Establishing Quantitative Structure–Activity Relationships for the Degradation of Aromatic Organics by UV–H2O2 Using Machine Learning DOI

Zhongli Lu,

Jiming Liu, Xuqian Zhang

et al.

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

Published: March 26, 2025

The degradation of aromatic organic compounds in aquatic environments is critical due to their persistence and toxicity. This study establishes a machine learning (ML)-driven quantitative structure–activity relationship model predict the pseudo-first-order reaction rate constants (K) for UV–H2O2 organics. A data set comprising 134 experimental observations 30 was constructed, integrating conditions, quantum chemical parameters, physicochemical properties. Among six ML algorithms evaluated, gradient boosting decision tree emerged as optimal model, with feature importance analysis identifying H2O2 concentration, topological polar surface area, q(C)min dominant factors. Theoretical calculations supported by linking higher reactivity o,p'-dicofol lower energy gaps elevated electrophilic susceptibility. Additionally, establishment interpretable expressions not only provides transparency clarity predictions but also aids economic analysis, which highlighted that mildly acidic pH low UV light intensity, along suitable concentrations, are cost-effective conditions process. work bridges chemistry elucidate mechanisms, offering rapid resource-efficient tool optimizing advanced oxidation processes.

Language: Английский

Combining crystal planes and heterojunctions of ZnS/SnS2 boosts photocatalytic performance DOI

Zhengxu Jiao,

Xiaofeng Shuai,

Yuxuan Du

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(5)

Published: Feb. 3, 2025

A reasonable construction of hybrid heterogeneous photocatalysts possessing fast charge separation and transfer visible light utilization ability is great significance for enhancing achieving highly efficient photochemical conversion. In this study, a series ZnS/SnS2 heterojunction were prepared by morphology-controlled strategy, the regulation heterostructure was realized photodegradation methyl violet (MV) under light. The results show that as-prepared consists morphological microparticles exhibits nanoparticle/nanosheet interspersed structure. multi-touch interface constructed heterostructures can significantly shorten carrier transport path greatly promote action local electric field at semiconductor/semiconductor interface. Exposed dual-crystal planes SnS2 coupled with ZnS enrich active sites enhance performance pure material. degradation rate MV over optimal material (ZnS/SnS2-50) exceeds 91.4% within 90 min irradiation, which about 62 times compared pristine ZnS.

Language: Английский

Citations

2
MoS2/MgAl-LDH Composites for the Photodegradation of Rhodamine B Dye DOI Creative Commons
Jingjing Dai, Guofei Li, Yuanyuan Wang

et al.

Inorganics, Journal Year: 2025, Volume and Issue: 13(3), P. 88 - 88

Published: March 17, 2025

During the process of producing potassium fertilizer from salt lake resources, a large amount waste liquid brine, rich in raw materials such as magnesium chloride, is generated. In this work, MoS2/MgAl-LDH composite material was constructed using secondary hydrothermal technique. Characterizations including X-ray diffractometer (XRD), scanning electron microscopy (SEM), and photoelectron spectroscopy (XPS) confirmed distribution MoS2 nanosheets on surface MgAl-LDH. Under full-spectrum irradiation, degradation efficiency Rhodamine B reached 85.5%, which 69.2% higher than that MgAl-LDH alone. The results electrochemical, UV-Vis, XPS-VB tests indicate internal electric field accelerated separation transportation charge carriers between These findings demonstrate great potential photocatalyst organic dyes, will aid green recycling utilization resources by-products.

Language: Английский

Citations

0
Establishing Quantitative Structure–Activity Relationships for the Degradation of Aromatic Organics by UV–H2O2 Using Machine Learning DOI

Zhongli Lu,

Jiming Liu, Xuqian Zhang

et al.

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

Published: March 26, 2025

The degradation of aromatic organic compounds in aquatic environments is critical due to their persistence and toxicity. This study establishes a machine learning (ML)-driven quantitative structure–activity relationship model predict the pseudo-first-order reaction rate constants (K) for UV–H2O2 organics. A data set comprising 134 experimental observations 30 was constructed, integrating conditions, quantum chemical parameters, physicochemical properties. Among six ML algorithms evaluated, gradient boosting decision tree emerged as optimal model, with feature importance analysis identifying H2O2 concentration, topological polar surface area, q(C)min dominant factors. Theoretical calculations supported by linking higher reactivity o,p'-dicofol lower energy gaps elevated electrophilic susceptibility. Additionally, establishment interpretable expressions not only provides transparency clarity predictions but also aids economic analysis, which highlighted that mildly acidic pH low UV light intensity, along suitable concentrations, are cost-effective conditions process. work bridges chemistry elucidate mechanisms, offering rapid resource-efficient tool optimizing advanced oxidation processes.

Language: Английский

Citations

0