Contribution of air-water interface in removing PFAS from drinking water: Adsorption, stability, interaction and machine learning studies

Understanding the Selective Removal of Perfluoroalkyl and Polyfluoroalkyl Substances via Fluorine-Fluorine Interactions: A Critical Review

As regulatory standards for per- and polyfluoroalkyl substances (PFAS) become increasingly stringent, innovative water treatment technologies are urgently demanded for effective PFAS removal. Reported sorbents often exhibit limited affinity for PFAS and are frequently hindered by competitive background substances. Recently, fluorinated sorbents (abbreviated as fluorosorbents) have emerged as a potent solution by leveraging fluorine-fluorine (F···F) interactions to enhance selectivity and efficiency in PFAS removal. This review delves into the designs and applications of fluorosorbents, emphasizing how F···F interactions improve PFAS binding affinity. Specifically, the existence of F···F interactions results in removal efficiencies orders of magnitude higher than other counterpart sorbents, particularly under competitive conditions. Furthermore, we provide a detailed analysis of the fundamental principles underlying F···F interactions and elucidate their synergistic effects with other sorption forces, which contribute to the enhanced efficacy and selectivity. Subsequently, we examine various fluorosorbents and their synthesis and fluorination techniques, underscore the importance of accurately characterizing F···F interactions through advanced analytical methods, and emphasize the significance of this interaction in developing selective sorbents. Finally, we discuss challenges and opportunities associated with employing advanced techniques to guide the design of selective sorbents and advocate for further research in the development of sustainable and cost-effective treatment technologies leveraging F···F interactions.

Predicting the performance of lithium adsorption and recovery from unconventional water sources with machine learning

Selective lithium (Li) recovery from unconventional water sources (UWS) (e.g., shale gas waters, geothermal brines, and rejected seawater desalination brines) using inorganic lithium-ion sieve (LIS) materials can address Li supply shortages and distribution issues. However, the development of high-performance LIS materials and the optimization of recovery-related operating parameters are hampered by the variety of production methods, intricate procedures, and experimental expenses. Machine learning (ML) techniques offer potential solutions for enhancing LIS material development. We collected literature data on Li adsorption, categorizing 16 parameters into adsorbent parameters, operating parameters, and solution components. Three tree-based algorithms-Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)-were used to evaluate the impact of these parameters on lithium adsorption. The grouped random splitting method limited data leakage and mitigated overfitting. XGBoost demonstrated the best performance, with an R² of 0.98 and a root-mean-squared error (RMSE) of 1.72. The SHAP values highlighted that operating parameters were the most influential, followed by adsorbent parameters and coexisting ion concentrations. Therefore, focusing on optimizing operating parameters or making targeted improvements on LIS based on operating conditions will enhance LIS performances in UWS. These insights are crucial for optimizing Li adsorption processes and designing effective inorganic LIS materials.

Insights into the silica scaling behaviors in membrane distillation and anti-scaling mechanism of functional polymers

Silica scaling imposes a significant limitation on the efficacy of membrane distillation (MD) in the treatment of hypersaline wastewater. The complex dynamic behaviors of silica at the membrane-water-air interface and the poor understanding of molecular-level anti-scaling mechanism hampers the development of effective antiscalants for mitigating silica scaling in MD. Despite using functional polymers to prevent silica polymerization, the inhibition mechanisms are unclear. Here, the kinetic process of silica scaling during MD and the potential anti-scaling mechanism of poly-ethylenimine (PEI) were investigated at the molecular level via molecular dynamics simulations. The investigation reveals that silica scales were more likely to adhere to the water-PTFE interface with a free energy potential well of -40.0 kJ mol-1 than that of the water-air interface with a -11.4 kJ mol-1 potential well. Silica scales falling at the water-air interface also migrated on the water-air interface until captured by the PTFE membrane. In this work, a representative functional amino-rich polymer PEI was constructed as silica inhibitors and its scale inhibition mechanism was elucidated. Notably, the inclusion of PEI increased the free-energy barriers for the silica polymerization reaction from 72.0 kJ mol-1 to 86.1 kJ mol-1, compared to scenarios without the antiscalants. Moreover, quantitative structure-activity relationships (QSAR) model of ΔGwater-silica was developed to predict the anti-scaling efficiencies of typical antiscalants based on machine learning method. These findings provide valuable insights into enhancing the efficiency of silica scaling mitigation strategies.

