Analysis of pH-dependent Structure and Mass Transfer Characteristics of Polydopamine Membranes by Molecular Dynamics Simulation

    Analysis of pH-dependent Structure and Mass Transfer Characteristics of Polydopamine Membranes by Molecular Dynamics Simulation Fusheng Pan, Ruisi Xing, Zhongyi Jiang PII: DOI: Reference:

S1004-9541(14)00121-9 doi: 10.1016/j.cjche.2014.09.014 CJCHE 75

To appear in: Received date: Revised date: Accepted date:

14 December 2013 2 April 2014 9 April 2014

Please cite this article as: Fusheng Pan, Ruisi Xing, Zhongyi Jiang, Analysis of pHdependent Structure and Mass Transfer Characteristics of Polydopamine Membranes by Molecular Dynamics Simulation, (2014), doi: 10.1016/j.cjche.2014.09.014

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

ACCEPTED MANUSCRIPT

T

Manuscript ID: 2013-0572 中文题目：pH 对聚多巴胺膜结构和传质特性影响的分子动力 学模拟分析

IP

Graphic abstract

The molecular simulation results reveal that water-accessible free volume voids in the

SC R

gaps among multilayer structures stacked by dopamine oligomers tend to connect each other and form water channels, providing pathway for the rapid diffusion of water

AC

CE P

TE

D

MA

NU

molecules.

1

ACCEPTED MANUSCRIPT Separation Science and Engineering

Ruisi Xing（邢瑞思）1,2,

1

Zhongyi Jiang（姜忠义）1,2,**

SC R

Fusheng Pan（潘福生）1,2,

IP

T

Analysis of pH-dependent Structure and Mass Transfer Characteristics of Polydopamine Membranes by Molecular Dynamics Simulation*

Key Laboratory for Green Chemical Technology of Ministry of Education, School of Chemical

Engineering and Technology, Tianjin University, Tianjin 300072, China 2

NU

Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072,

China

MA

Article history: Received 14 December 2013

Received in revised form 2 April 2014 Accepted 9 April 2014

TE

D

Available online xxxx

CE P

Abstract Detailed atomistic structures are constructed for polydopamine membranes containing different amount of catechol and quinone groups to investigate the effect of pH value in the membrane casting solution on sorption and diffusion of small gas molecules (water and propylene) in

AC

the membranes. Interactions between dopamine oligomers are calculated, and it is found that the -1

interactions decrease from -2356.52 kJ mol-1 in DOP-1 to -1586.69 kJ·mol in DOP-3 when all of the catechol groups are converted to quinone groups. The mobility of polymer segments and free volume properties of polydopamine membranes are analyzed. The sorption quantities of water and propylene in the membrane are calculated using Grand Canonical Monte Carlo method. The sorption results show that water adsorbed in DOP-1, DOP-2 and DOP-3 are 17.3, 18.6 and 20.0 mg water per g polymer, respectively, and no propylene molecule can be adsorbed. The diffusion behavior of water molecules in the membrane is investigated by molecular dynamics simulation. The diffusion coefficients of water molecules in DOP-1, DOP-2 and DOP-3 membranes are (1.80±0.52)×10-11, (3.40±0.64)×10-11 and (4.50±0.92)×10-11 m2·s-1, respectively. The predicted sorption quantities and

*Supported by the National Science Fund for Distinguished Young Scholars (21125627), the National Natural Science Foundation of China (21306131), Specialized Research Fund for the Doctoral Program of Higher Education (20120032120009), Seed Foundation of Tianjin University, and the Programme of Introducing Talents of Discipline to Universities (No: B06006). ** Corresponding author. E-mail address: [email protected]（Z. Jiang） 2

ACCEPTED MANUSCRIPT diffusion coefficients of water and propylene in the membrane present the same trends as those from experimental results.

