Abstract
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Yongyue Zhou, Mingcan Cui, Jeehyeong Khim, Jongbok Choi, Hongfeng Chen, Yangmin Ren
Abstract
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Institutions
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Pharmaceuticals and personal care products (PPCPs): a review on environmental contamination in China
10.1016/j.envint.2013.06.012 · 2013
Pharmaceutical and Personal Care Products (PPCPs) in the environment: plant uptake, translocation, bioaccumulation, and human health risks
10.1080/10643389.2020.1753634 · 2021
Adsorption characteristics of selected pharmaceuticals and an endocrine disrupting compound-Naproxen, carbamazepine and nonylphenol-on activated carbon
10.1016/j.watres.2008.02.020 · 2008
Membrane fouling in a powdered activated carbon – membrane bioreactor (PAC-MBR) for micro-polluted water purification: fouling characteristics and the roles of PAC
10.1016/j.jclepro.2020.122341 · 2020
Efficient sonoelectrochemical decomposition of chlorpyrifos in aqueous solution
10.1016/j.microc.2018.10.032 · 2019
Ultrasonically enhanced electrochemical oxidation of ibuprofen
10.1016/j.ultsonch.2014.04.019 · 2015
Sonoelectrochemistry: ultrasound-assisted organic electrosynthesis
10.1021/acssuschemeng.1c02989 · 2021
Electrochemical and sonochemical advanced oxidation processes applied to tartrazine removal. Influence of operational conditions and aqueous matrix
10.1016/j.envres.2021.111517 · 2021
Provenance
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Confidence 100%
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Degradation of diclofenac through ultrasonic-based advanced oxidation processes at low frequency
10.1016/j.jece.2022.108296 · 2022
Machine learning approaches for the prediction of materials properties
10.1063/5.0018384 · 2020
Flood prediction using machine learning models: literature review
10.3390/w10111536 · 2018
Comparing different supervised machine learning algorithms for disease prediction
10.1186/s12911-019-1004-8 · 2019
Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning
10.1016/j.chemosphere.2024.142701 · 2024
Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
10.1016/j.cej.2023.147266 · 2023
Machine learning model to predict rate constants for sonochemical degradation of organic pollutants
10.1016/j.ultsonch.2024.107032 · 2024
Machine learning-guided optimization of UV/chlorine process for sustainable micropollutant abatement
10.1016/j.jhazmat.2026.141711 · 2026
Machine learning models for inverse design of the electrochemical oxidation process for water purification
10.1021/acs.est.2c08771 · 2023
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10.1016/j.compchemeng.2025.109362 · 2026
Hierarchical machine learning-based prediction for ultrasonic degradation of organic pollutants using sonocatalysts
10.1016/j.envres.2025.122500 · 2025
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Quantification of the antagonistic and synergistic effects of Pb2+, Cu2+, and Zn2+ bioaccumulation by living Bacillus subtilis biomass using XGBoost and SHAP
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Combining group contribution method and semisupervised learning to build machine learning models for predicting hydroxyl radical rate constants of water contaminants
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Oxidative decomposition and mineralization of caffeine by advanced oxidation processes: the effect of hybridization
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10.1016/j.chemosphere.2013.05.047 · doi-reference
Effects of ultrasonic frequency and liquid height on sonochemical efficiency of large-scale sonochemical reactors
10.1016/j.ultsonch.2007.03.012 · doi-reference
Combining group contribution method and semisupervised learning to build machine learning models for predicting hydroxyl radical rate constants of water contaminants
10.1021/acs.est.4c11950 · doi-reference
Sonoelectrochemical degradation of phenol in aqueous solutions
10.1016/j.ultsonch.2012.09.004 · doi-reference
Quantification of the antagonistic and synergistic effects of Pb2+, Cu2+, and Zn2+ bioaccumulation by living Bacillus subtilis biomass using XGBoost and SHAP
10.1016/j.jhazmat.2022.130635 · doi-reference
Evaluation of anode materials in sonoelectrochemistry processes: kinetic, mechanism, and cost estimation
10.1016/j.chemosphere.2022.135547 · doi-reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · doi-reference
Explainable machine learning reveals how molecular descriptors govern micropollutant degradation in UV/H2O2 oxidation
10.1016/j.watres.2026.125796 · doi-reference
Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set?
