Research graph
References from Machine learning enabled kinetic prediction and inverse design of sonoelectrochemical oxidation: Molecular descriptor integration, applicability domain assessment, and experimental verification. Local targets link to admitted publications; unresolved targets remain external evidence.
Pharmaceuticals and personal care products (PPCPs): a review on environmental contamination in China
10.1016/j.envint.2013.06.012 · 2013 · External reference
Pharmaceutical and Personal Care Products (PPCPs) in the environment: plant uptake, translocation, bioaccumulation, and human health risks
10.1080/10643389.2020.1753634 · 2021 · External 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 · 2008 · External 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 · 2020 · External reference
Efficient sonoelectrochemical decomposition of chlorpyrifos in aqueous solution
10.1016/j.microc.2018.10.032 · 2019 · External reference
Ultrasonically enhanced electrochemical oxidation of ibuprofen
10.1016/j.ultsonch.2014.04.019 · 2015 · External reference
Sonoelectrochemistry: ultrasound-assisted organic electrosynthesis
10.1021/acssuschemeng.1c02989 · 2021 · External reference
Electrochemical and sonochemical advanced oxidation processes applied to tartrazine removal. Influence of operational conditions and aqueous matrix
10.1016/j.envres.2021.111517 · 2021 · External reference
Sonoelectrochemical degradation of the anti-inflammatory drug diclofenac in water
10.1016/j.cej.2015.03.070 · 2015 · External reference
Degradation of diclofenac through ultrasonic-based advanced oxidation processes at low frequency
10.1016/j.jece.2022.108296 · 2022 · External reference
Machine learning approaches for the prediction of materials properties
10.1063/5.0018384 · 2020 · External reference
Flood prediction using machine learning models: literature review
10.3390/w10111536 · 2018 · External reference
Comparing different supervised machine learning algorithms for disease prediction
10.1186/s12911-019-1004-8 · 2019 · External reference
Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning
10.1016/j.chemosphere.2024.142701 · 2024 · External reference
Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
10.1016/j.cej.2023.147266 · 2023 · External reference
Machine learning model to predict rate constants for sonochemical degradation of organic pollutants
10.1016/j.ultsonch.2024.107032 · 2024 · External reference
Machine learning-guided optimization of UV/chlorine process for sustainable micropollutant abatement
10.1016/j.jhazmat.2026.141711 · 2026 · External reference
Machine learning models for inverse design of the electrochemical oxidation process for water purification
10.1021/acs.est.2c08771 · 2023 · External reference
Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images
10.1021/acs.jcim.8b00338 · 2018 · External reference
Environmental impacts prediction using graph neural networks on molecular graphs
10.1016/j.compchemeng.2025.109362 · 2026 · External reference
Hierarchical machine learning-based prediction for ultrasonic degradation of organic pollutants using sonocatalysts
10.1016/j.envres.2025.122500 · 2025 · External reference
Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)
10.1016/j.watres.2023.121097 · 2024 · External 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 · 2026 · External 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 · 2021 · External reference
Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron
10.1016/j.jwpe.2023.104303 · 2023 · External reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · 2025 · External reference
Unresolved reference
External reference
Biochar-based persulfate activation: rate constant prediction, key variables identification, and system optimization
10.1016/j.jwpe.2024.105839 · 2024 · External reference
Tabular data: deep learning is not all you need
10.1016/j.inffus.2021.11.011 · 2022 · External reference
Deep neural networks and tabular data: a survey
10.1109/tnnls.2022.3229161 · 2024 · External reference
Unresolved reference
2016 · External reference
Sample size requirements for popular classification algorithms in tabular clinical data: empirical study
10.2196/60231 · 2024 · External 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 · 2020 · External reference
Explainable machine learning reveals how molecular descriptors govern micropollutant degradation in UV/H2O2 oxidation
10.1016/j.watres.2026.125796 · 2026 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020 · External reference
Evaluation of anode materials in sonoelectrochemistry processes: kinetic, mechanism, and cost estimation
10.1016/j.chemosphere.2022.135547 · 2022 · External 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 · 2023 · External reference
Sonoelectrochemical degradation of phenol in aqueous solutions
10.1016/j.ultsonch.2012.09.004 · 2013 · External 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 · 2025 · External reference
Effects of ultrasonic frequency and liquid height on sonochemical efficiency of large-scale sonochemical reactors
10.1016/j.ultsonch.2007.03.012 · 2008 · External reference
Degradation of caffeine by conductive diamond electrochemical oxidation
10.1016/j.chemosphere.2013.05.047 · 2013 · External reference
Electrochemical oxidation of paracetamol in water by graphite anode: Effect of pH, electrolyte concentration and current density
10.1016/j.jece.2018.08.036 · 2018 · External reference
Evaluation of anode materials in sonoelectrochemistry processes: kinetic, mechanism, and cost estimation