IP

T

Keywords Membranes, Polydopamine, Molecular dynamics simulation, Free volume, Diffusion

1 Introduciton

SC R

Inspired by the strong adhesion ability of mussel onto various surfaces, dopamine, a commercially available chemical containing catechol and amine groups (origins of the extraordinarily strong adhesion [1, 2]), has been widely used as surface coating

NU

agent and immobilized initiator to graft functional groups for surface modification [3-6]. Since it can adhere firmly onto the substrate and self-polymerize rapidly to form

MA

a dense, ultrathin film, dopamine presents intrinsic advantage to be utilized as the dense active layer in composite membranes for separation. Composite membranes with polydopamine active layer (about 14 nm) on porous polysulfone (PS) hollow fiber

D

were fabricated and exhibited superior separation performance in the dehumidification of propylene gas [7]. Composite membranes with a thin polydopamine layer (< 100 nm)

TE

on the porous PS substrate were utilized for pervaporative desulfurization and exhibited satisfactory separation performance [8]. A polydopamine layer about 50 nm

CE P

was coated onto Nafion membranes as the methanol barrier in direct methanol fuel cell [9]. The methanol crossover of the modified membranes was dramatically suppressed by 79% from 3.14 × 10−6 to about 0.65 × 10−6 cm2s−1, while the proton conductivity

AC

was decreased slightly. As a dense membrane, the separation performance relies heavily on the inter-chains interaction and the microstructure. Catechol groups play an important role in the redox reactions in the

self-polymerization of dopamine [1, 3, 10]. They are easily oxidized to quinone and can react with each other in an oxidative process under basic conditions. And quinone groups exhibit lower adhension interaction than parent catechols [11]. In the polymerization of dopamine, pH regulation is usually employed to manipulate its structure and performance of polydopamine [12], since pH of the dopamine solution can alter the equilibrium between catechol and quinone groups. At higher pH, more catechol groups of dopamine are deprotonated and oxidized to quinone groups [1]. The intermolecular

interaction,

microstructure

and

separation

performance

of

polydopamine are subsequently changed. However, influences of pH on the structure and performance of polydopamine are poorly understood.

3

ACCEPTED MANUSCRIPT It is rather difficult to characterize the structural and transport properties of polydopamine membranes by experimental method because of its ultra-thin thickness. Generally accepted alternative methods to characterize the homogenous membranes do

T

not work either since polydopamine cannot form a free-standing film. Molecular

IP

simulation has been proved as a useful tool for addressing membrane material and transport phenomena in separation processes at molecular level [13-17]. Herein,

SC R

molecular simulation is used to describe how pH influences the structure and performance of polydopamine. The models for polydopamine containing different amounts of catechol and quinone groups are constructed to simulate the membranes

NU

fabricated at different pH values. The interaction between dopamine oligomers within the polydopamine and its influences on the segment mobility and free volume

MA

properties are examined. The sorption positions and amount of water/propylene are calculated by Grand Canonical Monte Carlo (GCMC) method. The diffusion of water molecules in the membranes is investigated by molecular dynamics (MD) simulation.

TE

the simulation approach.

D

The simulation results are compared with experimental results to check the validity of

2 Details of the simulation

CE P

2.1 Model construction

Due to the complexity of the polymerization process, the structure of polydopamine is subject to debate at present. However, it is well accepted that

AC

5,6-dihydroxylindole is one of the main intermediate component in dopamine polymerization [10, 18-20]. In this study, based on Stark et al’s study [21], dopamine oligomer is simplified as a homopolymer comprised of 5,6-dihydroxyindole and its oxidation form 5,6-indole quinone in the redox polymerization. The oligomer, as the basic structural units, consists of 5 dopamine molecules and exhibits a platelet with lateral extents of 1.5-2.0 nm. Three models of dopamine oligomer with different amount of catechol groups and quinone groups are developed as shown in Fig. 1(a, b, c). Oxygen-containing functional groups on DOP-1 and DOP-3 are catechol groups and quinone groups, respectively, while those on DOP-2 are composed of 50% catechol groups and 50% quinone groups. It should be point out that the models only reflect the influence tendency of pH on the structure of polydopamine, since the content of catechol and quinone groups at different pH values is unclear.

