass Ratio on Membrane Fouling. As shown

268

above, the composition of the feed solution had a substantial effect on membrane

269

fouling. To confirm this finding, membrane filtration experiments were conducted at

270

different BSA/dextran mass ratios. Under each ratio, the hyperspectral SRS images of

271

the membranes were collected at three time points, i.e., 30, 60, and 100 min. The SRS

272

intensity ratio of the foulants and membrane substrate, which were located at 2905

273

and 2969 cm-1, respectively, was used to characterize the fouling degree. A higher

274

intensity ratio indicates more foulants formed on the membrane. As shown in Figure

275

5d, the intensity ratio increased with the filtration time, demonstrating the occurrence

276

of fouling during filtration. On the other hand, the intensity ratio of the BSA/dextran

277

mixture filtration was higher than those of the individual BSA or dextran filtrations at

278

all time points, and reached the highest when the BSA/dextran mass ratio was 1:1.

279

This might be because BSA and dextran in the mixture could interact with each other

280

and thereby synergistically resulted in more severe membrane fouling.

281

The above results clearly demonstrate that SRS microscopic imaging can provide

282

very useful information on the evolution of membrane fouling induced by proteins

283

and polysaccharides. Because of an improved spectral resolution, this technique was

284

capable of resolving the chemical structures of protein/polysaccharide foulants from

285

the membrane substrate and locating these foulants inside or on the membrane. The

286

chemical imaging results showed that proteins and polysaccharides tended to adsorb

287

inside the membrane within a 10-µm depth from the membrane surface, occupy pore

288

surfaces and cause pore constrictions in their individual filtration. Furthermore,

289

proteins caused more severe membrane fouling than polysaccharides. A mixture of

13



290

these two species synergistically induced more serious membrane fouling, in which

291

the fouling extent was governed by the mass ratio. Over time, a thin cake layer was

292

formed on the membrane. In general, for the individual protein or polysaccharide

293

filtration, the main fouling mechanism was attributed to pore constriction, whereas

294

pore constriction and cake layer formation were considered as the mechanism

295

responsible for the protein/polysaccharide mixture-induced membrane fouling.

296

To validate the SRS imaging technique with a different and well established

297

technique, we also used a fluorescence confocal microscopy to characterize

298

membrane fouling. Fluorescent phycocyanin was used as the protein foulant, and

299

dextran was labeled with FITC as the polysaccharide foulant. After filtration, the

300

membrane was imaged using a fluorescence confocal microscopy (FV1000, Olympus

301

Co., Japan), and the results are shown in Figure S6. Clearly, both protein and

302

polysaccharide foulants were mainly distributed on the membrane surface within 10

303

µm (Figure S6a, b), and the protein aggregates were much larger than the

304

polysaccharide counterpart (Figure S6c). These results are in consistent with our

305

findings using the SRS imaging method. Since the laser wavelengths (488 nm for

306

polysaccharide and 635 nm for protein) in confocal fluorescence microscopy were

307

shorter than that in the SRS microscopy, the fluorescence confocal microscopy

308

showed a higher sensitivity and better spatial resolution than the SRS microscopy. It

309

should be noted, however, that fluorescence confocal microscopy technique must use

310

fluorescent compounds, and the fouling extent could be influenced by labeling

311

proteins or dextrans. In addition, fluorescence confocal microscopy fails to describe

312

the change of membrane substrates, which could be readily probed by SRS

313

microscopy.

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The development of novel analytical tools for the characterization of membrane

315

fouling evolution is critical for the development of membrane technologies. In most

316

cases, chemical information of the foulants and their distribution cannot be

317

simultaneously obtained during membrane fouling by conventional approaches (e.g.,

318

IR, SEM, and OCT).10,

319

achieve this goal, fluorescent probes are required and affect the fouling process.24, 44

320

In the present work, an ultrafast, highly sensitive, non-invasive and label-free

321

vibrational imaging platform with a sufficient spectral and spatial resolution was

322

established for membrane fouling evolution characterization. Compared with the

323

previously reported analytical methods, this novel approach is able to simultaneously

324

reveal the chemical structure of foulants and their temporal/spatial distributions

325

without labeling (Table S2).

