Elucidating per- and polyfluoroalkyl substances (PFASs) soil-water partitioning behavior through explainable machine learning models.

In this study, an optimized random forest (RF) model was employed to better understand the soil-water partitioning behavior of per- and polyfluoroalkyl substances (PFASs). The model demonstrated strong predictive performance, achieving an R2 of 0.93 and an RMSE of 0.86. Moreover, it required only 11 easily obtainable features, with molecular weight and soil pH being the predominant factors. Using three-dimensional interaction analyses identified specific conditions associated with varying soil-water partitioning coefficients (Kd). Results showed that soils with high organic carbon (OC) content, cation exchange capacity (CEC), and lower soil pH, especially when combined with PFASs of higher molecular weight, were linked to higher Kd values, indicating stronger adsorption. Conversely, low Kd values (< 2.8 L/kg) typically observed in soils with higher pH (8.0), but lower CEC (8 cmol+/kg), lesser OC content (1 %), and lighter molecular weight (380 g/mol), suggested weaker adsorption capacities and a heightened potential for environmental migration. Furthermore, the model was used to predict Kd values for 142 novel PFASs in diverse soil conditions. Our research provides essential insights into the factors governing PFASs partitioning in soil and highlights the significant role of machine learning models in enhancing the understanding of environmental distribution and migration of PFASs.

Spontaneous Degradation of the "Forever Chemicals" Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) on Water Droplet Surfaces.

Because of their innate chemical stability, the ubiquitous perfluoroalkyl and polyfluoroalkyl substances (PFASs) have been dubbed "forever chemicals" and have attracted considerable attention. However, their stability under environmental conditions has not been widely verified. Herein, perfluorooctanoic acid (PFOA), a widely used and detected PFAS, was found to be spontaneously degraded in aqueous microdroplets under room temperature and atmospheric pressure conditions. This unexpected fast degradation occurred via a unique multicycle redox reaction of PFOA with interfacial reactive species on the droplet surface. Similar degradation was observed for other PFASs. This study extends the current understanding of the environmental fate and chemistry of PFASs and provides insight into aid in the development of effective methods for removing PFASs.

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning.

With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.

Enhancement of Atmospheric Nucleation Precursors on Iodic Acid-Induced Nucleation: Predictive Model and Mechanism.

Iodic acid (IA) has recently been recognized as a key driver for new particle formation (NPF) in marine atmospheres. However, the knowledge of which atmospheric vapors can enhance IA-induced NPF remains limited. The unique halogen bond (XB)-forming capacity of IA makes it difficult to evaluate the enhancing potential (EP) of target compounds on IA-induced NPF based on widely studied sulfuric acid systems. Herein, we employed a three-step procedure to evaluate the EP of potential atmospheric nucleation precursors on IA-induced NPF. First, we evaluated the EP of 63 precursors by simulating the formation free energies (ΔG) of the IA-containing dimer clusters. Among all dimer clusters, 44 contained XBs, demonstrating that XBs are frequently formed. Based on the calculated ΔG values, a quantitative structure-activity relationship model was developed for evaluating the EP of other precursors. Second, amines and O/S-atom-containing acids were found to have high EP, with diethylamine (DEA) yielding the highest potential to enhance IA-induced nucleation by combining both the calculated ΔG and atmospheric concentration of considered 63 precursors. Finally, by studying larger (IA)1-3(DEA)1-3 clusters, we found that the IA-DEA system with merely 0.1 ppt (2.5×106 cm-3) DEA yields comparable nucleation rates to that of the IA-iodous acid system.

Lighting Nanoscale Insulators by Steric Restriction-Induced Emissions.

Luminescence detection is a sensitive approach for high-resolution visualization of nano-/macrosized objects, but it is challenging to light invisible insulators owing to their inert surfaces. Herein, we discovered a steric restriction-induced emission (SRIE) effect on nanoscale insulators to light them by fluorogenic probes. The SRIE effect enabled us to specifically differentiate a representative nanoscale insulator, boron nitride (BN) nanosheets, from 18 tested nanomaterials with 420-fold increments of photoluminescence intensity and displayed 3 orders of magnitude linearity for quantitative analysis as well as single-particle level detection. Molecular dynamics simulations indicated that the hydrophobic and electron-resistant surfaces of BN nanosheets restricted intramolecular motions of fluorogenic molecules for blockage of the nonradiative path of excited electrons and activation of the radiative electron transition. Moreover, the lighted BN nanosheets could be successfully visualized in complex cellular and tissue biocontexts. Overall, the SRIE effect will inspire more analytical techniques for inert materials.