4

(b)

(c)

MA

NU

SC R

(a)

IP

T

ACCEPTED MANUSCRIPT

(d)

(e)

(f)

D

Figure 1 Atomistic models of polydopamine with different ratio of catechol and quinone forms (a) oligomer of DOP-1, with functional groups of catechol groups; (b) oligomer of DOP-2,

TE

with functional groups of 50% catechol groups and 50 % quinone groups; (c) oligomer of DOP-3, with functional groups of quinone groups; (d) DOP-1, amorphous cells of (a); (e)

CE P

DOP-2, amorphous cells of (b); (f) DOP-3, amorphous cells of (c)

A packing model, with an initial density of 1.68 gcm-3 [22] and containing 20 oligomers, is constructed by amorphous cell module using the combination of an

AC

algorithm developed by Theodorou and Suter [23] and the scanning method of Meirovitch [24]. Periodic boundary conditions are applied to the cubic simulation box.

2.2 Structure optimization and MD simulation procedure MD simulations are carried out using the Discover and Amorphous Cell module of Materials Studio, developed by Accelrys Software Inc. The COMPASS (condensed-phase optimization molecular potentials for atomistic simulation studies) force filed is used in this study. Non-bond cutoff distance of 0.950 nm (with a spline width of 0.100 nm and a buffer width of 0.050 nm) is employed to evaluate the non-bond interaction. Long-tail corrections to the energy due to the non-bond cutoff are employed in the dynamics simulation. The temperature and pressure are both controlled by the Berendsen method [25] with decay constant of 0.1 ps. The equations of motion are integrated with a time step of 1 fs for all dynamic runs. An optimization procedure is applied to the initially constructed atomistic 5

ACCEPTED MANUSCRIPT structure as our previous work [26, 27]. A 2000-step energy minimization is performed first to eliminate the undesirable contact (overlapping or close contact). The annealing method is applied to overcome local energy minima and yield equilibrated

T

configurations with a global energy minimum. In this method, the models are heated up

IP

step-wise from 300K to 600K at intervals of 50K, and then cooled to 300K at intervals of 20K. At each step a 250-ps NPT dynamics is applied to the cell. Afterwards, a 200

SC R

ps NPT (T = 300K, P = 1.01×105 Pa) dynamics is performed to obtain the equilibrium density. An additional 500 ps NVT (T = 300 K) dynamics is performed on the endpoint of the NPT dynamics to obtain equilibrium molecular structures, and the atomic

MA

3 Results and discussion

NU

trajectory is recorded every picosecond for the subsequent analysis.

3.1 Morphology of the optimized polydopamine models The morphologies of polydopamine after optimization are shown in Fig. 1(d, e, f).

D

The basic structural unit of polydopamine is a fragment of dopamine oligomers (like a fragment of a graphite sheet), or several fragments stacked together with ~0.34 nm

TE

spacing. These basic structural units range between several nanometers and exhibit a multilayer structure. Polydopamine is based on a random packing of these multilayer

CE P

structures. The optimized structure of polydopamine is consistent with experimental results [28].

3.2 Interaction between dopamine oligomers

AC

The interaction energy (ΔE) between dopamine oligomers are calculated by

E  Ebulk  nEoligomer

(1)

where Ebulk is the potential energy of polydopamine bulk system, n is the number of dopamine oligomers, and Eoligomer is the potential energy of single oligomer. ΔE values between dopamine oligomers in DOP-1, DOP-2 and DOP-3 are -2356.52, -2301.76, and -1586.69 kJ·mol-1, respectively. High amount of polar groups (catechol groups, quinone groups, amine groups) on dopamine oligomers could form hydrogen bonds and polar group interaction with other oligomers, while π-electron conjugated structure in the dopamine oligomer will form strong π-π interaction among oligomers. Such strong π-π interactions drive oligomers to stack together and exhibit a multilayer structure as graphite. The interaction energies in polydopamine follow the order of DOP-1 > DOP-2 > DOP-3. The decrease of interaction energy is resulted from the increase content of

6

ACCEPTED MANUSCRIPT quinone groups. The simulated results show that quinone-quinone interaction (-57.74 kJ·mol-1) is weaker than catechol-quinone interaction (-76.36 kJ·mol-1) and catechol-catechol interaction (-99.23 kJ·mol-1). The result is consistent with the

T

single-molecule measurements by atomic force microscopy [11].

