A and B sites doped CaAl12O19: Phase component, energy band, color and photoluminescence tuning, color performance prediction and application for pigments

Jin, Yujia; Wang, Shifa; Yu, Xinmiao; Li, Maoyuan; Gao, Huajing; Yang, Hua; Fang, Leiming; Zhang, Yuanyuan; Ding, Jinjin; Líu, Hao; Chen, Xiangyu; Han, Mengjun; V., Jagadeesha Angadi; Ubaidullah, Mohd; Pandit, Bidhan; Li, Dengfeng

doi:10.1016/j.saa.2023.123254

Cited by 7 publications

(1 citation statement)

References 69 publications

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“…In addition, the neural network model can be optimized by combining other deep learning algorithms such as simulated annealing, genetic algorithm, tabu search, neural network, beetle search algorithm, sparrow search algorithm, dung beetle optimization algorithm, whale algorithm, fuzzy logic, particle swarm optimization algorithm, immune algorithm, distribution estimation algorithm, etc. [66,67].…”

Section: Limitations Of Neural Network Modelsmentioning

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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Section: Limitations Of Neural Network Modelsmentioning

confidence: 99%

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Wang,

Mo,

et al. 2024

Catalysts

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Enhanced Photocatalytic Activity of O‐deficient Modified CuAl₂O₄/MoS₂ Composite Photocatalysts in Wastewater Treatment Applications

Han,

Wang,

et al. 2024

Advanced Sustainable Systems

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The O‐deficient modified CuAl2O4/MoS2 composite photocatalysts (ODMCAOMSCP) are fabricated by a wet chemical route and low‐temperature treatment for the degradation of tetracycline hydrochloride (TC). The fine particles dotted on the large sheet are revealed to be comprised of large sheets with rich oxygen defects and nanoparticles, which endows it with a high optical absorption coefficient, available reactive species, excellent charge carrier separation, and high photocatalytic activity. The finely designed ODMCAOMSCP achieve a TC degradation first‐order rate constant of 0.01436 min−1, which is 3.99 and 2.70 times higher than that of MoS2 and CuAl2O4, respectively. Response surface analysis reveals that multiple environmental factors including initial TC concentration, catalyst content, and initial pH affect the photocatalytic activity of the catalyst on the basis of backpropagation (BP) neural network optimized by genetic algorithm and Box‐Behnken design. The degradation intermediates and degradation pathways are determined by a liquid chromatograph‐mass spectrometer (LC‐MS). The toxicity of TC over ‐ ODMCAOMSCP is studied by the toxicity analysis software and the cultivation of mung bean. Mechanism investigation demonstrates that the major active species of ODMCAOMSCP for the degradation TC are attributed to oxygen vacancy, hydroxyl radical (∙OH), and superoxide radical (∙O2−).

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Rare earth ions (Er, Ho and Sm) regulate the optical and photoluminescence properties of CaAl12O19: Performance prediction and anti-counterfeiting application

Jin,

Wang,

Zhang

et al. 2024

Ceramics International

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A and B sites doped CaAl12O19: Phase component, energy band, color and photoluminescence tuning, color performance prediction and application for pigments

Cited by 7 publications

References 69 publications

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Enhanced Photocatalytic Activity of O‐deficient Modified CuAl₂O₄/MoS₂ Composite Photocatalysts in Wastewater Treatment Applications

Rare earth ions (Er, Ho and Sm) regulate the optical and photoluminescence properties of CaAl12O19: Performance prediction and anti-counterfeiting application

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A and B sites doped CaAl12O19: Phase component, energy band, color and photoluminescence tuning, color performance prediction and application for pigments

Cited by 7 publications

References 69 publications

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

Enhanced Photocatalytic Activity of O‐deficient Modified CuAl2O4/MoS2 Composite Photocatalysts in Wastewater Treatment Applications

Rare earth ions (Er, Ho and Sm) regulate the optical and photoluminescence properties of CaAl12O19: Performance prediction and anti-counterfeiting application

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Enhanced Photocatalytic Activity of O‐deficient Modified CuAl₂O₄/MoS₂ Composite Photocatalysts in Wastewater Treatment Applications