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.

Counterintuitive Oxidation of Alcohols at Air-Water Interfaces.

This study shows that the oxidation of alcohols can rapidly occur at air-water interfaces. It was found that methanediols (HOCH2OH) orient at air-water interfaces with a H atom of the -CH2- group pointing toward the gaseous phase. Counterintuitively, gaseous hydroxyl radicals do not prefer to attack the exposed -CH2- group but the -OH group that forms hydrogen bonds with water molecules at the surface via a water-promoted mechanism, leading to the formation of formic acids. Compared with gaseous oxidation, the water-promoted mechanism at the air-water interface significantly lowers free-energy barriers from ∼10.7 to ∼4.3 kcal·mol-1 and therefore accelerates the formation of formic acids. The study unveils a previously overlooked source of environmental organic acids that are bound up with aerosol formation and water acidity.

Mechanistic Understanding of Superoxide Radical-Mediated Degradation of Perfluorocarboxylic Acids.

Perfluorocarboxylic acids (PFCAs) exhibit strong persistence in sunlit surface waters and in radical-based treatment processes, where superoxide radical (O2•-) is an important and abundant reactive oxygen species. Given that the role of O2•- during the transformation of PFCAs remains largely unknown, we investigated the kinetics and mechanisms of O2•--mediated PFCAs attenuation through complementary experimental and theoretical approaches. The aqueous-phase rate constants between O2•- and C3-C8 PFCAs were measured using a newly designed in situ spectroscopic system. Mechanistically, bimolecular nucleophilic substitution (SN2) is most likely to be thermodynamically feasible, as indicated by density functional theory calculations at the CBS-QB3 level of theory. This pathway was then investigated by ab initio molecular dynamics simulation with free-energy samplings. As O2•- approaches PFCA, the C-F bond at the alpha carbon is spontaneously stretched, leading to the bond cleavage. The solvation mechanism for O2•--mediated PFCA degradation was also elucidated. Our results indicated that although the less polar solvent enhanced the nucleophilicity of O2•-, it also decreased the desolvation process of PFCAs, resulting in reduced kinetics. With these quantitative and mechanistic results, we achieved a defined picture of the O2•--initiated abatement of PFCAs in natural and engineered waters.

Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry.

It is an important topic in environmental sciences to understand the behavior and toxicology of chemical pollutants. Quantum chemical methodologies have served as useful tools for probing behavior and toxicology of chemical pollutants in recent decades. In recent years, machine learning (ML) techniques have brought revolutionary developments to the field of quantum chemistry, which may be beneficial for investigating environmental behavior and toxicology of chemical pollutants. However, the ML-based quantum chemical methods (ML-QCMs) have only scarcely been used in environmental chemical studies so far. To promote applications of the promising methods, this Perspective summarizes recent progress in the ML-QCMs and focuses on their potential applications in environmental chemical studies that could hardly be achieved by the conventional quantum chemical methods. Potential applications and challenges of the ML-QCMs in predicting degradation networks of chemical pollutants, searching global minima for atmospheric nanoclusters, discovering heterogeneous or photochemical transformation pathways of pollutants, as well as predicting environmentally relevant end points with wave functions as descriptors are introduced and discussed.

Prediction Models on pKa and Base-Catalyzed Hydrolysis Kinetics of Parabens: Experimental and Quantum Chemical Studies.

Parabens for which the molecules contain hydrolytic and ionizable groups, are emerging pollutants due to their ubiquity in the environment. However, lack of pKa and second-order base-catalyzed hydrolysis kinetics (kB) values limits their environmental persistence assessment. Herein, six parabens were selected as reference compounds for which the pKa and kB values were measured experimentally. A semiempirical quantum chemical (QC) method was selected to calculate pKa of the parabens, and density functional theory (DFT) methods were selected to calculate kB for neutral and anionic forms of the parabens, by comparing the QC-calculated and determined values. Combining the QC-calculated and experimental pKa and kB values, quantitative structure-activity relationships with determination coefficients (R2) being 0.947 and 0.842 for the pKa and kB models, respectively, were developed, which were validated and could be employed to efficiently fill the kB and pKa data gaps of parabens within applicability domains. The base-catalyzed hydrolysis half-lives were estimated to range from 6 h to 1.52 × 106 years (pH 7-9, 25 °C), further necessitating the in silico models due to the tedious and onerous experimental determination, and the huge number of hydrolyzable and ionizable chemicals that may be released into the environment.

Atmospheric Chemistry of Allylic Radicals from Isoprene: A Successive Cyclization-Driven Autoxidation Mechanism.

