publications

2025

2024

2023

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

   

   Ultralow Ru Single Atoms Confined in Cerium Oxide Nanoglues for Highly-Sensitive and Robust H2O2-Related Biocatalytic Diagnosis

   Small 2023

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   Exploring highly efficient, portable, and robust biocatalysts is a great challenge in colorimetric biosensors. To overcome the challenging states in creating single-atom biocatalysts, such as insufficient activity and stability, here, this work has engineered a unique CeO2 support as nanoglue to tightly anchor the Ru single-atom sites (CeO2-Ru) with strong electronic coupling for achieving highly sensitive and robust H2O2-related biocatalytic diagnosis. The morphology and chemical/electronic structure analysis demonstrates that the Ru atoms are well-dispersed on CeO2 surface to form high-density active sites. Benefiting from the unique structure, the prepared CeO2-Ru exhibits outstanding peroxidase (POD) like catalytic activity and selectivity to H2O2. Steady-state kinetic study results show that the CeO2-Ru presents the highest Vmax and turnover number than the state-of-the-art POD-like biocatalysts. Consequently, the CeO2-Ru discloses a high efficiency, good selectivity, and robust stability in the colorimetric detection of L-cysteine, glucose, and uric acid. Notably, the limit of detection (LOD) can reach 0.176 × 10−3 m for the L-cysteine, 0.095 × 10−3 m for the glucose, and 0.088 × 10−3 m for the uric acid via cascade reaction. This work suggests that the proposed unique CeO2 nanoglue will offer a new path to create single-atom noble metal biocatalysts and take a step closer to future biotherapeutic and biocatalytic applications.

3. ACS SCE

   

   Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

   ACS Sustainable Chem. Eng. 2023

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   Atmospheric water harvesting based on metal–organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify top-performing MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure–property relationships are analyzed in terms of pore structure and framework chemistry, providing task-specific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 “Computation-Ready, Experimental” (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1–0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6–0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications.

4. 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.

2022

1. 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.

2. JMS

   

   Microscopic insight into anion conduction in covalent−organic framework membranes: A molecular simulation study

   Lyu, Bohui,  Wang, Mao, Jiang, Zhongyi, and Jiang, Jianwen

   Journal of Membrane Science 2022

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   Recently there has been considerable interest in the utilization of covalent-organic frameworks (COFs) as ionexchange membranes. While rapid ion conduction is experimentally observed in COF membranes, the under­ lying mechanism remains elusive. Herein, we report a molecular simulation study on chloride ion (Cl− ) con­ duction in four COF membranes functionalized with quaternary ammonium groups (QA-2, QA-4, QA-6 and QAEO) of different side chains (ethyl, butyl, hexyl and diethyl ether). It is revealed that membrane flexibility is crucial to be incorporated in simulation for reliable predictions. The Cl− conductivities in the four membranes are predicted to decrease as COF-QA-2 > COF-QA-4 ≈ COF-QA-EO > COF-QA-6, which is in good agreement with experimental data. The pore size, rather than membrane-Cl− interaction, is unravelled to be the key factor governing the trend of conductivity. The microscopic insight provided by simulation is useful to elucidate the fundamental mechanism of ion conduction, and might facilitate the design of new COF membranes for optimal ion conduction.

3. 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.

4. 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.

5. 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.

6. ACS AMI

   

   Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

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

   ACS Applied Materials & Interfaces 2022

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   At present, 100 000+ metal–organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in “Computation-Ready, Experimental” (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental “Cambridge Structural Database” (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes.

