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Partitioning from water to nonaqueous phases is an important process that controls the behavior of contaminants in the environment and biota. However, for ionic chemicals including many perfluoroalkyl and polyfluoroalkyl substances (PFAS), environmentally relevant partition coefficients cannot be predicted using the octanol/water partition coefficient, which is commonly used as a hydrophobicity indicator for neutral compounds. As an alternative, this study measured C18 liquid chromatography retention times of 39 anionic PFAS and 20 nonfluorinated surfactants using isocratic methanol/water eluent systems. By measuring a series of PFAS with different perfluoroalkyl chain lengths, retention factors at 100% water (k0) were successfully extrapolated even for long-chain PFAS. Molecular size was the most important factor determining the k0 of PFAS and non-PFAS, suggesting that the cavity formation process is the key driver for retention. Log k0 showed a high correlation with the log of partition coefficients from water to the phospholipid membrane, air/water interface, and soil organic carbon. The results indicate the potential of C18 retention factors as predictive descriptors for anionic PFAS partition coefficients and the possibility of developing a more comprehensive multiparameter model for the partitioning of anionic substances in general.

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Henry's law constants (H) for selected probe molecules have been used as descriptors to estimate the COSMO-RS sigma profiles of solvents and solvent mixtures. Henry's law constants were calculated with COSMOtherm for small sets of probe molecules in 155 organic solvents (training set), and these constants subsequently used as descriptors to model the solvent sigma profiles with 61 multiple linear regression (MLR) equations. Subsequent input into COSMOtherm of weighted basis molecule solvent mixtures whose sigma profiles closely matched those modelled for the training set solvents allowed estimation of air-solvent and water-solvent partition ratios for solutes in solvents and solvent mixtures without input of the solvent or solvent mixture identity. The best performing model had 16 descriptors and gave both a training and test set average root-mean square error (RMSE) of 0.008 and an average relative square error (RSE) of 0.07. Partition ratios (K) were then generated for a test set of 251 additional organic solute molecules in solvent/water media where solvents were test set compounds and H constants for the same probe molecules were used as descriptors. The best performing sigma profile model yielded log K RMSE values ranging from 0.17 to 0.92. Finally, this approach was applied to several mixtures ranging from simple binary mixtures to two mixtures considered to be of unknown or variable composition, complex reaction productions or biological materials (UVCBs), namely gasoline and an essential oil mixture. Mixture/water partition ratios were estimated for 251 solutes giving log K RMSE values ranging from 0.24 to 0.88.

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Perfluoroalkyl carboxylic and sulfonic acids (PFCAs and PFSAs, respectively) have low acid dissociation constant values and are, therefore, deprotonated under most experimental and environmental conditions. Hence, the anionic species dominate their partitioning between water and organic phases, including octanol and phospholipid bilayers which are often used as model systems for environmental and biological matrices. However, data for solvent-water (SW) and membrane-water partition coefficients of the anion species are only available for a few per- and polyfluoroalkyl substances (PFAS). In the present study, an equation is derived using a Born-Haber cycle that relates the partition coefficients of the anions to those of the corresponding neutral species. It is shown via a thermodynamic analysis that for carboxylic acids (CAs), PFCAs, and PFSAs, the log of the solvent-water partition coefficient of the anion, log KSW (A- ), is linearly related to the log of the solvent-water partition coefficient of the neutral acid, log KSW (HA), with a unity slope and a solvent-dependent but solute-independent intercept within a PFAS (or CA) family. This finding provides a method for estimating the partition coefficients of PFCAs and PFSAs anions using the partition coefficients of the neutral species, which can be reliably predicted using quantum chemical methods. In addition, we have found that the neutral octanol-water partition coefficient, log KOW , is linearly correlated to the neutral membrane-water partition coefficient, log KMW ; therefore, log KOW , being a much easier property to estimate and/or measure, can be used to predict the neutral log KMW . Application of this approach to KOW and KMW for PFCAs and PFSAs demonstrates the utility of this methodology for evaluating reported experimental data and extending anion property data for chain lengths that are unavailable. Environ Toxicol Chem 2023;42:2317-2328. © 2023 SETAC.

