Facile pore size tuning and characterization of nanoporous ceramic membranes for the purification of polysaccharide

Facile pore size tuning and characterization of nanoporous ceramic membranes for the purification of polysaccharide

Journal Pre-proof Facile pore size tuning and characterization of nanoporous ceramic membranes for the purification of polysaccharide Xianfu Chen, Tin...

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Journal Pre-proof Facile pore size tuning and characterization of nanoporous ceramic membranes for the purification of polysaccharide Xianfu Chen, Ting Qi, Yun Zhang, Tao Wang, Minghui Qiu, Zhaoliang Cui, Yiqun Fan PII:

S0376-7388(19)32738-3

DOI:

https://doi.org/10.1016/j.memsci.2019.117631

Reference:

MEMSCI 117631

To appear in:

Journal of Membrane Science

Received Date: 31 August 2019 Revised Date:

30 October 2019

Accepted Date: 1 November 2019

Please cite this article as: X. Chen, T. Qi, Y. Zhang, T. Wang, M. Qiu, Z. Cui, Y. Fan, Facile pore size tuning and characterization of nanoporous ceramic membranes for the purification of polysaccharide, Journal of Membrane Science (2019), doi: https://doi.org/10.1016/j.memsci.2019.117631. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Graphical abstract

1

Facile pore size tuning and characterization of nanoporous ceramic

2

membranes for the purification of polysaccharide

3

Xianfu Chen1, Ting Qi1, Yun Zhang1, Tao Wang2, Minghui Qiu1, Zhaoliang Cui1, Yiqun Fan1*

4

1. State Key Laboratory of Materials-Oriented Chemical Engineering, College of Chemical

5

Engineering, Nanjing Tech University, Nanjing, 210009, China

6

2. State Key Laboratory of Food Science and Technology, National Engineering Laboratory for

7

Cereal Fermentation Technology, Jiangsu Provincial Research Center for Bioactive Product

8

Processing Technology, School of Food Science and Technology, Jiangnan University, Wuxi, 214122,

9

China

10 11

*Corresponding author: Prof. Yiqun Fan

E-mail: [email protected]

Abstract

12

Developing nanoporous ceramic membranes with tunable pore structure offers a new paradigm

13

to the precise separation and purification of functional polysaccharides. Here, a facile in-situ

14

chemical deposition method was applied to obtain nanoporous ZrO2 membranes with controllable

15

pore size. A modified rejection curve method based on Poiseuille flow, Ferry’s equation, and

16

log-normal distribution was proposed to calculate pore size distribution of membrane. The results

17

showed that with the increase of precursor content from 0 to 20 wt.%, the geometric mean diameter

18

of ceramic membranes decreased from 5.9 nm to 3.1 nm, corresponding to a significant decrease of

19

molecular weight cut-off from 15.0 kDa to 3.3 kDa. Still, the pure water permeability was

20

maintained at a high level of about 42 L·m-2·h-1·bar-1. Meanwhile, the geometric standard deviation

21

decreased from 1.20 to 1.16, indicating a narrower pore size distribution. The obtained nanoporous

22

ZrO2 membrane performed a high rejection of dextran, while the fructose can be effectively and

23

efficiently separated with a separation factor as high as 11.5, therefore enlarging our choices

24

removing fructose from dextran.

25

Keywords: ceramic membrane; in-situ chemical deposition; pore size tuning; log-normal

26

distribution 1

27

1. Introduction

28

Functional polysaccharides derived from plants, fungus and algaes have various health-related

29

benefits stemming from their immuno-stimulating and anti-tumor properties [1, 2]. In their

30

separation and purification process, membranes are attracting incrementing attention due to high

31

efficiency, low cost, as well as the advantages in continuous operation [3, 4]. However, the

32

mismatches of the molecular size and the distribution of polysaccharides exert rigorous requirements

33

on the pore size and its distribution of membranes. Generally, the smaller the molecular weight of the

34

polysaccharide for separation, the smaller the pore size, with narrow distributions, of the membrane

35

is required.