2021

1. ACS ANM

   

   Molecular Simulation Study on Molecularly Mixed Porous Organic Cage/Polymer Composite Membranes for Water Desalination and Solvent Recovery

   Wei, Wan,  Wang, Mao, and Jiang, Jianwen

   ACS Applied Nano Materials 2021

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   Emerging as a distinct type of molecular cage possessing both intrinsic and extrinsic porosity, porous organic cages (POCs) have been regarded as intriguing nanofillers to produce molecularly mixed composite membranes (MMCMs). Here, we report a molecular simulation study to examine the swelling of three MMCMs in water, methanol, and acetonitrile and then explore their applications for water desalination and solvent recovery. The MMCMs are formed by mixing three POCs (CC1, CC3, and CC17, respectively) with a polymer of intrinsic microporosity (PIM-1). The POC/PIM membranes exhibit the greatest swelling in methanol, followed by acetonitrile and water. It is revealed that the periphery groups on the POCs have different interactions with the polymer and solvent during swelling, which plays a significant role in determining the structures of swollen membranes. Remarkably, water and methanol permeabilities through PIM-1 are enhanced 1–2-fold by all three POCs, while acetonitrile permeability is enhanced 1-fold by CC17. The enhancement is primarily attributed to the large voids formed in swollen membranes during swelling. Within the simulation durations in this study, all three POC/PIM membranes exhibit perfect rejection toward salt (NaCl) and a small solute (paracetamol). Furthermore, the POCs are observed to provide an additional pathway for solvent transport. A majority of water molecules can reside within the POCs for less than 400 ps, while methanol and acetonitrile molecules reside for approximately 1000 and 2000 ps. The residence time of the solvent is unravelled to depend on not only the solvent–cage interaction but also the solvent size and shape. From the bottom up, this comprehensive simulation study provides microscopic insights into the crucial effects of the POCs on the swelling and solvent permeation of POC/PIM membranes, and it facilitates the judicious selection of appropriate POCs to produce high-performance MMCMs for water desalination, solvent recovery, and other industrially important molecular separation.

2020

1. AS

   

   Vertically Transported Graphene Oxide for High-Performance Osmotic Energy Conversion

   Zhang, Zhenkun, Shen, Wenhao, Lin, Lingxin,  Wang, Mao, Li, Ning, Zheng, Zhifeng, Liu, Feng, and Cao, Liuxuan

   Advanced Science 2020

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   Reverse electrodialysis is a promising method to harvest the osmotic energy stored between seawater and freshwater, but it has been a long-standing challenge to fabricate permselective membranes with the power density surpassing the industry benchmark of 5.0 W m−2 for half a century. Herein, a vertically transported graphene oxide (V-GO) with the combination of high ion selectivity and ultrafast ion permeation is reported, whose permeation is three orders of magnitude higher than the extensively studied horizontally transported GO (H-GO). By mixing artificial seawater and river water, an unprecedented high output power density of 10.6 W m−2 is obtained, outperforming all existing materials. Molecular dynamics (MD) simulations reveal the mechanism of the ultrafast transport in V-GO results from the quick entering of ions and the large accessible area as well as the apparent short diffusion paths in V-GO. These results will facilitate the practical application of osmotic energy and bring an innovative design strategy for various systems involving ultrafast transport, such as filtration and catalysis.

2. NA

   

   The mixture effect on ionic selectivity and permeability of nanotubes

   Wang, Mao, Shen, Wenhao, Wang, Xue, Zhang, Gehui, Zhao, Shuang, and Liu, Feng

   Nanoscale Advances 2020

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   Ion-selective nanotubes have great potential in applications such as ion separation, desalination, and power generation. However, their performance is often limited by the deteriorated selectivity in mixed salt solutions. To reveal the underlying mechanism of the mixture effect on ion transport through nanotubes, we perform molecular dynamics (MD) simulations on ion transport through carbon nanotubes (CNTs) and polymer nanopores with a pore diameter of ∼1 nm and a charge density of −1 e nm−2. Based on the simulation results, when a single salt solution is replaced by a mixed salt solution, the ionic selectivity drops as the permeability of higher permeable ions decreases much greater than that of lower permeable ions. This is because the adsorption of lower permeable ions on the inner surface of nanotubes blocks the ion flux and increases the entrance barrier to the nanotube, and the adsorption is significantly reduced in the mixed salt solution. Such a reduction results from the occupancy of higher permeable ions on the adsorption sites as they have a higher adsorption tendency albeit weaker adsorption compared with lower permeable ions. These studies will help design the next generation of nanostructures to circumvent the mixture effect and show high permeability and selectivity in real applications.

2018

1. 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.

2. 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.

