Mao Wang

selected publications

1. ES&T

   

   Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery

   Environmental Science & Technology 2023

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   Organic solvents are extensively utilized in industries as raw materials, reaction media, and cleaning agents. It is crucial to efficiently recover solvents for environmental protection and sustainable manufacturing. Recently, organic solvent nanofiltration (OSN) has emerged as an energy-efficient membrane technology for solvent recovery; however, current OSN membranes are largely fabricated by trial-and-error methods. In this study, for the first time, we develop a machine learning (ML) approach to design new thin-film composite membranes for solvent recovery. The monomers used in interfacial polymerization, along with membrane, solvent and solute properties, are featurized to train ML models via gradient boosting regression. The ML models demonstrate high accuracy in predicting OSN performance including solvent permeance and solute rejection. Subsequently, 167 new membranes are designed from 40 monomers and their OSN performance is predicted by the ML models for common solvents (methanol, acetone, dimethylformamide, and n-hexane). New top-performing membranes are identified with methanol permeance superior to that of existing membranes. Particularly, nitrogen-containing heterocyclic monomers are found to enhance microporosity and contribute to higher permeance. Finally, one new membrane is experimentally synthesized and tested to validate the ML predictions. Based on the chemical structures of monomers, the ML approach developed here provides a bottom-up strategy toward the rational design of new membranes for high-performance solvent recovery and many other technologically important applications.

2. Small

   

   Designing Nanofluidic Diode from a Hybrid-Bilayer Covalent Organic Framework: Molecular Simulation Investigation

   Wang, Mao, and Jiang, Jianwen

   Small 2023

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   Nanofluidic diodes are potentially useful in many important applications such as sensing, electronics, and energy conversion. However, the manufacturing of controllable nanopores for nanofluidic diodes is technically challenging. Herein, a nanofluidic diode is designed from a highly programmatic covalent organic framework (COF). Through molecular simulation, remarkable diode behavior is observed in a hybrid-bilayer COF but not in its constituent single-layer COFs. The rectification effect of ion current in the hybrid-bilayer COF is attributed to an asymmetric electrostatic potential across the COF nanopore. Furthermore, a synergistic effect of counterion is unraveled in the hybrid-bilayer COF, and the presence of counterion is found to reduce the entry barrier and facilitate ion transport. The performance of the hybrid-bilayer COF as a nanofluidic diode is comprehensively investigated by varying salt concentration, layer number, interlayer spacing, and slipping. This proof-of-concept simulation study demonstrates the feasibility of the hybrid-bilayer COF as a nanofluidic diode and the finding may stimulate the development of new nanofluidic platforms.

3. JMS

   

   Multilayered graphene oxide membranes for bioethanol purification: Microscopic insight from molecular simulation

   Wang, Mao, and Jiang, Jianwen

   Journal of Membrane Science 2022

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   Graphene oxide (GO) membranes have received considerable interest for separation. The channels in GO membranes are in an atomic scale and thus it is challenging to unravel underlying separation mechanism via experiment. In this study, we model multilayered GO membranes with various channel widths (W) and oxidation degrees (O/C), subsequently simulate their performance for bioethanol (an ethanol/water mixture) purification. In the sub-nm membranes with W = 0.75 nm, ethanol-selective permeation occurs at a low O/C but it changes to highly water-selective at a high O/C (with separation factor αH2O/EtOH of 5 and ∞ at O/C = 15% and 35%, respectively). In the wider membranes with W = 1.00 and 1.50 nm, moderate ethanol-selective permeation with αEtOH/H2O of 1–3 is observed. Detailed microscopic analysis reveals that ethanol and water differ in their intercalation and diffusion. In the wider membranes, both ethanol and water exhibit similar intercalation behavior and adopt Fickian diffusion, particularly at a low O/C ratio. In the sub-nm channels, both components feature sub-diffusion but ethanol has negligible diffusion at a high O/C = 35%. Depending on W, ordered structures with different layers are formed for ethanol and water. In the sub-nm channels, hydrogen bonds between water and channel are found to increase in number with increasing O/C ratio but their stability decreases. The microscopic insight provides quantitative understanding of bioethanol purification in GO membranes, and might facilitate the development of new GO membranes for important separation processes.

4. ACS AMI

   

   Accelerating Discovery of High Fractional Free Volume Polymers from a Data-Driven Approach

   Wang, Mao, and Jiang, Jianwen

   ACS Applied Materials & Interfaces 2022

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   As a fundamental structure characteristic in polymers, fractional free volume (FFV) plays an indispensable role in governing polymer properties and performance. However, the design of new high-FFV polymers is challenging. In this study, we report a data-driven approach and aim to accelerate the discovery of high-FFV polymers. First, a computational method is proposed to calculate FFV, and a two-step fragmentation method is developed to construct a fragment library for digital representation of polymer structures. Data mining is employed to identify promising fragments for high FFV. Subsequently, machine learning (ML) models are trained using a data set with 1683 polymers and their excellent transferability is demonstrated by out-of-sample predictions in another data set with 11,479 polymers. Finally, the ML models are used to screen ∼1 million hypothetical polymers, and 29,482 polymers with FFV > 0.2 are shortlisted; representative high-FFV polymers are validated by molecular simulations, and design strategies are highlighted. To further facilitate the discovery of new high-FFV polymers, we develop an online interactive platform https://ffv-prediction.herokuapp.com, which allows for rapid FFV predictions, given polymer structures. The data-driven approach in this study might advance the development of new high-FFV polymers and further explore quantitative structure–property relationships for polymers.