36

Ceramic membranes are representative materials featuring high permeability, selectivity,

37

antifouling, and long-term stability, and are demonstrating to be excellent candidates for separation

38

of organic matters [5, 6]. For molecular-level separation practices entailing great contributions from

39

the nanoporous structure, the sol-gel method is widely used in the fabrication of ceramic membranes

40

[7]. Generally, the pore structure can be adjusted by changing the particle size of the sol and the

41

thermal treatment process [8]. Besides, some post-modification treatments including chemical vapor

42

deposition (CVD) [9], atomic layer deposition (ALD) [10], surface grafting [11] as well as the in-situ

43

chemical deposition [12] are also effective methods to tune the nanoporous structure of ceramic

44

membranes.

45

Guiding the preparation and the post-modification of membrane materials with an established

46

separation and purification performance necessitates obtaining pore size and distribution parameters

47

with sufficient accuracy. To this end, various methods have been established for determining

48

membrane pore size, including: nitrogen adsorption-desorption analysis [13-16], mercury intrusion

49

porosimetry [17, 18], micromorphology [19, 20], permeation flux analysis [21, 22], gas-liquid

50

exclusion method [23, 24], liquid-liquid exclusion method [25, 26], permeation porometry [27, 28],

51

and solute retention method [29-31]. Among the different protocols, the solute retention method has

52

been widely used and recognized as a facile and non-destructive approach for nanoporous membrane

53

materials. To characterize the pore size distribution, various studies on transport theories and

54

distribution functions have been carried out to establish the relationship between the size and the

2

55

rejection of applied solute [32-36]. Generally, Poiseuille flow, steric interaction between solute

56

molecules and pores, and log-normal distribution are extensively scrutinized in order to ascertain the

57

pore size distribution with considerable reliability, enabling the assessment of the geometric mean

58

diameter and the geometric standard deviation, the two parameters of the pore size distribution. Ren

59

et al. [37] applied an Extreme model to describe the relationship between these two parameters.

60

Therefore, the pore size distribution can be obtained by only two points, and the parameter solving

61

process is significantly simplified.

62

In this work, an in-situ chemical deposition technique was applied for the fabrication of

63

nanoporous ZrO2 membranes by using zirconium n-propoxide and normal propyl alcohol as the

64

precursor and the solvent, respectively. The resulting ZrO2 membranes were characterized by XRD,

65

XPS, SEM, and TEM. In addition, the mathematical relationship between the curve parameters of

66

pore size distribution and solute rejection was established. A modified rejection curve method was

67

proposed for the parameter solving process, and the changes in the pore size and its distribution were

68

then systematically studied based on the retention of dextran.

69

2. Theory

70

2.1 The pore size distribution of ceramic membranes

71 72 73

The most common form of the two-parameter log-normal model was used to describe the pore size distribution of ceramic membranes, as shown in Eq. 1. ϕ (d ) =

 1 ln( d / D * ) 2  1 exp  − ( )  ln(σ ) d ln(σ ) 2π  2 

(1)

74

where the σ and D* are the geometric standard deviation and the geometric mean diameter,

75

respectively. According to the natural characters of log-normal function, the two parameters σ and D*

76

have relationships as following [38]:

77 78

D* =d50

σ=

d84.13 d50

(2) (3)

79

where d84.13 and d50 are the pore size when the integration of φ(d) equals to 84.13% and 50%,

80

respectively. The relationship between these two parameters σ and D* was also described by Ren et al. 3

81

[37] with an Extreme model:

σ −1 D* ( − e− z − z +1) , = Ae z= , σ ∈ [1, ∞ ) ω a

82

(4)

83

where the regression parameters A and ω can be described in relationships with the rejection. The

84

regression parameters A and ω are 1.296 and 0.299, respectively, when the rejection equals to 90%.

85

The corresponding molecular weight is termed as the MWCO (molecular weight cut off) of the tested

86

membrane. The molecular size at MWCO is expressed as DMWCO.

87

2.2 Rejection curves of ceramic membranes

88

According to the Ferry’s equation [39], the rejection R(d) of the solute by the pore channel with

89

a size of d can be described in the form of Eq. 5 assuming that the interaction between solute

90

molecules of different sizes is negligible in the transport of flow in pore channels. 2  a a   (2 − )  , d > a R(d ) =   d d  1, d ≤ a 

91

92

where a is the diameter of the dextran molecule calculated from the Eq. 6 [40].

a = 0.066M w0.46

93 94 95 96

(6)

where Mw is the molecular weight of the dextran molecule. Considering the pore size distribution of ceramic membranes, the rejection R(a) of the solute with a molecular size of a can be expressed by Eq. 7 [37].

