Search for: All records

GenX, the trade name of hexafluoropropylene oxide dimer acid (HFPO-DA) and its ammonium salt, is a short-chain PFAS that has emerged as a substitute for the legacy PFAS perfluorooctanoic acid (PFOA). However, GenX has turned out to be more toxic than people originally thought. In order to monitor and regulate water quality according to recently issued drinking water standards for GenX, rapid and ultrasensitive detection of GenX is urgently needed. For the first time, this study reports ultrasensitive (as low as 1 part per billion (ppb)) and fast detection (in minutes) of GenX in water via surface-enhanced Raman spectroscopy (SERS) using a hierarchical nanofibrous SERS substrate, which was prepared by assembling ~60 nm Ag nanoparticles on electrospun nylon-6 nanofibers through a “hot start” method. The findings in this research highlight the potential of the engineered hierarchical nanofibrous SERS substrate for enhanced detection of short-chain PFASs in water, contributing to the improvement of environmental monitoring and management strategies for PFASs. 

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, integrating key parameters such as pH, pHpzc, surface area, temperature, and PFAS molecular properties. RSM was employed to model adsorption behavior, while gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGB) were used to enhance predictive performance. Hybrid models—linear, RMSE-based, multiplicative, and meta-learning—were developed and evaluated. The meta-learning HOP-RSM-GB model achieved near-perfect accuracy (R² = 1.00, RMSE = 10.59), outperforming all other models. Surface plots revealed that low pH and high pHpzc maximized the adsorption while increasing log Kow consistently enhanced PFAS adsorption. These findings establish hybrid RSM-ML modeling as a powerful framework for optimizing PFAS remediation strategies. The integration of statistical and machine learning approaches significantly improves predictive accuracy, reduces experimental costs, and provides deeper insights into adsorption mechanisms. This study underscores the importance of data-driven approaches in environmental engineering and highlights future opportunities for integrating ML-driven modeling with experimental adsorption research.