5. ACS AMI

   

   Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

   Wang, Mao, Xu, Qisong, Tang, Hongjian, and Jiang, Jianwen

   ACS Applied Materials & Interfaces 2022

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   Pervaporation (PV) is considered as a robust membrane-based separation technology for liquid mixtures. However, the development of PV membranes is impeded largely by the lack of adequate models capable of reliably predicting the performance of PV membranes. In this study, we collect an experimental data set with a total of 681 data samples including 16 polymers and 6 organic solvents for a wide variety of water/organic mixtures under various operating conditions. Then, two types of machine learning (ML) models are developed for prediction and high-throughput screening of polymer membranes for PV separation. Based on the intrinsic properties of polymer and solvent (water contact angle of polymer and solubility parameter of solvent) as gross descriptors, the first type accurately predicts PV separation performance (total flux and separation factor). The second type is based on the molecular representation of polymer and solvent, giving accuracy comparable to the first type, and applied to screen ∼1 million hypothetical polymers for PV separation of water/ethanol mixtures. With a threshold of 700 for the PV separation index, 20 polymers are shortlisted, with many surpassing experimental samples. Among these, 10 are further identified to be synthesizable in terms of a synthetic complexity score. The ML models developed in this study would facilitate the optimization of operating conditions and accelerate the development of new polymer membranes for high-performance PV separation.

6. ATS

   

   Membrane Fouling: Microscopic Insights into the Effects of Surface Chemistry and Roughness

   Wang, Mao, Wang, John, and Jiang, Jianwen

   Advanced Theory and Simulations 2022

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   Fouling is a major obstacle and challenge in membrane-based separation processes. Caused by the sophisticated interactions between foulant and membrane surface, fouling strongly depends on membrane surface chemistry and roughness. Current studies in the field have been largely focused on polymer membranes. Herein, a molecular dynamics simulation study is reported for fouling on alumina and graphene membrane surfaces during water treatment. For two foulants (sucralose and bisphenol A), it is found that the fouling on alumina surfaces is reduced with increasing surface roughness; however, the fouling on graphene surfaces is enhanced by roughness. It is unravelled that the foulant–surface interaction becomes weaker in the ridge region of a rough alumina surface, thus allowing foulant to leave the surface and reducing fouling. Nevertheless, such behavior is not observed on a rough graphene surface because of strong foulant–graphene interaction. In addition, with increasing roughness, the hydrogen bonds between water and alumina surface are found to increase in number as well as stability. This simulation study reveals that surface chemistry and roughness play a crucial role in membrane fouling, and the microscopic insights are useful for the design of new membranes toward high-performance water treatment.

7. Nanoscale

   

   A coupled effect of dehydration and electrostatic interactions on selective ion transport through charged nanochannels

   Wang, Mao, Shen, Wenhao, Ding, Siyuan, Wang, Xue, Wang, Zhong, Wang, Yugang, and Liu, Feng

   Nanoscale 2018

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   Selective ion transport is an essential feature of biological ion channels. Due to the subnanometer size and negatively charged surface of ion channels, the ion selectivity is affected by both dehydration effects and electrostatic interactions. Their coupled effect on selective ion transport, however, has been elusive. Here, using molecular dynamics simulations, we study ion (Li+ and Mg2+) transport through subnanometer carbon nanotubes (CNTs) with varying charge densities. Our results indicate that the dehydration effect governs the ionic transport at low surface charge densities, hence the nanochannel shows a selectivity for Li+ ions. In contrast, the nanochannel switches to a selectivity for Mg2+ ions as the electrostatic interaction between the cations and the negatively charged wall dominates the transport at high surface charge densities.

8. NC

   

   Ultrafast ion sieving using nanoporous polymeric membranes

   Wang, Pengfei*,  Wang, Mao*, Liu, Feng, Ding, Siyuan, Wang, Xue, Du, Guanghua, Liu, Jie, Apel, Pavel, Kluth, Patrick, Trautmann, Christina, and Wang, Yugang

   Nature Communications 2018

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   Nanoporous membranes show great potential for ionic separations, but the typical trade-off between permeability and selectivity hinders their applicability. Here the authors fabricate nanoporous polymeric membranes with a high density of 0.5 nm pores and demonstrate their exceptional performance for ion sieving.