∫ R( a) =



0

d 4 R(d )ϕ (d )dd





0

d 4ϕ (d )dd

 1 1  =1 − 1 + erf  2   2 ln σ

  1  a  +2  *  exp  −6(ln σ ) 2  1 + erf  D   2 ln σ  2

97

(5)

  D*     2 ln + 4(ln ) σ        a    

  D*     2 σ ln + 2(ln )        a    

  1   D*      a   15 2 2 −2  *  exp  − (ln σ )  1 + erf  + (ln σ )  ln     D   2    2 ln σ   a     3

(7)

4   1  D*    1 a  +  *  exp  −8(ln σ ) 2  1 + erf  ln    2 D   2 ln σ  a    

98 99

To simplify the expression, the rejection curve can also be described by the log-normal model [38], as shown in Eq. 8. 4

100

R(a ) =

 ln(a / Da* )  1 1 + erf   2 2  2 ln σ a 

(8)

101

where the two parameters σa and Da* are the geometric standard deviation and the geometric mean

102

diameter, respectively. These two parameters have the following relationships:

Da* = a50

103

σa =

104

(9)

a84.13 a50

(10)

105

where a84.13 and a50 are the molecular sizes when the R(a) equals to 84.13% and 50%, respectively.

106 107

2.3 The relationship between pore size distribution parameters (D*, σ) and rejection curve parameters (Da*, σa)

108

The MWCO partially reflects the information of the rejection curve. To make full use of the

109

information from the rejection curve, the pore size distribution function was usually under scrutiny.

110

The relationship between the pore size distribution parameters (D*, σ) and the rejection curve

111

parameters (Da*, σa) was therefore studied. By combining Eqs. 2-3, Eq. 5, and Eqs. 9-10, the

112

following relationships can be obtained:

113 114 115 116 117 118 119

σ a a84.13 / d84.13 1 − 1 − 0.8413 0.6864 = = = * * Da* / D* Da* / D* Da / D σ

(11)

When the R(a)=90%, the Eq. 4 can be written as below: D

*

DMWCO

= 1.296e

 1−σ 1−σ   − e 0.299 + +1   0.299   

=T (σ )

(12)

where DMWCO can also be described as a function of parameters (Da*, σa) according to Eq. 8: DMWCO = P ( Da* , σ a )

(13)

Therefore, the relationship between pore size distribution parameters (D*, σ) and rejection curve parameters (Da*, σa) can be obtained from Eqs. 11-13:

120

D * = U ( Da* , σ a )

(14)

121

σ = H ( Da* , σ a )

(15)

5

122

3. Experimental Section

123

3.1 Preparation of membranes

124

The in-situ chemical deposition process was carried out according to the previous fabrication

125

method of titania nanoporous membrane [12]. Here, zirconium n-propoxide (ZNP, Sigma Aldrich,

126

70%) and n-propanol (NPA, Lingfeng Shanghai, AR) were used as the precursor and solvent,

127

respectively. Before chemical deposition, tubular α-Al2O3 supports with an asymmetric structure

128

were placed in an oven at 110 °C for 10 h to remove adsorbed water. These applied supports had an

129

inner diameter of 8 mm, an outer diameter of 12 mm and a length of 110 mm, and contained an

130

α-Al2O3 ultrafiltration layer with an average pore size of about 5 nm on the inside wall. The treated

131

membranes were then immersed in the precursor solution of a certain concentration for 12 h.

132

Subsequently, the impregnated membranes were air-dried at room temperature for 12 h and then

133

dried in an oven at 110 °C for another 12 h. Finally, nanoporous ceramic membranes were obtained

134

after the dried supports were sintered at 350-700 °C in a furnace for 2 h with a heating rate of

135

2 °C/min. All of the chemical reagents applied in this work were used as received without any

136

further purification.

