One-step solvothermal synthesis of MoS2@Ti cathode for electrochemical reduction of Hg2+ and predicting BP neural network model

Du, Yan; Li, Xiaohan; Cao, Limei; Yang, Ji

doi:10.1016/j.seppur.2023.125654

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Catalysts 2024, 14, x FOR PEER REVIEW 5 of following neural network models have been used to predict the photocatalytic perfo mance of photocatalysts: Perceptron (P), feed forward (FF), radial basis network (RBF deep feed forward (DFF), recurrent neural network (RNN), long/short term memo (LSTM), restricted BM (RBM), deep convolutional network (DCN), generative adversari network (GAN), extreme learning machine (ELM), echo state network (ESN), and suppo vector machine (SVM) [49][50][51][52][53][54][55][56][57][58]. In addition to the models mentioned above, the backpro agation (BP) neural network model is the most popular model for predicting the phot catalytic performance of various photocatalysts [59][60][61]. To make up for the shortcomin of a single neural network model, it has become a trend to combine multiple neural ne work models to predict the photocatalytic activity of photocatalysts.…”

Section: Neural Network Model Suitable For Photocatalyst Developmentmentioning

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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