The atmospheric chemistry of isoprene has broad implications for regional air quality and the global climate. Allylic radicals, taking 13-17% yield in the isoprene oxidation by •Cl, can contribute as much as 3.6-4.9% to all possible formed intermediates in local regions at daytime. Considering the large quantity of isoprene emission, the chemistry of the allylic radicals is therefore highly desirable. Here, we investigated the atmospheric oxidation mechanism of the allylic radicals using quantum chemical calculations and kinetics modeling. The results indicate that the allylic radicals can barrierlessly combine with O2 to form peroxy radicals (RO2•). Under ≤100 ppt NO and ≤50 ppt HO2• conditions, the formed RO2• mainly undergo two times "successive cyclization and O2 addition" to finally form the product fragments 2-alkoxy-acetaldehyde (C2H3O2•) and 3-hydroperoxy-2-oxopropanal (C3H4O4). The presented reaction illustrates a novel successive cyclization-driven autoxidation mechanism. The formed 3-hydroperoxy-2-oxopropanal product is a new isomer of the atmospheric C3H4O4 family and a potential aqueous-phase secondary organic aerosol precursor. Under >100 ppt NO condition, NO can mediate the cyclization-driven autoxidation process to form C5H7NO3, C5H7NO7, and alkoxy radical-related products. The proposed novel autoxidation mechanism advances our current understanding of the atmospheric chemistry of both isoprene and RO2•.

Heterogeneous Formation of HONO Catalyzed by CO2.

Gas-phase nitrous acid (HONO) is a major precursor of hydroxyl radicals that dominate atmospheric oxidizing capacity. Nevertheless, pathways of HONO formation remain to be explored. This study unveiled an important CO2-catalysis mechanism of HONO formation, using Born-Oppenheimer molecular dynamics simulations and free-energy samplings. In the mechanism, HCO3- formed from CO2 hydrolysis reacts with NO2 dimers to produce HONO at water surfaces, and simultaneously, itself reconverts back to CO2 via intermediates OC(O)ONO- and HOC(O)ONO. A flow system experiment was performed to confirm the new mechanism, which indicated that HONO concentrations with CO2 injections were increased by 29.4-68.5%. The new mechanism can be extended to other humid surfaces. Therefore, this study unveiled a previously overlooked vital role of CO2 that catalyzes formation of HONO and affects atmospheric oxidizing capacity.

Formation Mechanisms of Iodine-Ammonia Clusters in Polluted Coastal Areas Unveiled by Thermodynamics and Kinetic Simulations.

It has been revealed that iodine species play important roles in atmospheric new particle formations (NPFs) in pristine coastal areas. However, it is unclear whether other atmospheric species, such as NH3, for which the levels in coastal areas of China are >2.5 × 1010 molecules·cm-3 are involved in the NPFs of iodine species, although NH3 has been proved to promote particle formation of H2SO4. Via high-level quantum chemical calculations and atmospheric cluster dynamic code simulations, this study unveiled new mechanisms of nucleation, in which NH3 mediates the formation of iodine particles by assisting hydrolysis of I2O5 or reacting with HIO3. The simulated formation rates of iodine-ammonia clusters via the new mechanisms are much higher than those simulated via sequential addition of HIO3 with subsequent release of H2O, under the condition that NH3 concentrations are higher than 1010 molecules·cm-3. The new mechanisms can well explain the observed cluster formation rates at a coastal site in Zhejiang of China. The findings not only expand the current understandings of the role of NH3 in NPFs but also highlight the importance of monitoring and evaluating NPFs via the iodine-ammonia cluster pathway in the coastal areas of China and other regions worldwide.

Role of hydrogen bond capacity of solvents in reactions of amines with CO2: A computational study.

Various computational methods were employed to investigate the zwitterion formation, a critical step for the reaction of monoethanolamine with CO2, in five solvents (water, monoethanolamine, propylamine, methanol and chloroform) to probe the effect of hydrogen bond capacity of solvents on the reaction of amine with CO2 occurring in the amine-based CO2 capture process. The results indicate that the zwitterion can be formed in all considered solvents except chloroform. For two pairs of solvents (methanol and monoethanolamine, propylamine and chloroform) with similar dielectric constant but different hydrogen bond capacity, the solvents with higher hydrogen bond capacity (monoethanolamine and propylamine) facilitate the zwitterion formation. More importantly, kinetics parameters such as activation free energy for the zwitterion formation are more relevant to the hydrogen bond capacity than to dielectric constant of the considered solvents, clarifying the hydrogen bond capacity could be more important than dielectric constant in determining the kinetics of monoethanolamine with CO2.

Development of Prediction Models on Base-Catalyzed Hydrolysis Kinetics of Phthalate Esters with Density Functional Theory Calculation.

Many phthalate esters (PAEs) are chemicals of high production volume and of toxicological concern. The second-order rate constant for base-catalyzed hydrolysis ( kB) is a key parameter for assessing environmental persistence of PAEs. However, the kB values for most PAEs are lacking, and the experimental determination of kB encounters various difficulties. Herein, density functional theory (DFT) methods were selected by comparing empirical kB values of five PAEs and five carboxylic acid esters with the DFT-calculated ones. Results indicate that PAEs with cyclic side chains are more vulnerable to base-catalyzed hydrolysis than PAEs with linear alkyl side chains, followed by PAEs with branched alkyl side chains. By combining experimental and DFT-calculated second-order rate constants for base-catalyzed hydrolysis of one side chain in PAEs ( kB_side chain), quantitative structure-activity relationship models were developed. The models can differentiate PAEs with the departure of the leaving group (or the nucleophilic attack of OH-) as the rate-determining step in the hydrolysis and estimate kB values, which provides a promising way to predict hydrolysis kinetics of PAEs. The half-lives of the investigated PAEs were calculated and vary from 0.001 h to 558 years (pH = 7∼9), further illustrating the necessity of prediction models for hydrolysis kinetics in assessing the environmental persistence of chemicals.