137

3.2 Characterization

138

The crystalline phases of ZrO2 materials calcined at different temperatures were identified using

139

an X-ray diffraction (XRD) instrument (Smart Lab, Rigaku, Japan) equipped with a Cu-Kα radiation

140

(λ=0.154 nm) source operated at 40 kV and 40 mA. The scans were performed over a 2θ range from

141

20° to 80°. The chemical states of zirconium and aluminum elements were detected by XPS

142

(JPS-9200, JEOL, Japan) using Al Kα radiation. TEM (JEM-1011, JEOL, Japan) was performed to

143

examine the crystallite size and the lattice plane d-spacing.

144

A home-made cross-flow filtration apparatus [41] was used for measuring the permeability of

145

pure water and retention properties of the ZrO2 membranes. For the test of water permeability, the

146

transmembrane pressure was set in the range of 0.2-0.5 MPa, and the operating temperature was

147

controlled at about 25 °C. For the test of retention, dextran was applied as a testing solute at a

148

transmembrane pressure of about 0.5 MPa and a temperature of about 25 °C. The dextran solution

149

was a mixture of different dextran with various molecular weight (5 kDa, 10 kDa, 40 kDa, and 70 6

150

kDa), and the corresponding contents were 2 g·L-1, 2.5 g·L-1, 1 g·L-1, and 1 g·L-1, respectively. The

151

solute contents in both the feed and permeate were measured by using a gel permeation

152

chromatography (GPC, 1515, Waters, USA).

153

4. Results and Discussion

154

4.1 Characterization of ZrO2 membrane materials

155

The effects of the thermal treating temperature were first studied on the composition and phase

156

evolution of ZrO2 membrane materials. The ZrO2 powder was obtained by air-drying the precursor

157

solution without loaded on the membrane substrate. Before conducting the TG-DSC and XRD, the

158

ZrO2 powder was further dried in an oven at 110 °C for 6 h. The TG-DSC profiles showed a rapid

159

loss in weight was observed before 220 °C, followed by a continual loss in weight with the

160

temperature further increased to 455 °C (Figure 1a). The total loss in weight was about 5.4% due to

161

the dehydration of hydrous zirconia. Subsequently, the weight loss ceased regardless of further

162

temperature increases. Meanwhile, an obvious exothermic peak appeared at about 450 °C resultant

163

from the phase transition of ZrO2 powder from amorphous to tetragonal phase.

164

The XRD patterns of ZrO2 powder at different temperature were shown in Figure 1b,

165

demonstrating that, before 400 °C, ZrO2 was present in an amorphous phase, whereas characteristic

166

peaks of the tetragonal phase were observed as the temperature increased to 450 °C. This agreed well

167

with the above results of DSC analysis. The tetragonal peak became stronger due to the growth of

168

ZrO2 grains as a result of forwarding heating from 450 °C to 700 °C. To completely remove the

169

bounded water and obtain crystallized ZrO2 material, the thermal treating temperature was supposed

170

to be above 450 °C. However, the high temperature might affect the nanoporous structure of ZrO2

171

membranes. Considering this trade-off effect, a suitable thermal treating temperature at 450 °C was

172

therefore applied.

7

173 174

Figure 1. (a) TG-DSC curves of ZrO2 powder; (b) XRD patterns of ZrO2 powder sintered at different temperatures.

175

When the precursor content was 20wt.%, the XRD spectra of ceramic membranes before and

176

after the treatment of in-situ chemical deposition was studied (Figure 2a). There was no change in the

177

crystalline structure of these two samples, yet the intensity significantly declined after the deposition.

178

This phenomenon can be ascribed to the fact that the pores of Al2O3 may be filled with ZrO2 particles

179

[42]. However, no diffractive peak of ZrO2 was detected in the XRD patterns after the in-situ

180

chemical deposition. The result suggested that there was no additional membrane layer formed on the

181

membrane surface and the deposited ZrO2 nanoparticles were very small and well-dispersed in Al2O3,

182

being below the detection limit of XRD technique [43].