IP

Oxidation degree of catechol residues to corresponding quinones determines the bulk cohesiveness and the relative amount of catechol and quinone groups. At higher

SC R

pH value, more quinone groups by oxidized catechols endow polydopamine with higher cohesiveness. Meanwhile, the weaker interaction energy of quinones than parent catechols lowers interfacial adhesion ability [29]. A balance between interfacial

NU

adhesion and bulk cohesiveness should be maintained by finding the degree of overall oxidation that affords an optimal mixture of catechol and quinone groups.

MA

3.3 Segment mobility of polydopamine

Segment mobility of the polymer controls the dynamic properties (generation and disappearance) of free volume voids within the membranes, and constitutes a key issue

D

for transport properties [30, 31]. Segment mobility is investigated by examining the mean-square displacement (MSD) of polymer segment as a function of time.

TE

r (t ) 2  ri (t )  ri (0) 

2

(2)

CE P

where ri(t) and ri(0) are the position of atom i at time t and 0, respectively, and the bracket denotes the ensemble average, which is obtained from averaging all atoms and all time origins t = 0.

AC

Fig. 2 shows the MSDs of polydopamine in the membranes. Larger slope of MSD curve reflects higher segment mobility. In the studied system, the change of segment mobility depends on the interaction between dopamine oligomers. In DOP-1, the high interaction among catechol groups reduces the segment mobility of surrounding polymers. With the increasing content of quinone groups, the segment mobility is enhanced since the weaker attractive interaction decreases the confinement effects among these oligomers.

7

0.0040 0.0035

DOP-2

0.0030 0.0025 0.0020

IP

DOP-3

0.0015 0.0010 0

100

T

DOP-1

SC R

mean square displacement / nm

2

ACCEPTED MANUSCRIPT

200

300

400

500

time / ps

NU

Figure 2 Mean square displacement of polymer segments in polydopamine membranes

3.4 Free volume properties of the polydopamine membranes

MA

Free volume plays a crucial role in diffusion behavior of penetrant molecules in membranes. Positron annihilation spectroscopy (PAS) is commonly employed as a direct approach to probe free volume properties. In the experimental study, PAS

D

analysis only indicates that the polydopamine membranes possess a looser structure

TE

when fabricated at higher pH values. To investigate how pH value affects the microstructure, the fractional free volume (FFV), size distribution of free volume voids

CE P

and free volume morphology are analyzed. Free volume properties of polydopamine are analyzed by Connolly surface method [26, 27]. Fig. 3(a) shows the FFV of polydopamine membranes using molecular probes, with diameter ranging from 0.2 to 8

AC

angstrom. The FFV probed by water and propylene, modeled by spheres with diameter 0.26 and 0.47 nm, respectively, are shown in Table 1. The FFV of the membranes follows the order of DOP-3 > DOP-2 > DOP-1, which indicates that higher pH induces a looser structure. The simulated results present the same tendency as the experimental PAS results [7]. 30

DOP-3

(a)

2.0

DOP-2

intensity

fractional free volume / %

2.5

20

10

0 0.0

DOP-1

0.2

1.5 1.0

H2O

C3H6

(b)

DOP-1 DOP-2 DOP-3

0.5 0.4 0.6 diameter / nm

0.8

0.0 0.0

0.2 0.4 0.6 cavity diameter / nm

0.8

Figure 3 Fractional free volume (a) and cavity size distribution (b) of the polydopamine membranes

8

ACCEPTED MANUSCRIPT

Table 1 The FFV of polydopamine membranes obtained from geometrical analysis by water and

FFVH2O

FFVC3H6

DOP-1

6.35

0.45

DOP-2

12.42

DOP-3

15.05

T

Membranes

IP

propylene

SC R

3.06 3.37

NU

The FFV analysis indicates the overall free space in the membranes, but it can not reflect the actual free volume voids through which the penetrant molecules pass. To

MA

solve such problem, size distribution of free volume voids is calculated. The volume of free volume voids (FV) between rΔr and r+Δr is obtained by FV(d) = FV(dΔd)  FV(d+Δd)

(3)

D

where FV(dΔd) and FV(d+Δd) are the free volumes that is accessible for probes with

TE

diameter dΔd and d+Δd, respectively. The interval of probe diameter, Δd, is set to 0.005 nm in this study.