10.1021/acs.jcim.9b00714 · doi-reference
Sample size requirements for popular classification algorithms in tabular clinical data: empirical study
10.2196/60231 · doi-reference
Deep neural networks and tabular data: a survey
10.1109/tnnls.2022.3229161 · doi-reference
Tabular data: deep learning is not all you need
10.1016/j.inffus.2021.11.011 · doi-reference
Biochar-based persulfate activation: rate constant prediction, key variables identification, and system optimization
10.1016/j.jwpe.2024.105839 · doi-reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · doi-reference
Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron
10.1016/j.jwpe.2023.104303 · doi-reference
“pySiRC”: machine learning combined with molecular fingerprints to predict the reaction rate constant of the radical-based oxidation processes of aqueous organic contaminants
10.1021/acs.est.1c04326 · doi-reference
Improving kinetic prediction and structural-electronic mechanistic coherence in the fenton process via a cross-scale machine-learning framework
10.1021/acs.est.5c18754 · doi-reference
Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)
10.1016/j.watres.2023.121097 · doi-reference
Hierarchical machine learning-based prediction for ultrasonic degradation of organic pollutants using sonocatalysts
10.1016/j.envres.2025.122500 · doi-reference
Environmental impacts prediction using graph neural networks on molecular graphs
10.1016/j.compchemeng.2025.109362 · doi-reference
Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images
10.1021/acs.jcim.8b00338 · doi-reference
Machine learning models for inverse design of the electrochemical oxidation process for water purification
10.1021/acs.est.2c08771 · doi-reference
Machine learning-guided optimization of UV/chlorine process for sustainable micropollutant abatement
10.1016/j.jhazmat.2026.141711 · doi-reference
Machine learning model to predict rate constants for sonochemical degradation of organic pollutants
10.1016/j.ultsonch.2024.107032 · doi-reference
Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
10.1016/j.cej.2023.147266 · doi-reference
Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning
10.1016/j.chemosphere.2024.142701 · doi-reference
Comparing different supervised machine learning algorithms for disease prediction
10.1186/s12911-019-1004-8 · doi-reference
Flood prediction using machine learning models: literature review
10.3390/w10111536 · doi-reference
Machine learning approaches for the prediction of materials properties
10.1063/5.0018384 · doi-reference
Degradation of diclofenac through ultrasonic-based advanced oxidation processes at low frequency
10.1016/j.jece.2022.108296 · doi-reference
Sonoelectrochemical degradation of the anti-inflammatory drug diclofenac in water
10.1016/j.cej.2015.03.070 · doi-reference
Electrochemical and sonochemical advanced oxidation processes applied to tartrazine removal. Influence of operational conditions and aqueous matrix
10.1016/j.envres.2021.111517 · doi-reference
Sonoelectrochemistry: ultrasound-assisted organic electrosynthesis
10.1021/acssuschemeng.1c02989 · doi-reference
Ultrasonically enhanced electrochemical oxidation of ibuprofen
10.1016/j.ultsonch.2014.04.019 · doi-reference
Efficient sonoelectrochemical decomposition of chlorpyrifos in aqueous solution
10.1016/j.microc.2018.10.032 · doi-reference
Membrane fouling in a powdered activated carbon – membrane bioreactor (PAC-MBR) for micro-polluted water purification: fouling characteristics and the roles of PAC
10.1016/j.jclepro.2020.122341 · doi-reference
Adsorption characteristics of selected pharmaceuticals and an endocrine disrupting compound-Naproxen, carbamazepine and nonylphenol-on activated carbon
10.1016/j.watres.2008.02.020 · doi-reference
Pharmaceutical and Personal Care Products (PPCPs) in the environment: plant uptake, translocation, bioaccumulation, and human health risks
10.1080/10643389.2020.1753634 · doi-reference