Simulating and Predicting Adsorption of Organic Pollutants onto Black Phosphorus Nanomaterials.

Layered black phosphorus (BP) has exhibited exciting application prospects in diverse fields. Adsorption of organics onto BP may influence environmental behavior and toxicities of both organic pollutants and BP nanomaterials. However, contributions of various intermolecular interactions to the adsorption remain unclear, and values of adsorption parameters such as adsorption energies (Ead) and adsorption equilibrium constants (K) are lacking. Herein, molecular dynamic (MD) and density functional theory (DFT) was adopted to calculate Ead and K values. The calculated Ead and K values for organics adsorbed onto graphene were compared with experimental ones, so as to confirm the reliability of the calculation methods. Polyparameter linear free energy relationship (pp-LFER) models on Ead and logK were developed to estimate contributions of different intermolecular interactions to the adsorption. The adsorption in the gaseous phase was found to be more favorable than in the aqueous phase, as the adsorbates need to overcome cohesive energies of water molecules onto BP. The affinity of the aromatics to BP was comparable to that of graphene. The pp-LFER models performed well for predicting the Ead and K values, with external explained variance ranging from 0.90 to 0.97, and can serve as effective tools to rank adsorption capacities of organics onto BP.

Organic acid-ammonia ion-induced nucleation pathways unveiled by quantum chemical calculation and kinetics modeling: A case study of 3-methyl-1,2,3-butanetricarboxylic acid.

Nucleation of organic acids (OAs) and H2SO4 is an important source for new particle formation in the atmosphere. However, it is still unclear whether organic acids can produce nanoparticles independent of H2SO4. In this study, 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA) was adopted as a model of OAs. Pathways of clustering from MBTCA, ammonia and ions (NH4+ and NO3-) to form a 1.9 nm nucleus were investigated by quantum chemical calculation and kinetic modeling. Results show recombination of charged clusters/ions plays an essential role in the nucleation processes. Cluster formation rates increase by a factor of 103 when NH3 increases from 2.6 × 108 molecules·cm-3 (under clean conditions) to 2.6 × 1011 molecules·cm-3 (under polluted conditions), as NH3 can stabilize MBTCA clusters and change ion compositions from H3O+ to NH4+. Although the proposed new mechanism cannot compete with H2SO4-NH3-H2O or H2SO4-OA nucleation currently, it may be important in the future with the decline of SO2 concentration.

Opposite pH-dependent roles of hydroxyl radicals in ozonation and UV photolysis of genistein.

Phytoestrogens were frequently detected in municipal or industrial wastewater, and raised great attentions due to potential risks to humans or organisms. Until now, transformation mechanisms of phytoestrogens in advanced wastewater treatments were largely unknown. Here, pH influence mechanisms on transformations of phytoestrogens during two typical advanced wastewater treatments (ozonation and photolysis) were investigated, employing genistein (Gs) as a case. Removal efficiencies of Gs decreased significantly with increases of pH during ozonation, while photolytic rates increased by 44 or 200 times from pH 4.9 to 11.6 under irradiations without or with UVC. pH increases caused both dissociation of Gs and formation of hydroxyl radicals (OH) in ozonation or photolysis, however, led to opposite changes to degradation rates. This was because that OH played negatively as a competitor for O3 in ozonation, but acted as an accelerating species inducing self-sensitized photooxidation of Gs under UV light. Ozonation and photolytic products of Gs were similar at pH 4.9 or 8.6, but were totally different at pH 11.6. Most of the transformation products maintained isoflavone structures, and might possess phytoestrogenic effects. This study provided a deep insight into the pH influencing mechanism on typical advanced wastewater treatment processes of phytoestrogens. MAIN FINDING OF THE WORK: Opposite pH-dependent degradation mechanisms caused by hydroxyl radicals (OH) were elucidated for ozonation and UV photolysis of phytoestrogens, taking genistein as a case.