The investigation of the enrichment behavior of identified PFAS and unknown PFAA-precursors in water and suspended particulate matter of the surface microlayer: A case study in Tianjin (China)

The surface microlayer (SML) is an important air water interface layer, known as the skin of the ocean, which has chemical enrichment properties. Chemical enrichment in the SML can affect the occurrence of pollutants in the underlying water and air samples. Although the enrichment of per- and polyfluorinated substances (PFAS), a class of persistent organic pollutants of high concern, has been reported in the SML, information on the behavior of unknown PFAA-precursors in SML is lacked, and it is not clear whether there is a similar PFAS enrichment in suspended particulate matter (SPM) in the SML. Therefore, to investigate these questions, we conducted a systematic survey of 24 PFAS in 11 paired water and SPM samples from the SML and underlying water (U50cm and U2m) from the Duliujian River, which flows to the Bohai sea in Tianjin, China. The ∑PFAS mean concentrations in the water and SPM samples were 38.2 ng/L and 64.6 ng/g dw, respectively. The PFAS concentrations of PFAS in the SML were higher than those in the underlying water, and the enrichment factors (EFs) were greater in the SPM than that in the water. The long-chain PFAS EFs were greater than those for short-chain PFAS, indicating that the EFs were positively correlated with the hydrophobicity. Moreover, by applying the total oxidizable precursor (TOP) assay, the unknown PFAA-precursors (C5-C12) in the water and SPM contributed 11.4∼86.4 mol% and 7.1∼88.0 mol% to total PFAS, respectively. The ecological risk of the targeted PFAS in the SML was relatively higher than that in the underlying water, indicating that PFAS in the SML require more attention. Preliminary estimates indicate that the PFAS-enriched SML is an important exposure route that poses a potential risk to wildlife in rivers and oceans.

Preferential Partitioning of Per- and Polyfluoroalkyl Substances (PFAS) and Dissolved Organic Matter in Freshwater Surface Microlayer and Natural Foam

Per- and polyfluoroalkyl substances (PFAS) are surfactants that can accumulate in the surface microlayer (SML) and in natural foams, with potential elevated exposure for organisms at the water surface. However, the impact of water chemistry on PFAS accumulation in these matrices in freshwater systems is unknown. We quantified 36 PFAS in water, the SML, and natural foams from 43 rivers and lakes in Wisconsin, USA, alongside measurements of pH, cations, and dissolved organic carbon (DOC). PFAS partition to foams with concentration ranging 2300-328,200 ng/L in waters with 6-139 ng/L PFAS (sum of 36 analytes), corresponding to sodium-normalized enrichment factors ranging <50 to >7000. Similar enrichment is observed for DOC (∼70). PFAS partitioning to foams increases with increasing chain length and is positively correlated with [DOC]. Modest SML enrichment is observed for PFOS (1.4) and FOSA (2.4), while negligible enrichment is observed for other PFAS and DOC due to low specific surface area and turbulent conditions that inhibit surfactant accumulation. However, DOC composition in the SML is distinct from bulk water, as assessed using high-resolution mass spectrometry. This study demonstrates that natural foams in unimpacted and impacted waters can have elevated PFAS concentrations, whereas SML accumulation in surface waters is limited.

Insights into PFAS environmental fate through computational chemistry: A review

Per- and polyfluoroalkyl substances (PFAS) are widely used chemicals that exhibit exceptional chemical and thermal stability. However, their resistance to degradation has led to their widespread environmental contamination. PFAS also negatively affect the environment and other organisms, highlighting the need for effective remediation methods to mitigate their presence and prevent further contamination. Computational chemistry methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD) offer valuable tools for studying PFAS and simulating their interactions with other molecules. This review explores how computational chemistry methods contribute to understanding and tackling PFAS in the environment. PFAS have been extensively studied using DFT and MD, each method offering unique advantages and computational limitations. MD simulates large macromolecules systems however it lacks the ability model chemical reactions, while DFT provides molecular insights however at a high computational cost. The integration of DFT with MD shows promise in predicting PFAS behavior in different environments. This work summarizes reported studies on PFAS compounds, focusing on adsorption, destruction, and bioaccumulation, highlighting contributions of computational methods while discussing the need for continued research. The findings emphasize the importance of computational chemistry in addressing PFAS contamination, guiding risk assessments, and informing future research and innovations in this field.

