Electrogeneration of active photocatalysts for wastewater remediation: a review

Mutalib, Aunie Afifah Abdul; Jaafar, Nur Farhana

doi:10.1007/s10311-022-01534-6

Cited by 7 publications

(1 citation statement)

References 130 publications

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“…In the early stage of the development of photocatalysts, it is only through constantly preparing different catalysts and conducting photocatalytic experiments that effective photocatalysts can be found [12,13]. The unremitting efforts of scientists from various countries have resulted in the development of numerous photocatalysts with different catalytic activities [14][15][16][17][18][19]. Researchers have created many new heterojunction composite photocatalysts due to the low photocatalytic activity of a single-component photocatalyst and its inability to meet special needs like magnetic separation [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Wang,

Mo,

et al. 2024

Catalysts

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Photocatalysts have made great contributions to the degradation of pollutants to achieve environmental purification. The traditional method of developing new photocatalysts is to design and perform a large number of experiments to continuously try to obtain efficient photocatalysts that can degrade pollutants, which is time-consuming, costly, and does not necessarily achieve the best performance of the photocatalyst. The rapid development of photocatalysis has been accelerated by the rapid development of artificial intelligence. Intelligent algorithms can be utilized to design photocatalysts and predict photocatalytic performance, resulting in a reduction in development time and the cost of new catalysts. In this paper, the intelligent algorithms for photocatalyst design and photocatalytic performance prediction are reviewed, especially the artificial neural network model and the model optimized by an intelligent algorithm. A detailed discussion is given on the advantages and disadvantages of the neural network model, as well as its application in photocatalysis optimized by intelligent algorithms. The use of intelligent algorithms in photocatalysis is challenging and long term due to the lack of suitable neural network models for predicting the photocatalytic performance of photocatalysts. The prediction of photocatalytic performance of photocatalysts can be aided by the combination of various intelligent optimization algorithms and neural network models, but it is only useful in the early stages. Intelligent algorithms can be used to design photocatalysts and predict their photocatalytic performance, which is a promising technology.

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