CE P

The size distribution of free volume voids in polydopamine membranes is shown in Fig. 3(b). The diameter of free volume voids mostly is around 0.13 nm. The size distributions of Dop-2 and Dop-3 shift toward the right-hand side in comparison to that of Dop-1. With the increase of pH in the fabrication process, smaller void region

AC

reduces and larger void region increases. The variation indicates that the polydopamine changes from tight to loose, which is more beneficial for transport of penetrant molecules. From Fig. 3(b), it can be observed that the intensity of void size larger than 0.45 nm approaches zero in DOP-1, while DOP-2 and DOP-3 provide a moderate intensity. Fig. 4 shows morphology maps of free volume voids accessible to water and propylene. Free volume voids are mainly created by inefficient packing or transient gaps among dopamine oligomers. The increase of FFV with pH is ascribed to the increasing amount and larger size of free volume voids by the weaker interactions among dopamine oligomers. The dispersion of water-accessible free volume voids in polydopamine is heterogeneous. The voids are much denser in the gaps among the multilayer structures than those in the multilayer structures. In the dense dispersion area, the free volume voids interconnect with neighboring ones and form water

9

ACCEPTED MANUSCRIPT channels, especially in DOP-2 and DOP-3. Such water channels would provide a convenient pathway for diffusion of water molecules. On the contrary, much less propylene-accessible free volume voids are present in the polydopamine, always with

NU

SC R

IP

T

longer distance.

DOP-2 (a)

DOP-3 (a)

TE

D

MA

DOP-1 (a)

CE P

DOP-1 (b)

DOP-2 (b)

DOP-3 (b)

Figure 4 Morphology of free volume voids (bright region) accessible to water (a) and propylene

.

AC

(b) in polydopamine membranes

3.5 Sorption behavior of water/propylene in the membranes Sorption of water and propylene in polydopamine membranes is investigated by

the GCMC method implemented in the Sorption module. The method of Metropolis et al. [32] is employed for accepting or rejecting configurational moves (rotation and translation of sorbate molecules) as well as for sorbate insertion and deletion, in which the trial configurations are generated without bias and the adsorbate structure is treated as rigid. A total of 10000000 steps are used. The GCMC simulation is conducted at a fixed pressure of 350 kPa. The partial pressure of water and propylene is set as the experimental value (water mass content 0.5%). In the fixed pressure simulation, the configurations are sampled from a grand canonical ensemble, in which the fugacities of all components, as well as the temperature, are fixed as if the framework is in open contact with an infinite sorbate 10

ACCEPTED MANUSCRIPT reservoir with a fixed temperature. The GCMC calculations are therefore carried out over the equilibrated configurational snapshot of the membrane. The amount of water adsorbed in DOP-1, DOP-2 and DOP-3 are 17.3, 18.6 and

T

20.0 mg water per gram polymer, respectively. According to the simulation results,

IP

with the increase content of quinone groups in the membrane, the water uptake increases slightly. The sorption of water in the polymeric membranes is mainly

SC R

governed by the polymer-penetrant interaction and adsorbed positions within the membranes. With the increasing content of quinone groups, the polydopamine-water interaction is lower, since quinone-water interaction (-58.16 kJ·mol-1) is lower than

NU

catechol-water interaction (-76.93 kJ·mol-1). Meanwhile, the larger FFV originated from the increasing content of quinone groups induces more adsorbed positions (as

MA

shown in Fig. 5). Such two factors with opposite effects change the water sorption slightly. The calculated sorption amount of propylene in polydopamine is zero for all models of polydopamine membranes. Low polydopamine-propylene interaction and

D

big size of propylene make it difficult to be adsorbed. The calculated results prove the ultra high sorption selectivity of polydopmine membranes towards water vapor over

AC

CE P

TE

propylene as in the experiment [7].