3. Nanotechnology

   

   Fabrication and application of nanoporous polymer ion-track membranes

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

   Nanotechnology 2018

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   With the development of the nano-fabrication and nanofluidics, nanoporous membranes have shown great potential in applications such as molecular separation, energy conversion, and molecular sensing. However, their performance has often been limited by the trade-off between selectivity and permeability and the lack of scalability. The prospect of overcoming these problems with nanoporous polymer ion-track membranes is promising. Focusing on these membranes, this review provides a comprehensive overview of fabrication methods, including the traditional track-etching technique and the recently developed track-UV technique; characterization methods; transport mechanisms; and major properties and applications.

2016

1. AFM

   

   Highly selective ionic transport through subnanometer pores in polymer films

   Wen, Qi, Yan, Dongxiao, Liu, Feng,  Wang, Mao, Ling, Yun, Wang, Pengfei, Kluth, Patrick, Schauries, Daniel, Trautmann, Christina, Apel, Pavel, and others,

   Advanced Functional Materials 2016

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   Novel transport phenomena through nanopores are expected to emerge as their diameters approach subnanometer scales. However, it has been challenging to explore such a regime experimentally. Here, this study reports on polymer subnanometer pores exhibiting unique selective ionic transport. 12 μm long, parallel oriented polymer nanopores are fabricated in polyethylene terephthalate (PET) films by irradiation with GeV heavy ions and subsequent 3 h exposure to UV radiation. These nanopores show ionic transport selectivity spanning more than 6 orders of magnitude: the order of the transport rate is Li+>Na+>K+>Cs+>>Mg2+>Ca2+>Ba2+, and heavy metal ions such as Cd2+ and anions are blocked. The transport can be switched off with a sharp transition by decreasing the pH value of the electrolyte. Structural measurements and molecular dynamics simulations suggest that the ionic transport is attributed to negatively charged nanopores with pore radii of ≈0.3 nm, and the selectivity is associated with the dehydration effect.

2. Highly efficient power conversion from salinity gradients with ion-selective polymeric nanopores

   Ling, Yun, Yan, Dong-Xiao, Wang, Peng-Fei,  Wang, Mao, Wen, Qi, Liu, Feng, and Wang, Yu-Gang

   Chinese Physics Letters 2016

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   A polymeric nanopore membrane with selective ionic transport has been proposed as a potential device to convert the chemical potential energy in salinity gradients to electrical power. However, its energy conversion efficiency and power density are often limited due to the challenge in reliably controlling the size of the nanopores with the conventional chemical etching method. Here we report that without chemical etching, polyimide (PI) membranes irradiated with GeV heavy ions have negatively charged nanopores, showing nearly perfect selectivity for cations over anions, and they can generate electrical power from salinity gradients. We further demonstrate that the power generation efficiency of the PI membrane approaches the theoretical limit, and the maximum power density reaches 130 mW/m 2 with a modified etching method, outperforming the previous energy conversion device that was made of polymeric nanopore membranes.

3. Influence of UV-irradiation on latent tracks in polyethylene terephthalate films

   Wen, Qi, Wang, Peng-Fei, Ling, Yun,  Wang, Mao, Yan, Dong-Xiao, Cao, Xing-Zhong, Wang, Bao-Yi, and Wang, Yu-Gang

   Chinese Physics Letters 2016

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   Polyethylene terephthalate (PET) films in thickness of 12 μm are irradiated by Xe and Au ions at the energies of 9.5 and 11.4MeV/u and with the ion fluence from 5 × 10 9 cm −2 to 1 × 10 11 cm −2 . After irradiation, ultra-violet lights are used to illuminate the samples with latent tracks at the wavelength of 365 nm with flux density of 4.2 mW/cm −2 . UV-irradiation effects on tracked PET are investigated by the UV-vis spectrum and positron annihilation lifetime spectroscopy (PALS). It is found that carbonaceous clusters in PET films are generated by ion irradiation and decomposed with UV illumination by calculating the optical energy band gap E g in the UV-vis spectrum. The free volumes behave differently in track and bulk after UV illumination. In our experiment, the PALS results show an increase in radius and density of free volume in tracked PET films after UV treatment, which indicates an expansion in radius of latent tracks.

2015