Machine learning: new ideas and tools in environmental science and engineering S Zhong, K Zhang, M Bagheri, JG Burken, A Gu, B Li, X Ma, BL Marrone, ... Environmental Science & Technology 55 (19), 12741-12754, 2021 | 488 | 2021 |
Effect of MnO2 phase structure on the oxidative reactivity toward bisphenol A degradation J Huang#, S Zhong#, Y Dai, CC Liu, H Zhang Environmental Science & Technology 52 (19), 11309-11318, 2018 | 234 | 2018 |
Highly efficient visible light-driven Ag/AgBr/ZnO composite photocatalyst for degrading Rhodamine B L Shi, L Liang, J Ma, Y Meng, S Zhong, F Wang, J Sun Ceramics International 40 (2), 3495-3502, 2014 | 163 | 2014 |
Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning K Zhang, S Zhong, H Zhang Environmental Science & Technology 54 (11), 7008-7018, 2020 | 127 | 2020 |
A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants S Zhong, J Hu, X Fan, X Yu, H Zhang Journal of hazardous materials 383, 121141, 2020 | 87 | 2020 |
Revolutionizing membrane design using machine learning-bayesian optimization H Gao#, S Zhong#, W Zhang, T Igou, E Berger, E Reid, Y Zhao, ... Environmental Science & Technology 56 (4), 2572-2581, 2021 | 86 | 2021 |
Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds S Zhong, K Zhang, D Wang, H Zhang Chemical Engineering Journal 405, 126627, 2021 | 86 | 2021 |
NO oxidation over Ni–Co perovskite catalysts S Zhong, Y Sun, H Xin, C Yang, L Chen, X Li Chemical Engineering Journal 275, 351-356, 2015 | 75 | 2015 |
Kinetics and Mechanistic Insight into Efficient Fixation of CO2 to Epoxides over N-Heterocyclic Compound/ZnBr2 Catalysts M Liu, B Liu, S Zhong, L Shi, L Liang, J Sun Industrial & Engineering Chemistry Research 54 (2), 633-640, 2015 | 70 | 2015 |
Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model … S Zhong, J Hu, X Yu, H Zhang Chemical Engineering Journal 408, 127998, 2021 | 69 | 2021 |
ZnBr2/DMF as simple and highly active Lewis acid–base catalysts for the cycloaddition of CO2 to propylene oxide S Zhong, L Liang, B Liu, J Sun Journal of CO2 Utilization 6, 75-79, 2014 | 63 | 2014 |
Tetraethylorthosilicate induced preparation of mesoporous graphitic carbon nitride with improved visible light photocatalytic activity L Shi, L Liang, F Wang, M Liu, S Zhong, J Sun Catalysis Communications 59, 131-135, 2015 | 61 | 2015 |
Machine learning-assisted QSAR models on contaminant reactivity toward four oxidants: combining small data sets and knowledge transfer S Zhong, Y Zhang, H Zhang Environmental Science & Technology 56 (1), 681-692, 2021 | 53 | 2021 |
DMF and mesoporous Zn/SBA-15 as synergistic catalysts for the cycloaddition of CO2 to propylene oxide S Zhong, L Liang, M Liu, B Liu, J Sun Journal of CO2 Utilization 9, 58-65, 2015 | 43 | 2015 |
A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through artificial neural network Z Jiang, J Hu, X Zhang, Y Zhao, X Fan, S Zhong, H Zhang, X Yu Environmental Research 187, 109697, 2020 | 33 | 2020 |
New insight into the reactivity of Mn (III) in bisulfite/permanganate for organic compounds oxidation: The catalytic role of bisulfite and oxygen S Zhong, H Zhang Water research 148, 198-207, 2019 | 33 | 2019 |
Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe (II) complex Y Gao, S Zhong, TL Torralba-Sanchez, PG Tratnyek, EJ Weber, Y Chen, ... Water research 192, 116843, 2021 | 29 | 2021 |
Mn (III)-ligand complexes as a catalyst in ligand-assisted oxidation of substituted phenols by permanganate in aqueous solution S Zhong, H Zhang Journal of hazardous materials 384, 121401, 2020 | 25 | 2020 |
Count-based morgan fingerprint: A more efficient and interpretable molecular representation in developing machine learning-based predictive regression models for water … S Zhong, X Guan Environmental Science & Technology 57 (46), 18193-18202, 2023 | 20 | 2023 |
Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning H Gao#, S Zhong#, R Dangayach, Y Chen Environmental Science & Technology, 2023 | 17 | 2023 |