DOP-1

DOP-2

DOP-3

Figure 5 Adsorbed position of water molecules in polydopamine membranes (atoms of polydopamine not shown)

3.6 Diffusion behavior of water/propylene in the membranes The diffusion coefficient D of water in equilibrated models of membranes is calculated from the slope of the MSD for long time by Einstein relation:

1 d Na 2 D  lim   ri (t )  ri (0)  t  6 dt i 1

(3)

In the simulation, a certain amount penetrant molecules (according to the GCMC simulation) are inserted into the membrane models. The models are equilibrated using the same procedure as mentioned in Section 2.2. The diffusion runs are performed 11

ACCEPTED MANUSCRIPT under the NVE conditions for 5 ns. The diffusion coefficient is an averaged value from all penetrant molecules. Due to the limitation of short calculation time, it is rather difficult to obtain exact value of diffusion coefficient. Therefore, this study simply

T

offers the changing trends of diffusivity in different polydopamine membranes.

IP

The simulated diffusion coefficients of water molecules in DOP-1, DOP-2 and DOP-3 polydopamine membranes are (1.80±0.52)×10-11, (3.40±0.64)×10-11, and

SC R

(4.50±0.92)×10-11 m2/s, respectively. The diffusion coefficient of water increases with the pH value in the fabrication process. The main factors determining the diffusion of water in the membranes are free volume properties, polymer segment mobility and

NU

polymer-water interaction. With the increase of pH, free volume voids are enlarged, the polymer segment mobility increases, and the polymer-water interaction decreases. All

MA

these factors enhance the diffusion of water.

The spatial distribution of water in polydopamine membranes (shown in Fig. 6) indicates that water molecules are not uniformly distributed within the membranes.

D

Most water molecules are gathered in water channels (gaps among multilayer structures) and quite less locate between dopamine oligomers in the multilayer

TE

structure. Diffusion coefficients of these two kinds of water molecules in the last 100 ps of the diffusion run are calculated. The results display that diffusion coefficient of

CE P

water molecules in the water channel (5.60×10-11 m2/s) is much larger than that between oligomers (1.67×10−12 m2/s). Plenty of water channels with high water

AC

permeability endow polydopamine membranes with high water vapor permeance.

DOP-1

DOP-2

DOP-3

Figure 6 Snapshots of the spatial distribution of water in polydopamine membranes (atoms of polydopamine not shown)

Although the size distribution of free volume in the membranes indicates that some free volume voids in DOP-2 and DOP-3 are larger than that of propylene, the diffusion behavior of propylene molecules cannot be achieved until a new free volume void generated near the free volume voids is occupied by propylene molecules, since 12

ACCEPTED MANUSCRIPT the propylene-accessible free volume voids are always with large distance. The generation of the free volume voids accessible to propylene is rather difficult because of the low segment mobility of polydopamine. Hence, these polydopamine membranes

T

fabricated at different pH value possess high diffusion selectivity. The high diffusion

IP

selectivity and adsorption selectivity endow polydopamine membranes with high

SC R

separation factors for water vapor over propylene.

4 Conclusions

A combination of GCMC and MD simulation methods is employed to investigate

NU

the structure properties and sorption-diffusion behavior of water/propylene in polydopamine membranes fabricated at different pH values in membrane casting

MA

solution. Higher content of quinone groups at higher pH value induces weaker interaction among dopamine oligomers, subsequently leading to higher polymer segment mobility and larger FFV. The morphology and distribution of free volume

D

voids accessible to water and propylene are shown explicitly. Water channels are formed by interconnected free volume voids, especially in the gaps among the

TE

multilayer structures stacked by the dopamine oligomers. Such channels endow the polydopamine with high water vapor permeability. Moreover, appropriate size

CE P

distribution of free volume voids and low polymer segment mobility inhibit the penetration of propylene and ensure high separation factors. The calculated sorption and diffusion coefficients of water molecules in the polydopamine membranes exhibit

AC

the same trends as those from the experimental investigations. It proves that molecular simulation is a useful tool to probe the structure at atomistic level and dynamic properties and transportation behavior in pico-second level. The visible structure of membranes and transportation process of penetrant molecules in the simulation are helpful to understand the transportation behavior of penetrant molecules in the membranes.

Acknowledgements Gratitude is also expressed to R & D Center for Petrochemical Technology, and Advanced Instrumental Detecting & Analytical Center, School of Chemical Engineering and Technology, Tianjin University, for providing access to the Material Studio molecular modeling software.

