@article{MTMT:34723507,
	title = {Unlocking enhanced selectivity of Pd/Al 2 O 3 in semihydrogenation of alkynes by in situ ligand‐reduction strategy},
	url = {https://m2.mtmt.hu/api/publication/34723507},
	author = {Wang, Mingxuan and Chen, Yi and Liu, Xu and Zhao, Zijiang and Ren, Linhan and Huang, Songtao and Dong, Guanglu and Xia, Molin and Li, Xiaonian and Wei, Zhongzhe and Wang, Jianguo},
	doi = {10.1002/aic.18386},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	volume = {9(2)},
	unique-id = {34723507},
	issn = {0001-1541},
	abstract = {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.},
	year = {2024},
	eissn = {1547-5905},
	pages = {1},
	orcid-numbers = {Wei, Zhongzhe/0000-0003-1521-6587; Wang, Jianguo/0000-0003-2391-4529}
}

@article{MTMT:34694376,
	title = {Ionic liquid binary mixtures: Machine learning‐assisted modeling, solvent tailoring, process design, and optimization},
	url = {https://m2.mtmt.hu/api/publication/34694376},
	author = {Chen, Yuqiu and Ma, Sulei and Lei, Yang and Liang, Xiaodong and Liu, Xinyan and Kontogeorgis, Georgios M. and Gani, Rafiqul},
	doi = {10.1002/aic.18392},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	volume = {2024},
	unique-id = {34694376},
	issn = {0001-1541},
	abstract = {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.},
	year = {2024},
	eissn = {1547-5905},
	orcid-numbers = {Lei, Yang/0000-0002-1975-3569; Liang, Xiaodong/0000-0002-2007-546X; Liu, Xinyan/0000-0001-9828-8660; Kontogeorgis, Georgios M./0000-0002-7128-1511}
}

@article{MTMT:34671596,
	title = {Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling},
	url = {https://m2.mtmt.hu/api/publication/34671596},
	author = {Fu, Zhongwang and Wang, Zheyu and Chen, Gong},
	doi = {10.1002/aic.18388},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34671596},
	issn = {0001-1541},
	keywords = {ALGORITHM; parameter estimation; kinetic modeling; Particle Swarm Optimization; CHO cell culture},
	year = {2024},
	eissn = {1547-5905},
	orcid-numbers = {Wang, Zheyu/0000-0001-6037-6219}
}

@article{MTMT:34650058,
	title = {Role of mechanotransduction on decision making for treatment of chronic wounds},
	url = {https://m2.mtmt.hu/api/publication/34650058},
	author = {Mcelvain, Kelly and Gopalakrishnan, Sandeep and Dabagh, Mahsa},
	doi = {10.1002/aic.18390},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34650058},
	issn = {0001-1541},
	keywords = {Wound healing; Tissue Engineering; wound dressing; mechanotransduction; Computational modeling; Chronic wound; wound tissue},
	year = {2024},
	eissn = {1547-5905}
}

@article{MTMT:34643902,
	title = {Data-driven identification of crystallization kinetics},
	url = {https://m2.mtmt.hu/api/publication/34643902},
	author = {Nyande, Baggie W. and Nagy, Zoltan K. and Lakerveld, Richard},
	doi = {10.1002/aic.18333},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34643902},
	issn = {0001-1541},
	keywords = {CRYSTALLIZATION; Population balance model; PAT; data-driven modeling; Sparse identification},
	year = {2024},
	eissn = {1547-5905}
}

@article{MTMT:34639756,
	title = {Breaking the scaling relationship via lattice expansion of Ag for CO<sub>2</sub> electroreduction over a wide potential window},
	url = {https://m2.mtmt.hu/api/publication/34639756},
	author = {Tuo, Yongxiao and Liu, Wanli and Lu, Qing and Zhang, Xinling and Zhou, Xin and Zhou, Yan and Feng, Xiang and Wu, Mingbo and Wang, Zhihua and Chen, De and Zhang, Jun},
	doi = {10.1002/aic.18365},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34639756},
	issn = {0001-1541},
	keywords = {CO2 electroreduction; Lattice expansion; scaling relationship; Ag electrocatalyst; wide potential range},
	year = {2024},
	eissn = {1547-5905},
	orcid-numbers = {Tuo, Yongxiao/0000-0001-7997-805X}
}

@article{MTMT:34548546,
	title = {Characterization of the contact dynamics of spheres coated in a thin viscous film using an electrified Newton's cradle},
	url = {https://m2.mtmt.hu/api/publication/34548546},
	author = {Punch, O. and Heenan, A. and Marshall, A. and Holland, D.J.},
	doi = {10.1002/aic.18309},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	volume = {70},
	unique-id = {34548546},
	issn = {0001-1541},
	year = {2024},
	eissn = {1547-5905}
}

@article{MTMT:34613957,
	title = {G-MATT: Single-step retrosynthesis prediction using molecular grammar tree transformer},
	url = {https://m2.mtmt.hu/api/publication/34613957},
	author = {Zhang, Kevin and Mann, Vipul and Venkatasubramanian, Venkat},
	doi = {10.1002/aic.18244},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	volume = {70},
	unique-id = {34613957},
	issn = {0001-1541},
	abstract = {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.},
	keywords = {Artificial intelligence; machine learning; computational chemistry; retrosynthesis prediction; symbolic and numeric AI},
	year = {2024},
	eissn = {1547-5905},
	orcid-numbers = {Mann, Vipul/0000-0003-0225-8729; Venkatasubramanian, Venkat/0000-0002-4923-0582}
}

@article{MTMT:34648358,
	title = {Genome engineering allows selective conversions of terephthalaldehyde to multiple valorized products in bacterial cells},
	url = {https://m2.mtmt.hu/api/publication/34648358},
	author = {Dickey, Roman M. and Jones, Michaela A. and Butler, Neil D. and Govil, Ishika and Kunjapur, Aditya M.},
	doi = {10.1002/aic.18230},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34648358},
	issn = {0001-1541},
	keywords = {biosynthesis; AMINE; Oxidoreductases; aldehyde; genome engineering},
	year = {2023},
	eissn = {1547-5905}
}

@article{MTMT:34588414,
	title = {Non-monotonic salt concentration dependence of inverted electrokinetic flow},
	url = {https://m2.mtmt.hu/api/publication/34588414},
	author = {Agrawal, Nikhil R. and Wang, Rui},
	doi = {10.1002/aic.18269},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	unique-id = {34588414},
	issn = {0001-1541},
	abstract = {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.},
	keywords = {ELECTROPHORESIS; Overcharging; Charge inversion; streaming current; Multivalent electrolytes; Ion correlation; Gaussian renormalized fluctuation theory},
	year = {2023},
	eissn = {1547-5905},
	orcid-numbers = {Agrawal, Nikhil R./0000-0002-0110-2581; Wang, Rui/0000-0002-4058-9521}
}