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Publication Zeolitic-imidazolate framework membranes for organic solvent nanofiltration: a molecular simulation exploration(2018-08) Wan Wei; Krishna M. Gupta; Jie Liu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPublication Water desalination and biofuel dehydration through a thin membrane of polymer of intrinsic microporosity: atomistic simulation study(2018-01) Qi Shi; Kang Zhang; Ruifeng Lu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPublication Porous organic cages embedded in a lipid membrane for water desalination: A molecular simulation study(Elsevier B.V., 2018-11-28) Zhao D.; Liu J.; Jiang J.; CHEMICAL & BIOMOLECULAR ENGINEERINGWe report a molecular simulation study on porous organic cages (POCs) embedded in a lipid membrane for water desalination by reverse osmosis (RO). Four types of POC channels are examined namely straight, tilted, tilted with 3-openings and crystalline. The flow rates of water are found to increase in the order of tilted < straight < 3-opening < crystalline channel. With 3D interconnected morphology, the crystalline channel possesses the highest flow rate about 6.86 water molecules per ns. Both Na+ and Cl− ions cannot pass through the four channels with 100% salt rejection. The activation energy for water flow through the POC channels is approximately 12 – 13 kJ/mol, much lower than 22 – 26 kJ/mol through aromatic polyamide membranes. Due to confinement effect, the number of hydrogen bonds formed by water molecules in the channels is lower than in bulk water. Wetting-dewetting transition is observed in the straight, tilted and 3-opening channels, but not in the crystalline channel. This work highlights the important role of channel alignment, opening and morphology in water flow, and provides microscopic understanding of water structure and dynamics in the POC channels. © 2018 Elsevier B.V.Publication Nanopore sequencing improves construction of customized CRISPR-based gene activation libraries(John Wiley & Sons, Inc., 2024-01-31) Handing Wang; Heng Yih Tan; Jiazhang Lian; Kang Zhou; Hal, Alper; CHEMICAL & BIOMOLECULAR ENGINEERINGClustered regularly interspaced short palindromic repeats (CRISPR)‐based screening has emerged as a powerful tool for identifying new gene targets for desired cellular phenotypes. The construction of guide RNA (gRNA) pools largely determines library quality and is usually performed using Golden Gate assembly or Gibson assembly. To date, library construction methods have not been systematically compared, and the quality check of each batch has been slow. In this study, an in‐house nanopore sequencing workflow was established for assessing the current methods of gRNA pool construction. The bias of pool construction was reduced by employing the polymerase‐mediated non‐amplifying method. Then, a small gRNA pool was utilized to characterize stronger activation domains, specifically MED2 (a subunit of mediator complex) and HAP4 (a heme activator protein), as well as to identify better gRNA choices for dCas12a‐based gene activation in Saccharomyces cerevisiae. Furthermore, based on the better CRISPRa tool identified in this study, a custom gRNA pool, which consisted of 99 gRNAs targeting central metabolic pathways, was designed and employed to screen for gene targets that could improve ethanol utilization in S. cerevisiae. The nanopore sequencing‐based workflow demonstrated here should provide a cost‐effective approach for assessing the quality of customized gRNA library, leading to faster and more efficient genetic and metabolic engineering in S. cerevisiae.Publication A Highly Rigid and Conjugated Microporous Polymer Membrane for Solvent Permeation and Biofuel Purification: A Molecular Simulation Study(2020-01) Jie Liu; Wan Wei; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPublication Machine Learning for Polymer Swelling in Liquids(American Chemical Society (ACS), 2020-07-20) Qisong Xu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGSwelling in liquids is of paramount importance for polymers used in many liquid-phase applications. This critical property has motivated numerous analytical theories and empirical experiments as well as recent atomistic simulations; however, a data-driven approach for polymer swelling is currently not available. In this study, we develop a machine learning (ML) methodology to investigate polymer swelling in liquids. This methodology is illustrated for the swelling of organic solvent nanofiltration (OSN) membranes and polydimethylsiloxane (PDMS) in various solvents. First, chemically intuitive descriptors such as solubility parameters and solvent properties are proposed to construct ML models. Using kernel ridge regression, the model based on the solubility parameters of the solvent and polymer is found to offer the best quantitative prediction and reveal multimodal swelling behavior for OSN membranes. For PDMS swelling, the solubility parameter and geometry of solvent are identified to be key properties. Then, a molecular representation via the sum-of-fragments approach is proposed and demonstrated remarkable predictive capability. Through appropriate data augmentation, reasonable out-of-sample prediction is achieved for polyetherimide swelling in nine solvents and PDMS swelling in substituted aromatic solvents. Finally, principal component analysis is applied to the proposed sum-of-fragments to explore its suitability as a molecular representation and the chemical space of polymer swelling. The relationships between molecular fragments and swelling degrees are quantitatively determined by Pearson correlations. This ML study demonstrates the development and utilization of physically meaningful chemical descriptors to construct models capable of superior prediction and unraveling fundamental insight into polymer swelling. Such a methodology can also be extended to other physical properties for polymers in liquids, thereby expanding its scope of potential applications.Publication A molecular simulation study for efficient separation of 2,5-furandiyldimethanamine by a microporous polyarylate membrane(2019-03) Krishna M. Gupta; Jie Liu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPublication Molecular simulations of liquid separations in polymer membranes(Elsevier Ltd, 2020-06-01) Qisong Xu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPolymer membranes are widely utilized in industrial separation processes. Along with numerous experiments conducted for gas and liquid separations, molecular simulations have also been performed to fundamentally elucidate separation mechanisms in polymer membranes. However, the simulation studies are mostly limited to gas and aqueous mixtures, and non-aqueous separations such as pervaporation and organic solvent nanofiltration are less or largely unexplored. In this review, the recent simulation studies on liquid separations in polymer membranes are critically reviewed. First, various rational design strategies to tune the performance of polymer membranes for water desalination are outlined. Then, new simulation protocols and computational characterization methods are presented for pervaporation and organic solvent nanofiltration. These bottom-up studies provide a rich landscape of microscopic insights into the separation mechanisms and processes of liquid mixtures in polymer membranes, which will facilitate the rational screening and design of high-performance polymer membranes for liquid separations. 2020 Elsevier LtdPublication Molecular simulation and analysis of sorption process toward theoretical prediction for liquid permeation through membranes(2018-12) Qisong Xu; Kang Zhang; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERINGPublication Microporous benzimidazole-linked polymer and its derivatives for organic solvent nanofiltration(2019-06) Jie Liu; Jianwen Jiang; CHEMICAL & BIOMOLECULAR ENGINEERING