Feature-Driven Ensemble Machine Learning Prediction of Photocatalytic Discoloration of Dyes DOI

SHUBHAM MAKWANA,

Leena V. Bora,

Nisha V. Bora

et al.

Published: Jan. 1, 2025

Language: Английский

Light-driven simultaneous water purification and green energy production by photocatalytic fuel cell: A comprehensive review on current status, challenges, and perspectives DOI

Jiahua Ni,

Yanjun Wen,

Donglai Pan

et al.

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 473, P. 145162 - 145162

Published: Aug. 2, 2023

Language: Английский

Citations

60
Recent Advances in Diverse MXenes‐Based Structures for Photocatalytic CO2 Reduction into Renewable Hydrocarbon Fuels DOI
Qijun Tang, Tianhao Li, Wenguang Tu

et al.

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: 34(19)

Published: Jan. 17, 2024

Abstract Photocatalytic CO 2 reduction into renewable hydrocarbon fuels is a green solution to address emission and energy issues simultaneously. However, the fast recombination of photogenerated charge carriers sluggish surface reaction kinetics restrict efficiency photocatalytic reduction. The emergence 2D MXenes has potential in improving reduction, owing their high electrical conductivity, flexible structural properties, abundant active sites. Hence, this review will concisely summarize highlight recent advances MXenes‐based photocatalysts used First, synthesis properties briefly introduced. Second, mechanism photoreduction along with roles are summarized, including promoting adsorption , enhancing separation photo‐induced carriers, acting as robust support, photothermal effect. Third, different kinds such MXenes/metal oxides, MXenes/nitrides, MXenes/LDH, MXenes/perovskite, MXene‐derived for classified via type semiconductors. Finally, challenges perspectives also presented, exploring suitable machine learning, uncovering structure‐activity relationship by situ, time‐ space‐resolved characterization techniques, anti‐oxidization ability, scale‐up applications.

Language: Английский

Citations

22
Size dependent photogeneration charge carrier density of hollow ZnIn2S4 microspheres for enhanced photocatalytic activity DOI
Pengfei Zhang, Jie Zhao, Chenyu Lu

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 489, P. 151331 - 151331

Published: April 15, 2024

Language: Английский

Citations

17
Strategies to improve the photocatalytic performance of covalent triazine frameworks DOI
Yubing Liu, Hao Wu, Qian Wang

et al.

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 11(40), P. 21470 - 21497

Published: Jan. 1, 2023

Various strategies for improving the photocatalytic performance of covalent triazine frameworks, including molecular design, structural regulation and creation heterostructures, are summarized.

Language: Английский

Citations

36
Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal DOI
Chang‐Min Kim, Zeeshan Haider Jaffari, Ather Abbas

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 465, P. 132995 - 132995

Published: Nov. 11, 2023

Language: Английский

Citations

33
MatGPT: A Vane of Materials Informatics from Past, Present, to Future DOI
Zhilong Wang, An Chen, Kehao Tao

et al.

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(6)

Published: Oct. 10, 2023

Abstract Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, informatics is continuously accelerating the vigorous development of new materials. The emergence “GPT (Generative Pre‐trained Transformer) AI” shows that scientific research field has entered era intelligent civilization with “data” as basic factor “algorithm + computing power” core productivity. continuous innovation AI will impact cognitive laws methods, reconstruct knowledge wisdom system. This leads to think more about informatics. Here, a comprehensive discussion models infrastructures provided, advances in discovery design are reviewed. With rise paradigms triggered by “AI for Science”, vane informatics: “MatGPT”, proposed technical path planning from aspects data, descriptors, generative models, pretraining directed collaborative training, experimental robots, well efforts preparations needed develop generation informatics, carried out. Finally, challenges constraints faced discussed, order achieve digital, intelligent, automated construction joint interdisciplinary scientists.

Language: Английский

Citations

30
Combination of ensemble machine learning models in photocatalytic studies using nano TiO2 - Lignin based biochar DOI

K C Abhayasimha,

Chinta Sankar Rao, Vaishakh Nair

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141326 - 141326

Published: Jan. 30, 2024

Language: Английский

Citations

10
Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review DOI
Mir Mohammad Ali, Md. Arif Hossen, Azrina Abd Aziz

et al.

Next Materials, Journal Year: 2025, Volume and Issue: 8, P. 100522 - 100522

Published: Feb. 10, 2025

Language: Английский

Citations

1
Predictive Modeling of Photocatalytic Hydrogen Production: Integrating Experimental Insights with Machine Learning on Fe/g-C3N4 Catalysts DOI Creative Commons
Bahriyenur Arabacı, Rezan Bakır, Ceren Orak

et al.

Industrial & Engineering Chemistry Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Language: Английский

Citations

1
Advances in the optical and electronic properties and applications of bismuth-based semiconductor materials DOI
Peng Xia, Yuanjun Song, Yuze Liu

et al.

Journal of Materials Chemistry C, Journal Year: 2023, Volume and Issue: 12(5), P. 1609 - 1624

Published: Dec. 22, 2023

In recent years, bismuth-based semiconductors have become a research hotspot in the new semiconductor field due to their unique optical and electronic properties.

Language: Английский

Citations

22