183

To verify that the ZrO2 particles had been successfully deposited in the nanopores of the

184

substrate, XPS and TEM were performed. The composition and the chemical structure of ZrO2

185

membrane were investigated by XPS as shown in Figure 2b. A new peak of Zr3d binding energy was

186

identified at about 183 eV after the in-situ chemical deposition, indicating that ZrO2 can successfully

187

deposit on the Al2O3 membrane. Additionally, there is a 0.25 eV shift of Al2p peak from 74.23 eV to

188

73.98 eV after the in-situ chemical deposition (Figure 2c). This was likely due to a bonding process 8

189

between Zr and Al, in which Zr tended to donate an electron and Al gain an electron [44]. Then, the

190

electron transfer from Zr4+ to Al3+ when Zr4+ incorporated into the Al2O3 lattice, which led to an

191

enrichment of electron charge density around Al3+. Consequently, more electron donation to the Al–

192

O bond took place than that from Zr to O [45], which lowered the binding energy. Conversely, the

193

photoelectron peak of Zr3d5/2 was centered at 182.7 eV (Figure 2d,), which was larger than that of

194

pure ZrO2 powder obtained at 450 oC (182.0 eV). The XPS data mentioned above, in addition,

195

indicated that there might be an aluminate phase generated between Al2O3 and ZrO2, which bound

196

the ZrO2 nanoparticles tightly to the inner wall of membrane pores.

197 198

Figure 2. XRD patterns (a) and XPS spectra of samples: survey (b), Al2p (c) and Zr3d (d).

199

The lattice diffraction fringe of ZrO2-Al2O3 material was studied by TEM, showing a plane

200

distance of about 0.182 nm (Figure 3c), a typical tetragonal zirconia pattern (t-ZrO2, [200] = 0.182

201

nm). Mapping images were studied as well to show the existence and dispersion of the zirconium

202

element as illustrated in Figure 3d-g, which demonstrated that the zirconium element was well

203

dispersed in the ZrO2-Al2O3 sample.

9

204 205 206

Figure 3. TEM and EDX mapping of ZrO2-Al2O3 materials.

4.2 Pore size tuning of nanoporous ceramic membranes

207

The effect of precursor content on the performance of the ceramic membrane was investigated

208

as shown in Figure 4. NPA and ZNP were applied as the solvent and precursor, respectively. The

209

pristine membrane performed an MWCO of about 15 kDa with a pure water permeability of about 54

210

L·m-2·h-1·bar-1. With the increase of the precursor content, the permeability of pure water remained at

211

a high level (>40 L·m-2·h-1·bar-1), although was reduced to some extent. When the precursor content

212

was 20wt.%, the pure water permeability was reduced to 42 L·m-2·h-1·bar-1, which was only 22%

213

smaller than that of the pristine membrane. By contrast, the MWCO of the membranes decreased

214

significantly with the increase of the precursor content. When the precursor content was above

215

10wt.%, the MWCO of obtained membranes was below 5 kDa, which was reduced to about 3.3 kDa

216

that was smaller than a quarter of that of the pristine membrane when the precursor content was

217

20wt.%. The corresponding molecular size of dextran was about 2.7 nm. These results were

218

attributed to the diminishing of vacancies of the pore channels in response to the concentrating

219

precursors. Consequently, the pore size decreased, while the mass transfer resistance increased.

220

However, there was a sharp contrast for the variances between permeability and MWCO, probably

221

due to the precursors migrating toward the surface of the membrane during the evaporation of the

222

solvent [12]. As a result, the surface pores with significantly decreased pore size were obtained.

223

While the average pore size of the entire membrane did not seem to present a remarkable decrease.

10

224

Therefore, while obtaining increased separation accuracy, the mass transfer resistance was still

225

maintained at an acceptable level. 20.0k

50

22%

16.0k

40 12.0k 78%

227

8.0k

20 4.0k

10 0

226

Permeability MWCO

30

MWCO/Da

-2

-1

Permeability/L⋅m ⋅h ⋅bar

-1

60

0

5

10

15

20

0.0

ZNP content/wt.%

Figure 4. Effects of precursor content on the performance of nanoporous membrane.

228

The effects of precursor content on the surface and cross-sectional morphology of the

229

nanoporous ceramic membrane were presented in Figure 5. Upon a precursor concentration of 20

230

wt.%, no discernable pore structure of the ceramic membrane surface was found by the FESEM.

231

Meanwhile, a clear demarcation between the membrane layer and the transition layer was presented

232

in the cross-sectional images. The cross-sectional morphology of the ceramic membranes did not

233

change substantially without a noticeable new layer structure on the surface, even when the precursor

234

content was lifted up to 20 wt.%. This suggested that the in-situ chemical deposition method mainly

235

improves the separation accuracy of the membrane material via pore size adjustment, rather than the

236

formation of a new membrane layer on the surface of the membrane material.

