AIMC Topic: Fluorocarbons

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Using Machine Learning to Predict First-Order Reaction Rate Constants of PFAS Degradation.

Bulletin of environmental contamination and toxicology
Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent pollutants, posing challenges for effective remediation. This study presented a machine learning (ML) framework to predict the first-order reaction rate constant (k) of PFAS de...

Advancing PFAS Detection through Machine Learning Prediction of F NMR Spectra.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants with diverse structures. To further advance the impact assessment and remediation technology for PFAS pollution, new approaches for identifying emerging PFAS are neces...

Identifying toxicological effects of perfluoroalkyl and polyfluoroalkyl substances exposure on osteoarthritis.

Scientific reports
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant challenge due to their persistence, bioaccumulation, and multi-system toxicity. Their impact on degenerative diseases, particularly osteoarthritis (OA), remains understudied, nec...

Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria.

Environmental science & technology
Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model ...

Bacterial cellulose for emerging contaminants: A review of applications for PFAS, nanoplastics, and endocrine disruptors in water treatment.

The Science of the total environment
Emerging contaminants, including per- and polyfluoroalkyl substances (PFAS), nanoplastics, and endocrine-disrupting chemicals (EDCs), pose significant threats to water quality due to their persistence, toxicity, and resistance to conventional treatme...

Generative AI-Empowered Screening Strategy for Chemical Pollutants: A Case on Per- and Polyfluoroalkyl Substances.

Environmental science & technology
Identifying unknown chemical pollutants is essential for effective risk management. However, current analytical techniques are restricted to structures covered by existing databases, capturing only the tip of the iceberg in the vast pollutant chemica...

Predicting PFAS Diffusion Coefficients with Active Learning and Molecular Dynamics.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFAS) are over 14 000 synthetic compounds with exceptional environmental persistence. Used extensively in industrial and consumer applications, PFAS resist degradation and accumulate in environmental media and liv...

Engineering a Metal-Organic Framework-Dominated Bioinspired Multienzymatic Sensor Array for Portable Detection of Perfluoroalkyl Substances.

Analytical chemistry
Accurate identification of perfluoroalkyl substances (PFASs) is essential for environmental regulation and public health protection. However, current analytical techniques struggle to differentiate PFASs due to their structural similarity. Herein, we...

Machine Learning Study of PFAS Intermediate Adsorption on Transition Metals: Scaling Relationships for Environmental Catalyst Design.

Environmental science & technology
Per- and polyfluoroalkyl substances (PFASs), particularly trifluoroacetic acid (TFA), have emerged as global environmental pollutants due to their extreme persistence. Conventional treatment methods are largely ineffective, underscoring the need for ...

Discovery of Comprehensive Sets of Chemical Constituents as Markers of PFAS Sources through a Nontarget Screening and Machine Learning Approach.

Environmental science & technology
The objective of this study was to identify chemical constituents as markers of six per- and polyfluoroalkyl substance (PFAS) sources including aqueous film-forming foam-impacted groundwater, landfill leachate, biosolids leachate, municipal wastewate...