TY - JOUR AU - Wang, Mingxuan AU - Chen, Yi AU - Liu, Xu AU - Zhao, Zijiang AU - Ren, Linhan AU - Huang, Songtao AU - Dong, Guanglu AU - Xia, Molin AU - Li, Xiaonian AU - Wei, Zhongzhe AU - Wang, Jianguo TI - Unlocking enhanced selectivity of Pd/Al 2 O 3 in semihydrogenation of alkynes by in situ ligand‐reduction strategy JF - AICHE JOURNAL J2 - AICHE J VL - 9(2) PY - 2024 SP - 1 SN - 0001-1541 DO - 10.1002/aic.18386 UR - https://m2.mtmt.hu/api/publication/34723507 ID - 34723507 AB - The partial hydrogenation of alkynes into alkenes constitutes a pivotal chemical transformation in the synthesis of fine chemicals, pharmaceuticals, and polymers. Nevertheless, the extant industrial catalyst falls short of achieving a felicitous equilibrium between its catalytic potency and selectivity. Herein, triphenylphosphine (PPh 3 ) modified Pd/Al 2 O 3 catalysts are fabricated through a “one‐pot” process, which achieve highly selective hydrogenation of alkynes at room temperature. PPh 3 plays a dual role—as a reducing agent and a ligand. Kinetic and characterization studies reveal PPh 3 alters H 2 activation from homolytic to heterolytic cleavage, which facilitate the formation of alkene. Moreover, PPh 3 can also optimize the reaction microenvironment, achieving the reduced self‐poisoning of alkyne and promoted desorption of alkene. In the realm of alkyne hydrogenation, our philosophy encompasses a global optimization of active sites, ligand systems, and reaction microenvironmental. This method steers the course of catalyst development for other reactions beyond the field of alkyne hydrogenation. LA - English DB - MTMT ER - TY - JOUR AU - Chen, Yuqiu AU - Ma, Sulei AU - Lei, Yang AU - Liang, Xiaodong AU - Liu, Xinyan AU - Kontogeorgis, Georgios M. AU - Gani, Rafiqul TI - Ionic liquid binary mixtures: Machine learning‐assisted modeling, solvent tailoring, process design, and optimization JF - AICHE JOURNAL J2 - AICHE J VL - 2024 PY - 2024 SN - 0001-1541 DO - 10.1002/aic.18392 UR - https://m2.mtmt.hu/api/publication/34694376 ID - 34694376 AB - This work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)‐IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML‐based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML‐based GC models are sequentially integrated into computer‐aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL‐IL binary mixtures in practical applications. LA - English DB - MTMT ER - TY - JOUR AU - Fu, Zhongwang AU - Wang, Zheyu AU - Chen, Gong TI - Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling JF - AICHE JOURNAL J2 - AICHE J PY - 2024 PG - 12 SN - 0001-1541 DO - 10.1002/aic.18388 UR - https://m2.mtmt.hu/api/publication/34671596 ID - 34671596 LA - English DB - MTMT ER - TY - JOUR AU - Mcelvain, Kelly AU - Gopalakrishnan, Sandeep AU - Dabagh, Mahsa TI - Role of mechanotransduction on decision making for treatment of chronic wounds JF - AICHE JOURNAL J2 - AICHE J PY - 2024 PG - 11 SN - 0001-1541 DO - 10.1002/aic.18390 UR - https://m2.mtmt.hu/api/publication/34650058 ID - 34650058 LA - English DB - MTMT ER - TY - JOUR AU - Nyande, Baggie W. AU - Nagy, Zoltan K. AU - Lakerveld, Richard TI - Data-driven identification of crystallization kinetics JF - AICHE JOURNAL J2 - AICHE J PY - 2024 PG - 11 SN - 0001-1541 DO - 10.1002/aic.18333 UR - https://m2.mtmt.hu/api/publication/34643902 ID - 34643902 LA - English DB - MTMT ER - TY - JOUR AU - Tuo, Yongxiao AU - Liu, Wanli AU - Lu, Qing AU - Zhang, Xinling AU - Zhou, Xin AU - Zhou, Yan AU - Feng, Xiang AU - Wu, Mingbo AU - Wang, Zhihua AU - Chen, De AU - Zhang, Jun TI - Breaking the scaling relationship via lattice expansion of Ag for CO2 electroreduction over a wide potential window JF - AICHE JOURNAL J2 - AICHE J PY - 2024 PG - 11 SN - 0001-1541 DO - 10.1002/aic.18365 UR - https://m2.mtmt.hu/api/publication/34639756 ID - 34639756 LA - English DB - MTMT ER - TY - JOUR AU - Punch, O. AU - Heenan, A. AU - Marshall, A. AU - Holland, D.J. TI - Characterization of the contact dynamics of spheres coated in a thin viscous film using an electrified Newton's cradle JF - AICHE JOURNAL J2 - AICHE J VL - 70 PY - 2024 IS - 2 PG - 15 SN - 0001-1541 DO - 10.1002/aic.18309 UR - https://m2.mtmt.hu/api/publication/34548546 ID - 34548546 LA - English DB - MTMT ER - TY - JOUR AU - Zhang, Kevin AU - Mann, Vipul AU - Venkatasubramanian, Venkat TI - G-MATT: Single-step retrosynthesis prediction using molecular grammar tree transformer JF - AICHE JOURNAL J2 - AICHE J VL - 70 PY - 2024 IS - 1 PG - 21 SN - 0001-1541 DO - 10.1002/aic.18244 UR - https://m2.mtmt.hu/api/publication/34613957 ID - 34613957 AB - Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with prior domain knowledge. We present a tree-to-sequence transformer architecture that utilizes hierarchical SMILES grammar-based trees, incorporating crucial chemistry information that is often overlooked by SMILES text-based representations, such as local structures and functional groups. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a promising top-1 accuracy of 51% (top-10 accuracy of 79.1%), an invalid rate of 1.5%, and a bioactive similarity rate of 74.8% on the USPTO-50K dataset. Additional analyses of G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures. LA - English DB - MTMT ER - TY - JOUR AU - Dickey, Roman M. AU - Jones, Michaela A. AU - Butler, Neil D. AU - Govil, Ishika AU - Kunjapur, Aditya M. TI - Genome engineering allows selective conversions of terephthalaldehyde to multiple valorized products in bacterial cells JF - AICHE JOURNAL J2 - AICHE J PY - 2023 PG - 12 SN - 0001-1541 DO - 10.1002/aic.18230 UR - https://m2.mtmt.hu/api/publication/34648358 ID - 34648358 LA - English DB - MTMT ER - TY - JOUR AU - Agrawal, Nikhil R. AU - Wang, Rui TI - Non-monotonic salt concentration dependence of inverted electrokinetic flow JF - AICHE JOURNAL J2 - AICHE J PY - 2023 PG - 10 SN - 0001-1541 DO - 10.1002/aic.18269 UR - https://m2.mtmt.hu/api/publication/34588414 ID - 34588414 AB - Modeling of electrokinetic flows is crucial to understand numerous phenomena associated with electrochemistry, biophysics, and colloidal science. Here, we incorporate the modified Gaussian renormalized fluctuation theory into transport equations for electrolyte solutions to study the ion-correlation-induced inversion of electrokinetic flows, also known as charge inversion. We are able to capture the non-monotonic dependence of inverted streaming current and reversed electrophoretic mobility on salt concentration. By analyzing the double-layer structure, we elucidate that this non-monotonicity is a consequence of the competition between spatially varying ion correlations and the translational entropy of the ions. We find that for practical values of surface charge densities, the excluded volume effect does not play any significant role. In a significant improvement over existing theories, our theoretical predictions are in quantitative agreement with experimental measurements for charge inversion in trivalent salts. LA - English DB - MTMT ER -