Effects of Atmospheric Water on ·OH-initiated Oxidation of Organophosphate Flame Retardants: A DFT Investigation on TCPP.

Tris(2-chloroisopropyl) phosphate (TCPP), a widely used organophosphate flame retardant, has been recognized as an important atmospheric pollutant. It is notable that TCPP has potential for long-range atmospheric transport. However, its atmospheric fate is unknown, restricting its environmental risk assessment. Herein we performed quantum chemical calculations to investigate the atmospheric transformation mechanisms and kinetics of TCPP initiated by ·OH in the presence of O2/NO/NO2, and the effects of ubiquitous water on these reactions. Results show the H-abstraction pathways are the most favorable for the titled reaction. The calculated gaseous rate constant and lifetime at 298 K are 1.7 × 10-10 cm3molecule-1 s-1 and 1.7 h, respectively. However, when considering atmospheric water, the corresponding lifetime is about 0.5-20.2 days. This study reveals for the first time that water has a negative role in the ·OH-initiated degradation of TCPP by modifying the stabilities of prereactive complexes and transition states via forming hydrogen bonds, which unveils one underlying mechanism for the observed persistence of TCPP in the atmosphere. Water also influences secondary reaction pathways of selected TCPP radicals formed from the primary H-abstraction. These results demonstrate the importance of water in the evaluation of the atmospheric fate of newly synthesized chemicals and emerging pollutants.

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.

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.

Formation of free radicals by direct photolysis of halogenated phenols (HPs) and effects of DOM: A case study on monobromophenols.

The free radicals play an important role to understand direct/indirect transformation mechanisms of organic pollutants. However, very few efforts have been made to elucidate the radicals produced by direct photolysis. In this study, the short-lived radicals generated under simulated sunlight irradiation from representative halogenated phenols (HPs), monobromophenols, were investigated by electron paramagnetic resonance (EPR). The results showed that three radicals, carbon-centered radical (C), hydrogen radical (H) and hydroxyl radical (OH), were generated from the direct irradiation of HPs. Compared to other substitutions, halogenated atom at para-position led to the highest production of these radicals which is in accordance with the energies calculated by density functional theory. Based on the analyses of the reactive species and corresponding intermediate adducts, the possible reaction pathways for these radicals were tentatively proposed. Dissolved organic matters (DOM) could enhance the photodegradation of HPs by directly affecting the radicals' formation, mainly due to generation of excited triplet DOM (3DOM*). A positive correlation was found between the concentrations of hydrated electron and the steady state 3DOM* from different DOM. Our findings provided insights into environmental photochemical fate of HPs through their direct photolysis and will help more accurately understand their phototransformation mechanisms in the environment.

Modeling adsorption of organic pollutants onto single-walled carbon nanotubes with theoretical molecular descriptors using MLR and SVM algorithms.

Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environmental behavior and ecological risks of organic pollutants and SWNTs. In this study, two models for predicting logK were developed with multiple linear regression (MLR) and support vector machine (SVM) algorithms. The two models have satisfactory goodness-of-fit, robustness and predictive ability, and the SVM model performs slightly better than the MLR model. The two models are based on the up-to-date experimental dataset consisting of 61 logK values, and the applicability domains cover diverse organic compounds with functional groups > CC<, CC, C6H5, >CO, COOH, C(O)O, OH, O, F, Cl, Br, NH2, NH, >N, >NN<, NO2, >NC(O)NH2, >NC(O)NH, S and S(O)(O). The adsorption of organic compounds toward SWNTs is mainly determined by van der Waals forces and hydrophobic interactions. Since only in silico molecular descriptors were employed for the modeling, the developed models are beneficial for prediction purposes.

Determination of 21 antibiotics in sea cucumber using accelerated solvent extraction with in-cell clean-up coupled to ultra-performance liquid chromatography-tandem mass spectrometry.

An accelerated solvent extraction (ASE) with in-cell clean-up method coupled to ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed to determine 21 antibiotics in sea cucumber. The analytes include 10 sulfonamides, 4 fluoroquinolones, 3 amphenicols, 2 beta-lactams, 1 lincosamide and trimethoprim. Optimal parameters of ASE method were obtained at 80 °C, 1 static cycle of 5 min with methanol/acetonitrile (1/1, v/v) using 2 g of C18 as adsorbent. Recoveries at 50.1-129.2% were achieved with RSD under 20%. Method detection limits ranged from 0.03 to 2.9 µg kg-1. Compared to the reported ultrasound-assisted extraction method, the proposed method offered comparable extraction efficiency for sulfonamides from sea cucumber, but higher for other categories of antibiotics. This validated method was then successfully applied to sea cucumber samples and 9 antibiotics were detected with the highest concentration up to 57.7 µg kg-1 for norfloxacin.