237 238

Figure 5. Effect of precursor content on the microstructure of ceramic membranes.

11

239

4.3 The relationship between parameters (D*, σ) and parameters (Da*, σa)

240

According to Eqs. 13-15, the effects of parameters (Da*, σa) on DMWCO, D* and σ were obtained

241

as shown in Figure 6a-c. Both DMWCO and D* increased with increasing Da* and σa. It was interesting

242

to find that σ and D*/DMWCO were almost constant with the increasing Da*, yet showed to change with

243

increasing σa in the studied range from 1.4 to 5.2. The relationships between D*/DMWCO and σ with σa

244

was shown in Figure 6d. Their polynomial fitting functions were written as below:

245

σ = 0.51157 + 0.77836σ a − 0.28450σ a2 + 0.04921σ a3 − 0.00321σ a4 ,

246

D* / DMWCO = 2.26172 − 1.31803σ a + 0.46666σ a2 − 0.07884σ a3 + 0.00506σ a4 ,

r 2 = 0.99897 r 2 = 0.99937

(16) (17)

247 248 249

Figure 6. The effects of parameters (Da*, σa) on DMWCO (a), D* (b), σ (c), and the relationship of D*/DMWCO

250

The reason why D*/DMWCO and σ were only the functions of σa was analyzed. According to Eq.

251

and σ with σa (d).

8, DMWCO/Da* can be expressed as a function of σa when the R(a) equals to 90%.

DMWCO = M (σ a ) Da*

252

(18)

253

By substituting Eq. 12 and Eq. 18 into Eq. 11, the relationship between σa and σ can be given as Eq.

254

19.

12

255

256 257

σ a 0.6864 D D* = * * =0.6864 MWCO σ Da / D Da* DMWCO = 0.6864M (σ a )T (σ ) The above equation can also be expressed as Eq. 20 and Eq. 21:

σ T (σ )=

262

0.6864M (σ a )

(20)

(21)

Therefore, the relationship between σ and σa can be further written as:

σ =S −1 [ N (σ a )]

260 261

σa

S (σ )=N (σ a )

258 259

(19)

(22)

By substituting Eq. 22 into Eq. 12, the following relationship can be obtained: D* DMWCO

= T {S −1 [ N (σ a ) ]}

(23)

263

The expressions of Eq. 22 and Eq. 23 can explain the reason why σ and D*/DMWCO were almost

264

constant with the increasing Da*, yet showed to change with increasing σa.

265

4.4 The pore size distribution of nanoporous ZrO2 membranes

266

To obtain the pore size distribution of nanoporous ceramic membranes before and after the

267

in-situ chemical deposition treatment, the rejection curves of these membranes were fitted by using

268

Eq. 8 as shown in Figure 7. The pristine membrane and the membranes treated with precursor

269

contents of 1wt.%, 5 wt.%, 10 wt.%, 15 wt.% and 20 wt.% were noted as M1-M6 in that order. The

270

rejection curves of M1-M6 can be well fitted by using the log-normal function. Their r-squared

271

values were all larger than 0.99 and close to 1. The regression parameters σa and Da* as well as

272

DMWCO were listed in Table 1. Eq. 16 and Eq. 17 were then applied to resolve the pore size

273

distribution parameters σ and D* from the regression parameters σa and DMWCO. The resolving

274

process was noted as the modified rejection curve (MRC) method.

275

The calculated pore size distribution parameters σ and D* by MRC method were listed in Table

276

1. Both σ and D* decreased in the order of M1-M6, indicating that the mean pore size of nanoporous

277

ceramic membranes decreased, and that the pore size distribution became narrower with the increase

278

of precursor content. By substituting the σ and D* of Eq. 7 with those obtained from M1-M6, the 13

279

predicted rejection curves were also presented in Figure 6, in good agreement with the experimental

280

results. It suggested that the MRC method was effective in the resolving of pore size distribution

