ArticleEnvironmental science & technology2020
Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials.
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 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- A concept for the molecular design of readily treatable chemicals.RSC sustainability · 2026Review
- Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.Biodegradation · 2024Article
- Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers.Environmental science & technology · 2024Article
- Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.Materials (Basel, Switzerland) · 2024Review
- Bioretention Design Modifications Increase the Simulated Capture of Hydrophobic and Hydrophilic Trace Organic Compounds.Environmental science & technology · 2024Article
- Modeling sorption of environmental organic chemicals from water to soils.Water research X · 2024Article
- Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques.Molecules (Basel, Switzerland) · 2023Article
- Predicting the speciation of ionizable antibiotic ciprofloxacin by biochars with varying carbonization degrees.RSC advances · 2023Article
- Effect of pH-Dependent Homo/Heteronuclear CAHB on Adsorption and Desorption Behaviors of Ionizable Organic Compounds on Carbonaceous Materials.International journal of environmental research and public health · 2022Article
- A review of the application of machine learning in water quality evaluation.Eco-Environment & Health · 2022Review
- Sorption and Mobility of Charged Organic Compounds: How to Confront and Overcome Limitations in Their Assessment.Environmental science & technology · 2022Article
- 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
- Modern Carbon-Based Materials for Adsorptive Removal of Organic and Inorganic Pollutants from Water and Wastewater.Molecules (Basel, Switzerland) · 2021Review
- MIL-101(Fe)-derived magnetic porous carbon as sorbent for stir bar sorptive-dispersive microextraction of sulfonamides.Mikrochimica acta · 2021Article
- Rational Design of Molecularly Imprinted Polymers Using Quaternary Ammonium Cations for Glyphosate Detection.Sensors (Basel, Switzerland) · 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
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log
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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.