ArticleNature communications2024
Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Insights into the Possible Impact of PFAS on Phosphatidylcholine Metabolism and Membranes through Molecular Dynamics Simulations.ACS environmental Au · 2026Article
- Mechanistic Insights into PFAS Rejection in Nanofiltration and Reverse Osmosis from Data-Driven Analysis.Environmental science & technology · 2026Article
- Advances in electrochemical technologies for PFAS destruction.Chemical science · 2026Review
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.Environmental science & technology · 2026Review
- Insights on the Adsorption of Per- and Polyfluoroalkyl Substances onto Laboratory Syringe Membrane Filters: Experimental, Materials, and Mechanism Evaluations.ACS ES&T water · 2026Article
- Accelerate Flash Removal of PFAS from Soil by Human-Guided Bayesian Optimization and Interpretable Machine Learning.ACS nano · 2026Article
- Analytical Tools for Understanding Molecular Level Interactions of Per- and Polyfluoroalkyl Substances (PFAS) with Sorbent Materials.Analytical chemistry · 2026Review
- Understanding and modelling ammonia partitioning and transport across reverse osmosis membrane.Nature communications · 2026Article
- Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.Scientific reports · 2026Article
- High-throughput design of bimetallic materialsChemical science · 2025Article
- Comparative Analysis of Commercial and Novel High-Pressure Membranes for Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) Removal.Water environment research : a research publication of the Water Environment Federation · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Multi-omics characterization of RNF157 expression patterns in hepatocellular carcinoma and development of an RNF157-associated prognostic signature.Frontiers in pharmacology · 2025Article
- Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heat maps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.
Identifiers
What OpenQuestion holds
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.