ArticleEnvironmental science & technology2020
Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning.
Article in Environmental science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution.ACS ES&T water · 2026Article
- Identifying the key environmental drivers of chlorophyll-a in the Danjiangkou reservoir using interpretable machine learning.Scientific reports · 2026Article
- Development of Machine Learning Models to Predict Daily Gas Ebullition Flux in Waterways from Sediment Characteristics.ACS environmental Au · 2026Article
- Adsorption Mechanisms and AI-Driven Discovery of Biomass-Based COSmall (Weinheim an der Bergstrasse, Germany) · 2025Review
- Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar.Scientific reports · 2025Article
- A machine learning framework to predict PPCP removal through various wastewater and water reuse treatment trains.Environmental science : water research & technology · 2025Article
- Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model.Frontiers in cardiovascular medicine · 2025Article
- Mathematical Expression and Prediction of VOCs Adsorption Capacity and Isotherm.Journal of the American Chemical Society · 2024Article
- Predicting removal of arsenic from groundwater by iron based filters using deep neural network models.Scientific reports · 2024Article
- Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers.Environmental science & technology · 2024Article
- In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV-vis Spectroscopy: Implication for Understanding of Heterogeneous Processes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Highly Efficient Adsorption of Norfloxacin by Low-Cost Biochar: Performance, Mechanisms, and Machine Learning-Assisted Understanding.ACS omega · 2024Article
- Active Learning-Based Guided Synthesis of Engineered Biochar for COEnvironmental science & technology · 2024Article
- Bioretention Design Modifications Increase the Simulated Capture of Hydrophobic and Hydrophilic Trace Organic Compounds.Environmental science & technology · 2024Article
- Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques.Molecules (Basel, Switzerland) · 2023Article
- Article
- Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon.ACS ES&T water · 2022Article
- Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex.Water research · 2021Article
- Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients.Frontiers in chemistry · 2021Article
- Comment on Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, And Resins with Machine Learning.Environmental science & technology · 2020Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.