10.1016/j.chemosphere.2022.135547 · 2022 · External reference
Oxidative decomposition and mineralization of caffeine by advanced oxidation processes: the effect of hybridization
10.1016/j.ultsonch.2021.105635 · 2021 · External reference
Gradient boosting with piece-wise linear regression trees
2018 · External reference
Sonoelectrochemical degradation of the anti-inflammatory drug diclofenac in water
10.1016/j.cej.2015.03.070 · ExternalCitation · doi-reference
Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants
10.1016/j.cej.2023.147266 · ExternalCitation · doi-reference
Degradation of caffeine by conductive diamond electrochemical oxidation
10.1016/j.chemosphere.2013.05.047 · ExternalCitation · doi-reference
Evaluation of anode materials in sonoelectrochemistry processes: kinetic, mechanism, and cost estimation
10.1016/j.chemosphere.2022.135547 · ExternalCitation · doi-reference
Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning
10.1016/j.chemosphere.2024.142701 · ExternalCitation · doi-reference
Environmental impacts prediction using graph neural networks on molecular graphs
10.1016/j.compchemeng.2025.109362 · ExternalCitation · doi-reference
Pharmaceuticals and personal care products (PPCPs): a review on environmental contamination in China
10.1016/j.envint.2013.06.012 · ExternalCitation · 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 · ExternalCitation · doi-reference
Hierarchical machine learning-based prediction for ultrasonic degradation of organic pollutants using sonocatalysts
10.1016/j.envres.2025.122500 · ExternalCitation · doi-reference
Tabular data: deep learning is not all you need
10.1016/j.inffus.2021.11.011 · ExternalCitation · 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 · ExternalCitation · doi-reference
Electrochemical oxidation of paracetamol in water by graphite anode: Effect of pH, electrolyte concentration and current density
10.1016/j.jece.2018.08.036 · ExternalCitation · doi-reference
Degradation of diclofenac through ultrasonic-based advanced oxidation processes at low frequency
10.1016/j.jece.2022.108296 · ExternalCitation · 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 · ExternalCitation · doi-reference
Machine learning-guided optimization of UV/chlorine process for sustainable micropollutant abatement
10.1016/j.jhazmat.2026.141711 · ExternalCitation · doi-reference
Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron
10.1016/j.jwpe.2023.104303 · ExternalCitation · doi-reference
Biochar-based persulfate activation: rate constant prediction, key variables identification, and system optimization
10.1016/j.jwpe.2024.105839 · ExternalCitation · doi-reference
Efficient sonoelectrochemical decomposition of chlorpyrifos in aqueous solution
10.1016/j.microc.2018.10.032 · ExternalCitation · doi-reference
Effects of ultrasonic frequency and liquid height on sonochemical efficiency of large-scale sonochemical reactors
10.1016/j.ultsonch.2007.03.012 · ExternalCitation · doi-reference
Sonoelectrochemical degradation of phenol in aqueous solutions
10.1016/j.ultsonch.2012.09.004 · ExternalCitation · doi-reference
Ultrasonically enhanced electrochemical oxidation of ibuprofen
10.1016/j.ultsonch.2014.04.019 · ExternalCitation · doi-reference
Oxidative decomposition and mineralization of caffeine by advanced oxidation processes: the effect of hybridization
10.1016/j.ultsonch.2021.105635 · ExternalCitation · doi-reference
Machine learning model to predict rate constants for sonochemical degradation of organic pollutants
10.1016/j.ultsonch.2024.107032 · ExternalCitation · 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 · ExternalCitation · doi-reference
Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)
10.1016/j.watres.2023.121097 · ExternalCitation · doi-reference
Explainable machine learning reveals how molecular descriptors govern micropollutant degradation in UV/H2O2 oxidation
10.1016/j.watres.2026.125796 · ExternalCitation · 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 · ExternalCitation · doi-reference
Machine learning models for inverse design of the electrochemical oxidation process for water purification
10.1021/acs.est.2c08771 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images
10.1021/acs.jcim.8b00338 · ExternalCitation · 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 · ExternalCitation · doi-reference
Sonoelectrochemistry: ultrasound-assisted organic electrosynthesis
10.1021/acssuschemeng.1c02989 · ExternalCitation · doi-reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · ExternalCitation · doi-reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · ExternalCitation · doi-reference
Machine learning approaches for the prediction of materials properties
10.1063/5.0018384 · ExternalCitation · doi-reference
Pharmaceutical and Personal Care Products (PPCPs) in the environment: plant uptake, translocation, bioaccumulation, and human health risks
10.1080/10643389.2020.1753634 · ExternalCitation · doi-reference
Deep neural networks and tabular data: a survey
10.1109/tnnls.2022.3229161 · ExternalCitation · doi-reference
Comparing different supervised machine learning algorithms for disease prediction
10.1186/s12911-019-1004-8 · ExternalCitation · doi-reference
Sample size requirements for popular classification algorithms in tabular clinical data: empirical study
10.2196/60231 · ExternalCitation · doi-reference
Flood prediction using machine learning models: literature review
10.3390/w10111536 · ExternalCitation · doi-reference