References 1 Lee, H., Dellatore, S.M., Miller, W.M., Messersmith, P.B., “Mussel-inspired surface chemistry for 13

ACCEPTED MANUSCRIPT multifunctional coatings”, Science, 318 (5849), 426-430 (2007). 2 Lee, H., Lee, B.P., Messersmith, P.B., “A reversible wet/dry adhesive inspired by mussels and geckos”, Nature, 448 (7151), 338-342 (2007). 3 Sedó, J., Saiz-Poseu, J., Busqué, F., Ruiz-Molina, D., “Catechol-based biomimetic functional materials”, Adv. Mate., 25 (5), 653-701 (2013).

T

4 Xu, L.Q., Yang, W.J., Neoh, K.G., Kang, E.T., Fu, G.D., “Dopamine-induced reduction and

IP

functionalization of graphene oxide nanosheets”, Macromolecules, 43 (20), 8336–8339 (2010). 5 Yang, S.H., Kang, S.M., Lee, K.B., Chung, T.D., Lee, H., Choi, I.S., “Mussel-inspired

SC R

encapsulation and functionalization of individual yeast cells”, J. Am. Chem. Soc., 133 (9), 2795–2797 (2011).

6 Li, C.Y., Wang, W.C., Xu, F.J., Zhang, L.Q., Yang, W.T., “Preparation of pH-sensitive membranes via dopamine-initiated atom transfer radical polymerization”, J. Membr. Sci., 367 (1–2), 7–13

NU

(2011).

7 Pan, F.S., Jia, H.P., Qiao, S.Z., Jiang, Z.Y., Wang, J.T., Wang, B.Y., Zhong, Y.R., “Bioinspired fabrication of high performance composite membranes with ultrathin defect-free skin layer”, J.

MA

Membr. Sci., 341 (1–2), 279-285 (2009).

8 Li, B., Liu, W.P., Jiang, Z.Y., Dong, X., Wang, B.Y., Zhong, Y.R., “Ultrathin and stable active layer of dense composite membrane enabled by poly(dopamine)”, Langmuir, 25 (13), 7368–7374 (2009).

D

9 Wang, J.T., Xiao, L.L., Zhao, Y.N., Wu, H., Jiang, Z.Y., Hou, W.Q., “A facile surface modification of Nafion membrane by the formation of self-polymerized dopamine nano-layer to enhance the

TE

methanol barrier property”, J. Power Sour., 192 (2), 336-342 (2009). 10 Dreyer, D.R., Miller, D.J., Freeman, B.D., Paul, D.R., Bielawski, C.W., “Elucidating the structure

CE P

of poly(dopamine)”, Langmuir, 28 (15), 6428−6435 (2012). 11 Lee, H., Scherer, N.F., Messersmith, P.B., “Single-molecule mechanics of mussel adhesion”, Proc. Natl. Acad. Sci., 103 (35), 12999−13003 (2006). 12 Barrett, D.G., Fullenkamp, D.E., He, L.H., Holten-Andersen, N., Lee, K.Y.C., Messersmith, P.B., “pH-based regulation of hydrogel mechanical properties through mussel-inspired chemistry and

AC

processing”, Adv. Funct. Mater., 23 (9), 1111–1119 (2013).

13 Zhao, J., Ma, J., Chen, J., Pan, F.S., Jiang, Z.Y., “Experimental and molecular simulation investigations on interfacial characteristics of gelatin/polyacrylonitrile composite pervaporation membrane”, Chem. Eng. J., 178, 1–7 (2011).