20, 23, 26, 33

While confocal fluorescence microscopy may

326

Implications of This Work. Overall, the present SRS microscopic imaging

327

method successfully discriminated the spectral contributions of BSA and dextran

328

foulants from membrane substrate, and enabled the diagnosis of fouling origin.

329

Furthermore, this method is capable of probing the pore change of membrane

330

substrate and recognizing the fouling tendencies of protein and polysaccharide

331

foulants based on their 3D spatial distribution. Currently, the membrane fouling is

332

measured offline after filtration. By designing the filtration system that solves the

333

membrane immobilization problem in filtration process with referring to other

334

microscopes such as confocal laser scanning microscopy, real-time monitoring of

335

membrane fouling is feasible using such an SRS imaging platform. By selecting the

336

appropriate SRS spectral window, this approach will also have a potential for the

337

vibrational imaging of other membrane fouling processes induced by inorganic and

15



338

organic contaminants and even microorganisms. Thus, this method will also be useful

339

in designing anti-fouling membranes and developing anti-fouling strategies.

340 341 342 343

AUTHOR INFORMATION *Corresponding authors: Prof. Han-Qing Yu, E–mail: [email protected]; Prof. Ji-Xin Cheng, E-mail: [email protected]

344 345

ACKNOWLEDGEMENTS

346

We thank the National Natural Science Foundation of China (51538011) for the

347

support. W.C. acknowledges the financial support from the China Scholarship

348

Council (201506340011) and Shanghai Tongji Gao Tingyao Environmental Science

349

& Technology Development Foundation (STGEF), China.

350 351

ASSOCIATED CONTENT

352

Supporting Information Available. Averages of SRS intensity at 2969 cm-1

353

before and after membrane fouling (Table S1), Comparison between our SRS imaging

354

approach with previous methods for characterizing memebrane fouling process (Table

355

S2), Schematic illustration of membrane filtration system (Figure S1), Calibration of

356

Raman shifts in the spectral focusing system (Figure S2), Spatial resolution estimation

357

of the imaging system (Figure S3), 3D reconstructed SRS imaging of PVDF

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membrane substrate (Figure S4), Cross-section view of foulants on the membrane

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during BSA/dextran mixture (1:1) filtration and the corresponding 3D reconstructed

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SRS images (Figure S5), Cross-section view of protein and polysaccharide foulants

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on the membrane along xz plane and the overlay image along xy plane using

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fluorescence confocal microscopy (Figure S6). This information is available free of

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charge via the Internet at http://pubs.acs.org/.

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Figure captions

Figure 1. Schematic illustration of the SRS setup for membrane fouling imaging. DS: delay stage, OM: optical modulator, DM: dispersion medium, SU: scanning unit, Obj: objective (60X), F: optical filter, PD: photodiode.

Figure 2. Comparison of spontaneous Raman and SRS spectra. (a) Spontaneous spectrum of RA and Lorentz fitting; (b) SRS spectral profiles of RA with 4 rods and with original setup; (c) Spontaneous spectra of PVDF membrane, BSA, and dextran; and (d) their SRS spectral profiles.

Figure 3. SRS imaging of membrane fouling during individual filtration of BSA/dextran. (a) SRS spectral profiles of pure, BSA- and dextran-fouled membranes; (b) SRS imaging of pure membrane at 2969 cm-1; and (c, d) SRS imaging of (c) BSA- and (d) dextran-fouled membrane. SNR: 600.

Figure 4. Cross-section view of the foulants on membrane. Foulants along xz plane during (a) BSA filtration and (b) dextran filtration, and the corresponding 3D reconstructed SRS imaging of BSA (c); and dextran (d) foulants. Size: 120 µm × 120 µm × 20 µm.

Figure 5. Hyperspectral SRS imaging and MCR analysis of the BSA/dextran mixture fouled membrane. (a) SRS spectrum of the fouled membrane and Lorentz peak fitting; (b) Decomposed spectral profiles of components by MCR analysis; (c) Reconstructed concentration images of PVDF (grey), BSA (red), dextran (green), and the overlay image; and (d) Intensity ratio change at 2905/2969 cm-1 during filtration at various BSA/dextran ratios.

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Figure 5

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Table of Contents (TOC) art

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Evolution of Membrane Fouling Revealed by Label-Free Vibrational

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