281

parameters σ and D*. 1.0

M1

M3

M2

0.9

DMW CO=5.528nm

DMW CO=4.896nm

0.8

DMW CO=3.877nm

0.7 Experimental Fitting Predicting

0.6

Rejection

0.5 0.4

r2fitting

0.3 0.2 1.0

r

DMWCO=3.417nm

0.7

r2fitting r 2

r

=0.99970

r2fitting

Experimental Fitting Predicting

r2fitting

=0.99724

2 predicting

r

=0.99944 8

DMW CO=2.755nm

Experimental Fitting Predicting

=0.99778

6

10

12

=0.99947

M6

DMWCO=3.043nm

2 predicting

4

=0.99714

2 predicting

2 predicting

Experimental Fitting Predicting

0.6

r2fitting

=0.99613

M5

0.8

282

r

=0.99988

predicting

M4

0.9

0.5

r2fitting

=0.99673

2

Experimental Fitting Predicting

Experimental Fitting Predicting

2

4

=0.99764

6

8

10

r 12

2

4

=0.99200

2 predicting

6

=0.99138 8

10

12

Molecular size/nm

283

Figure 7. The dextran retention curves.

284

Additionally, the pore size distribution parameters σ and D* were also calculated by the

285

two-point method proposed by Ren et al. [37] The obtained pore size distribution parameters σ and

286

D* for M1-M6 were listed in Table 1, where the σ and D* were close to those obtained by the MRC

287

method. Considering that only two points in the rejection curve were taken into account in the

288

two-point method, the selection of these two points might affect the resolving σ and D* especially

289

when fluctuations occurred in the rejection curve. While all the data points in the rejection curve

290

were considered by the MRC method so as to make the obtained parameters σ and D* more

291

representative for the description of membrane pore size distribution.

292

According to the pore size distribution parameters, σ and D* obtained by MRC method, the mean

293

diameter Dm, the most probable in pore size distribution Dp, the average diameter by pore area Da

294

and the average diameter by pore flux Df were calculated by the expression in Table S1. The results

295

were shown in Table 1. For each membrane, there was a relationship of different pore size 14

296

descriptions: Dp < D* < Dm < Da < Df. The pore size distribution curves of M1-M6 were presented in

297

Figure 8, where the pore area distribution function φa(d) and pore flux distribution function φf(d)

298

were expressed as Eq. S3 and Eq. S4. It can be seen that the pore size of nanoporous ceramic

299

membranes decreased and the pore size distribution became narrower with the increasing precursor

300

content.

301

Table 1. The parameters of rejection and pore size distribution curves for M1-M6 membranes.

302

Membrane

Da *

σa

DMWCO

M1

3.101

1.552

5.528

M2

2.864

1.497

M3

2.322

M4

Two-point method D

*

MRC method

σ

D

5.851

1.212

4.896

5.266

1.472

3.877

2.085

1.451

M5

1.906

M6

1.792

*

σ

Dm

Dp

Da

Df

5.942

1.200

6.041

5.748

6.142

6.349

1.205

5.363

1.188

5.443

5.206

5.525

5.691

4.302

1.185

4.284

1.183

4.345

4.165

4.407

4.533

3.417

3.881

1.169

3.804

1.178

3.856

3.703

3.908

4.014

1.427

3.043

3.520

1.157

3.417

1.173

3.461

3.332

3.505

3.595

1.389

2.755

3.198

1.155

3.138

1.164

3.174

3.066

3.211

3.285

The units of Da*, DMWCO, D*, Dm, Dp, Da, and Df were in nm. 0.8

M1

ϕ(d) ϕa(d)

M2

ϕ(d) ϕa(d)

ϕf(d)

0.6

M3

ϕ(d) ϕa(d)

ϕf(d)

ϕf(d)

Intensity/a.u.

0.4

0.2

0.0 0.8

M4

ϕ(d) ϕa(d)

M5

ϕ(d) ϕa(d)

ϕ(d) ϕa(d)

ϕf(d)

ϕf(d)

0.6

M6

ϕf(d)

0.4

0.2

0.0

303 304 305

2

4

6

8

10

12

2

4

6

8

10

12

2

4

6

8

10

12

Pore size/nm

Figure 8. The membrane pore distribution of ZrO2 membranes.