Molecular insights into the adsorption and penetration of oil droplets on hydrophobic membrane in membrane distillation

Membrane fouling induced by oily substances significantly constrains membrane distillation performance in treating hypersaline oily wastewater. Overcoming this challenge necessitates a heightened fundamental understanding of the oil fouling phenomenon. Herein, the adsorption and penetration mechanism of oil droplets on hydrophobic membranes in membrane distillation process was investigated at the molecular level. Our results demonstrated that the adsorption and penetration of oil droplets were divided into four stages, including the free stage, contact stage, spreading stage, and equilibrium stage. Due to the extensive non-polar surface distribution of the polytetrafluoroethylene (PTFE) membrane (comprising 95.41 %), the interaction between oil molecules and PTFE was primarily governed by van der Waals interaction. Continuous oil droplet membrane fouling model revealed that the new oil droplet molecules preferred to penetrate into membrane pores where oil droplets already existed. The penetration of resin (a component of medium-quality oil droplets) onto PTFE membrane pores required the "pre-paving" of light crude oil. Finally, the ΔE quantitative structure-activity relationships (QSAR) models were developed to evaluate the penetration mechanism of pollutant molecules on the PTFE membrane. This research provides new insights for improving sustainable membrane distillation technologies in treating saline oily wastewater.

Insights into the pH-dependent interactions of sulfadiazine antibiotic with soil particle models

Sulfonamide antibiotics (SAs) are widely used as a broad-spectrum antibiotic, leading to global concerns due to their potential soil accumulation and subsequent effects on ecosystems. SAs often exhibit remarkable environmental persistence, necessitating further investigation to uncover the ultimate destiny of these molecules. In this work, molecular dynamics simulations combined with complementary quantum chemistry calculations were employed to investigate the influence of pH on the behavior of sulfadiazine (SDZ, a typical SAs) in soil particle models (silica, one of the main components of soil). Meanwhile, the quantification of SDZ molecules aggregation potential onto silica was further extended. SDZ molecules tend to form a monolayer on the soil surface under acidic conditions while forming aggregated adsorption on the surface under neutral conditions. Due to the hydrophilicity of the silica, multiple hydration layers would form on its surface, hindering the further adsorption of SDZ molecules on its surface. The calculated soil-water partition coefficient (Psoil/water) of SDZ+ and SDZ were 9.01 and 7.02, respectively. The adsorption evaluation and mechanisms are useful in controlling the migration and transformation of SAs in the soil environment. These findings provide valuable insights into the interactions between SDZ and soil components, shedding light on its fate and transport in the environment.

Theoretical investigation of asphaltene molecules in crude oil viscoelasticity enhancement

Understanding the mechanisms of viscosity enhancement in crude oil phases is crucial for optimizing extraction and transportation processes. The enhanced viscosity mechanism of crude oil phase can be attributed to the intricate intermolecular interactions between asphaltene molecules. However, the molecular mechanism of the viscosification of asphaltene molecules in crude oil is not yet to be fully understood. In this work, molecular dynamics simulations were employed to investigate the dynamic behavior and viscosification mechanism of asphaltene molecules in complex oil phases. Research suggests that the neutral surface of asphaltenes features abundant positive and negative electrostatic potential regions, facilitating complementary pairing between these areas. This significantly augments electrostatic interactions among asphaltene molecules. Besides, the expansive nonpolar expanse on the normal asphaltene surface facilitates interactions between asphaltenes and crude oil molecules. This leads the crude oil viscosity of the system containing normal asphaltene is higher than that of the system containing acidic asphaltene under the same mass fraction (382 μ Pa·s for AAsp and 416 μ Pa·s for NAsp). This work provides insight into the viscosity enhancement mechanisms in crude oil phases and is helpful in improving the efficiency of crude oil extraction and transportation.