14 Jing, Y., Wei, L., Wang, Y.D., Yua, Y.X., “Molecular simulation of MCM-41: Structural properties and adsorption of CO2, N2 and flue gas”, Chem. Eng. J., 220, 264–275 (2013). 15 Hofmann, D., Heuchel, M., Yampolskii, Y., Khotimskii, V., Shantarovich, V., “Free volume distributions in ultrahigh and lower free volume polymers: comparison between molecular modeling and positron lifetime studies”, Macromolecules, 35 (6), 2129–2140 (2002). 16 Sun, D.L., Zhou, J., Molecular Simulation of Oxygen Sorption and Diffusion in the Poly (lactic acid), Chin. J. Chem. Eng., 21(3), 301-309(2013). 17 Yang, Q.Y., Xu, Q., Liu, B., Zhong, C.L., Smit, B., Molecular Simulation of CO 2/H2 Mixture Separation in Metal-organic Frameworks:Effect of Catenation and Electrostatic Interactions, Chin. J. Chem. Eng., 17(5), 781-790(2009). 18 Double, K.L., Zecca, L., Costi, P., Mauer, M., Griesinger, C., Ito, S., Ben-Shachar, D., Bringmann, G., Fariello, R.G., Riederer, P., Gerlach, M., “Structural characteristics of human substantia nigra neuromelanin and synthetic dopamine melanins”, J. Neurochem., 75 (6), 2583–2589 (2000). 14

ACCEPTED MANUSCRIPT 19 Pezzella, A., Panzella, L., Natangelo, A., Arzillo, M., Napolitano, A., d’Ischia, M., “5,6-Dihydroxyindole tetramers with “Anomalous” interunit bonding patterns by oxidative coupling of 5,5,6,6-tetrahydroxy-2,7-biindolyl: emerging complexities on theway toward an improved model of eumelanin buildup”, J. Org. Chem., 72 (24), 9225–9230 (2007). 20 Waite, J.H., “Mussel power”, Nat. Mater., 7 (1), 8–9 (2008).

T

21 Stark, K.B., Gallas, J.M., Zajac, G.W., Golab, J.T., Gidanian, S., McIntire, T. P., Farmer, J., “Effect

IP

of stacking and redox state on optical absorption spectra of melanins-comparison of theoretical and experimental results”, J. Phys. Chem. B, 109 (5), 1970–1977 (2005).

SC R

22 Davidson, R.L., Handbook of water-soluble gums and resins, Mac. Graw-Hill, New York, 1980. 23 Heodorou, T.D.N., Suter, U.W., “Detailed molecular structure of a vinyl polymer”, Macromolecules, 18, 1467–1478 (1985).

24 Meirovitch, H., “Computer simulation of self-avoiding walks: testing the scanning”, J. Chem.

NU

Phys., 79 (1), 502–508 (1983).

25 Berendsen, H.J.C., Postma, J.P.M., Funsteren, W.F., “Molecular dynamics with coupling to an external bath”, J. Chem. Phys., 81 (8), 3684–3690 (1984).

MA

26 Pan, F.S., Peng, F.B., Jiang, Z.Y., “Diffusion behavior of benzene/cyclohexane molecules in poly (vinyl alcohol)-graphite hybrid membranes by molecular dynamics simulation”, Chem. Eng. Sci., 62 (3), 703–710.

27 Pan F.S., Ma, J., Cui, L., Jiang, Z.Y., Zhang, M.H., “Water vapor/propylene sorption and diffusion

D

behavior in PVA-P(AA-AMPS) blend membranes by GCMC and MD simulation”, Chem. Eng. Sci., 64 (24), 5192–5197 (2009).

TE

28 Gallas, J.M., Littrell, K.C., Seifert, S., Zajac, G.W., Thiyagarajan, P., “Solution structure of copper ion-induced molecular aggregates of tyrosine melanin”, Biophy. J., 77 (2), 1135–1142 (1999).

CE P

29 Yu, M., Deming, T.J., “Synthetic polypeptide mimics of marine adhesives”, Macromolecules, 31 (15), 4739–4745 (1998).

30 Karayiannis, N.C., Mavrantzas, V.G., Theodorou, D.N., “Molecular simulation of permeation of small penetrants through membranes. 1. Diffusion coefficients”, Macromolecules, 27 (16), 4498–4508 (1994).

AC

31 Nagel, C., Schmidtke, E., Gunther-Schade, K., Hofmann, D., Fritsch, D., Strunskus, T., “Free volume distributions in glassy polymer membranes: comparison between molecular modeling and experiments”, Macromolecules, 33 (6), 2242–2248 (2000).

32 Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., “Equation of state calculations by fast computing machines”, J. Chem. Phys., 21, 1087–1092 (1953).

15