4.5 Application of nanoporous ZrO2 membranes

306

Dextran is a glucose polymer of the sucrose molecule, which is fermented by intestinal

307

membrane Leuconostoc whereby the glucose units were dehydrated into a polymer of glucose [46]. 15

308

Dextran is highly hydrophilic without antigen or coagulation effect, and its viscosity, together with

309

the osmotic pressure, is similar to that of blood. Meanwhile, it can be degraded but can maintain a

310

certain time in the body, which is recognized as an excellent replacement of plasma worldwide, and

311

is under extensive clinical uses [47]. The nanoporous ceramic membranes (Figure 9a) as prepared

312

were applied to remove byproduct fructose (180 Da) from dextran by using a homemade apparatus

313

(Figure 9b).

314 315 316

Figure 9. (a) schematic diagram of the purification process; (b) dextran rejection curves of the membranes before and after deposition; (c) stability of the ZrO2 membrane for dextran separation.

317

The schematic diagram of the purification process was presented in Figure 9c. In the modeled

318

system, dextran with an average molecular weight of about 5 kDa, and an identical concentration of

319

dextran and fructose of 60 g·L-1 were used. The modeled solution was fed from the inside of tubular

320

membrane by a plunger pump. The trans-membrane pressure was controlled at about 0.5 MPa by a

321

counterbalance valve. The byproduct fructose was removed from the permeate side. With the

322

continuous remove of fructose, purified dextran products could be obtained from the feed side. The

323

separation factor of the dextran and the fructose was studied to evaluate the efficiency of membrane

324

process. It was defined as shown in Eq. 24 [48].

16

αi j =

325

Yi Xi

Yj X

(24) j

326

where Yi, Yj, Xi, Xj are the mass fractions of fructose (i) and dextran (j) in the permeate (Y) and feed

327

(X), respectively. The concentration of fructose and dextran was measured by GPC. The GPC curves

328

of the feed and the permeates from M1 and M6 were analyzed as shown in Figure 9b. The retention

329

of dextran was significantly improved after the in-situ chemical deposition, while the fructose could

330

freely pass the M6 membrane. The separation factor was improved significantly to 11.5 for M6

331

compared to 1.5 for M1. In the continuous separation for 12 h (Figure 9c), the M6 membrane

332

obtained a separation factor that was stable at about 11.5, which at the meantime performed a high

333

flux above 22 L·m-2·h-1. The results indicated the excellent performance of the ceramic nanoporous

334

membrane being good candidates applied in the purification and separation of polysaccharides.

335

5. Conclusions

336

In summary, a facile pore size tuning method was achieved in the fabrication of nanoporous

337

ZrO2 membrane via an in-situ chemical deposition. The effects of thermal temperature and the

338

precursor on the performance of the ceramic membrane were investigated. The precursor content was

339

proved to play a vital role in pore size tuning. The relationship between the curve parameters of pore

340

size distribution and solute rejection was established. A modified rejection curve method based on

341

Poiseuille flow, Ferry’s equation, and log-normal distribution was proposed as an effective and

342

efficient approach for resolving the pore size distribution parameters. Upon a precursor concentration

343

of 20wt.% and a thermal temperature of 450 °C, the geometric mean diameter of ZrO2 membrane

344

decreased to a value as low as 3.1 nm. Meanwhile, with the increasing precursor content from 0 to 20

345

wt.%, the pore size distribution tended to be narrower, and the geometric standard deviation

346

simultaneously decreased from 1.20 to 1.16. The ZrO2 membrane as-prepared was successfully

347

applied to remove byproduct fructose from dextran. The separation factor was effectively improved

348

to 11.5, compared to 1.5 for the pristine membrane, suggesting a great prospect of the membrane

349

regarding the separation and purification of polysaccharides.

350

Acknowledgments

351

This study was financially supported by the National Key Research and Development Program 17

352

of China (2016YFC0205700, 2017YFC0403702), the National Natural Science Foundation of China

353

(21706115), the National High Technical Research Program of China (2012AA03A606), and the

354

Program for Changjiang Scholars and Innovative Research Team in University (IRT13070).

355

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22

Highlights 1. Nanoporous ZrO2 membrane was obtained via an in-situ chemical deposition process. 2. A modified rejection curve method was proposed to calculate pore size distribution. 3. The nanoporous membrane was applied to remove byproduct fructose from dextran.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: