We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations
AI summary Read the abstract
Researchers used machine learning models to identify the key factors controlling how well polyamide membranes filter out PFAS — the so-called 'forever chemicals' that contaminate drinking water — finding that electrical charge interactions between the chemicals and the membrane are the dominant force. This approach offers a data-driven way to design better water filtration membranes for removing these persistent pollutants.
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.
More Papers Like This
Unveiling the adsorption mechanism of perfluorooctane sulfonate onto polypropylene nanoplastics: A combined theoretical and experimental investigation
AI summary Read the abstract
Researchers combined computer simulations with lab experiments to understand how PFOS, a widespread "forever chemical," attaches to polypropylene nanoplastic particles in water. They found that PFOS binds readily to the plastic surface, and the resulting combination moves more easily through water than the plastic particle alone, making it potentially more dangerous. Changes in water acidity (pH) can affect how much PFOS sticks to the plastic, influencing how these pollutants travel together through the environment.
Review of Recent Computational Research on the Adsorption of PFASs with a Variety of Substrates
AI summary Read the abstract
This review summarizes recent computer modeling research on how PFAS, sometimes called "forever chemicals," stick to various materials, which could help develop better cleanup methods. While focused on PFAS rather than microplastics, both are persistent environmental pollutants that resist breakdown and accumulate in the body. Understanding how these chemicals interact with surfaces at the molecular level could lead to more effective ways to remove them from contaminated water and soil.
Removal of per- and polyfluoroalkyl substances and nanoplastics from water using selected nanomaterial-based membranes: A review
AI summary Read the abstract
"Forever chemicals" (PFAS) and tiny plastic particles are showing up in drinking water and are linked to health problems, so scientists urgently need better ways to filter them out. This review pulls together research on cutting-edge filter materials—super-thin, specially engineered membranes—that show real promise for trapping these pollutants more effectively than current methods. The catch: these filters aren't ready for your kitchen faucet yet, since researchers still need to make them cheaper to produce, longer-lasting, and effective in real-world water that's also full of salts and other contaminants.
Modelling the fate of chemicals of emerging concern in wastewater treatment: From mechanistic models to hybrid data driven approaches
AI summary Read the abstract
Everyday chemicals from medicines, cleaning products, and plastics often slip through wastewater treatment plants and end up back in our environment, potentially in our water supply. This review looked at different computer modeling approaches—including newer AI-based methods—that scientists use to predict how well treatment plants remove these pollutants, finding that combining traditional science-based models with machine learning works best. The authors note we still need better tools to track particularly concerning substances like PFAS ("forever chemicals") and microplastics, which matters because understanding how well treatment plants filter these out helps protect the water we drink and use every day.
Rapidly Predicting Aqueous Adsorption Constants of Organic Pollutants onto Polyethylene Microplastics by Combining Molecular Dynamics Simulations and Machine Learning
AI summary Read the abstract
Researchers developed a computational method combining molecular simulations with machine learning to rapidly predict how organic pollutants adsorb onto polyethylene microplastics in water. The approach accurately predicted adsorption behavior across different conditions including particle size, water salinity, and pH without requiring time-consuming laboratory experiments. The tool could help environmental scientists more quickly assess how microplastics interact with and transport chemical contaminants in aquatic environments.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.