Page 40 - Read Online
P. 40

Liu et al. J Mater Inf 2024;4:33  https://dx.doi.org/10.20517/jmi.2024.48       Page 33 of 33

               142.      Navidpour AH, Hosseinzadeh A, Huang Z, Li D, Zhou JL. Application of machine learning algorithms in predicting the
                    photocatalytic degradation of perfluorooctanoic acid. Catal Rev 2024;66:687-712.  DOI
               143.      Lira JO, Riella HG, Padoin N, Soares C. Computational fluid dynamics (CFD), artificial neural network (ANN) and genetic algorithm
                    (GA) as a hybrid method for the analysis and optimization of micro-photocatalytic reactors: NOx abatement as a case study. Chem
                    Eng J 2022;431:133771.  DOI
               144.      Li X, Maffettone PM, Che Y, Liu T, Chen L, Cooper AI. Combining machine learning and high-throughput experimentation to
                    discover photocatalytically active organic molecules. Chem Sci 2021;12:10742-54.  DOI  PubMed  PMC
               145.      Parmar N, Srivastava JK. Process optimization and kinetics study for photocatalytic ciprofloxacin degradation using TiO   2
                    nanoparticle:  a  comparative  study  of  artificial  neural  network  and  surface  response  methodology.  J  Indian  Chem  Soc
                    2022;99:100584.  DOI
               146.      Malayeri M, Nasiri F, Haghighat F, Lee C. Optimization of photocatalytic oxidation reactor for air purifier design: application of
                    artificial neural network and genetic algorithm. Chem Eng J 2023;462:142186.  DOI
               147.      Truong H, Cuong Nguyen X, Hur J. Recent advances in g-C N -based photocatalysis for water treatment: magnetic and floating
                                                              4
                                                             3
                    photocatalysts, and applications of machine-learning techniques. J Environ Manage 2023;345:118895.  DOI  PubMed
               148.      Saadetnejad D, Oral B, Can E, Yıldırım R. Machine learning analysis of gas phase photocatalytic CO  reduction for hydrogen
                                                                                         2
                    production. Int J Hydrog Energy 2022;47:19655-68.  DOI
               149.      Gordanshekan A, Arabian S, Solaimany Nazar AR, Farhadian M, Tangestaninejad S. A comprehensive comparison of green Bi WO /
                                                                                                      2  6
                    g-C N  and Bi WO /TiO  S-scheme heterojunctions for photocatalytic adsorption/degradation of Cefixime: artificial neural network,
                      3  4   2  6   2
                    degradation pathway, and toxicity estimation. Chem Eng J 2023;451:139067.  DOI
               150.      Anandhi G, Iyapparaja M. Photocatalytic degradation of drugs and dyes using a maching learning approach. RSC Adv 2024;14:9003-
                    19.  DOI  PubMed  PMC
               151.      Liu Q, Pan K, Zhu L, et al. Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting
                               †
                    over doped TiO . Green Chem 2023;25:8778-90.  DOI
                              2
               152.      Park H, Bentria ET, Rtimi S, Arredouani A, Bensmail H, El-mellouhi F. Accelerating the design of photocatalytic surfaces for
                    antimicrobial application: machine learning based on a sparse dataset. Catalysts 2021;11:1001.  DOI
               153.      Kim CM, Jaffari ZH, Abbas A, Chowdhury MF, Cho KH. Machine learning analysis to interpret the effect of the photocatalytic
                    reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. J Hazard Mater 2024;465:132995.  DOI  PubMed
               154.      Biswas T, Singh AK. Excitonic effects in absorption spectra of carbon dioxide reduction photocatalysts. npj Comput Mater
                    2021;7:640.  DOI
               155.      Jeong H, Yun B, Na S, et al. Multimodal deep learning models incorporating the adsorption characteristics of the adsorbent for
                    estimating the permeate flux in dynamic membranes. J Membr Sci 2024;709:123105.  DOI
               156.      Zhang Z, Yang Z, Zhao Z, Liu Y, Wang C, Xu W. Multimodal deep-learning framework for accurate prediction of wettability
                    evolution of laser-textured surfaces. ACS Appl Mater Interfaces 2023;Online ahead of print.  DOI  PubMed
               157.      Liang W, Huang J, Sun J, Zhang P, Li A. Multiscale modeling and simulation of surface-enhanced spectroscopy and plasmonic
                    photocatalysis. WIREs Comput Mol Sci 2023;13:e1665.  DOI
               158.      Kovačič Ž, Likozar B, Huš M. Photocatalytic CO  reduction: a review of Ab initio mechanism, kinetics, and multiscale modeling
                                                     2
                    simulations. ACS Catal 2020;10:14984-5007.  DOI
               159.      Gusarov S. Advances in computational methods for modeling photocatalytic reactions: a review of recent developments. Materials
                    2024;17:2119.  DOI  PubMed  PMC
               160.      Loh JYY, Wang A, Mohan A, et al. Leave no photon behind: artificial intelligence in multiscale physics of photocatalyst and
                    photoreactor design. Adv Sci 2024;11:e2306604.  DOI  PubMed  PMC
               161.      Oliveira GX, Kuhn S, Riella HG, Soares C, Padoin N. Combining computational fluid dynamics, photon fate simulation and machine
                    learning to optimize continuous-flow photocatalytic systems. React Chem Eng 2023;8:2119-33.  DOI
               162.      Huang G, Guo Y, Chen Y, Nie Z. Application of machine learning in material synthesis and property prediction. Materials
                    2023;16:5977.  DOI  PubMed  PMC
               163.      Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature
                    2018;559:547-55.  DOI  PubMed
   35   36   37   38   39   40   41   42   43   44   45