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Activated carbon fiber (ACF) has received increasing attention as an adsorbent due to its excellent surface properties. However, the adsorption mechanism of ACF for micropollutants, especially those in ionic forms, has not been sufficiently characterized to date. Therefore, the adsorption property of ACF was characterized using isotherm experiments and linear free energy relationship (LFER). For the experiments, adsorption affinities of thirty-five chemicals, i.e., pharmaceuticals and endocrine-disrupting chemicals, on ACF were estimated. Afterward, the adsorption affinities were used as dependent variables to build the LFER modeling. Finally, three isolated models for each chemical species, i.e., cations, anions, and neutrals, and a comprehensive model for the whole dataset were developed. The LFER results revealed that the models for anionic and neutral compounds have high predictabilities in R2 of 0.97 and 0.96, respectively, while that for cations has a slightly lower R2 of 0.72. In the comprehensive model including cationic, anionic, and neutral compounds, the accuracy of it is 0.81. From the developed LFER model based on the whole dataset, the adsorption mechanisms of ACF for the selected substances could be interpreted, in which the terms of hydrophobic interaction, hydrogen bonding basicity, and anionic Coulombic force of the compounds were identified as the predominant interactions with ACF.

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Yeast is ubiquitous and may act as a solid phase in natural aquatic systems, which may affect the distribution of organic micropollutants (OMs). Therefore, it is important to understand the adsorption of OMs on yeast. Therefore, in this study, a predictive model for the adsorption values of OMs on the yeast was developed. For that, an isotherm experiment was performed to estimate the adsorption affinity of OMs on yeast (i.e., Saccharomyces cerevisiae). Afterwards, quantitative structure-activity relationship (QSAR) modeling was performed for the purpose of developing a prediction model and explaining the adsorption mechanism. For the modeling, empirical and in silico linear free energy relationship (LFER) descriptors were applied. The isotherm results showed that yeast adsorbs a wide range of OMs, but the magnitude of Kd strongly depends on the types of OMs. The measured log Kd values of the tested OMs ranged from -1.91 to 1.1. Additionally, it was confirmed that the Kd measured in distilled water is comparable to that measured in real anaerobic or aerobic wastewater (R2 = 0.79). In QSAR modeling, the Kd value could be predicted by the LFER concept with an R2 of 0.867 by empirical descriptors and an R2 of 0.796 by in silico descriptors. The adsorption mechanisms of yeast for OMs were identified in individual correlations between log Kd and each descriptor: Dispersive interaction, hydrophobicity, hydrogen-bond donor, and cationic Coulombic interaction of OMs attract the adsorption, while the hydrogen-bond acceptor and anionic Coulombic interaction of OMs act as repulsive forces. The developed model can be used as an efficient method to estimate OM adsorption to yeast at a low level of concentration.

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The consistency principle represents a physicochemical condition requisite for ideal protein folding. It assumes that any pair of amino acid residues in partially folded structures has an attractive short-range interaction only if the two residues are in contact within the native structure. The residue-specific equilibrium constant, K, and the residue-specific rate constant, k (forward and backward), can be determined by NMR and hydrogen-deuterium exchange studies. Linear free energy relationships (LFER) in the rate-equilibrium free energy relationship (REFER) plots (i.e., log k vs. log K) are widely seen in protein-related phenomena, but our REFER plot differs from them in that the data points are derived from one polypeptide chain under a single condition. Here, we examined the theoretical basis of the residue-based LFER. First, we derived a basic equation, ρij=½(φi+φj), from the consistency principle, where ρij is the slope of the line segment that connects residues i and j in the REFER plot, and φi and φj are the local fractions of the native state in the transient state ensemble (TSE). Next, we showed that the general solution is the alignment of the (log K, log k) data points on a parabolic curve in the REFER plot. Importantly, unlike LFER, the quadratic free energy relationship (QFER) is compatible with the heterogeneous formation of local structures in the TSE. Residue-based LFER/QFER provides a unique insight into the TSE: A foldable polypeptide chain consists of several folding units, which are consistently coupled to undergo smooth structural changes.

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Per- and polyfluoroalkyl substances (PFAS) form weak van der Waals (vdW) interactions, which render this class of chemicals more volatile than nonfluorinated analogues. Here, the hexadecane/air partition coefficient (KHxd/air) values at 25 °C, as an index of vdW interaction strength and volatility, were determined for 64 neutral PFAS using the variable phase ratio headspace and gas chromatographic retention methods. Log KHxd/air values increased linearly with increasing number of CF2 units, and the increase in log KHxd/air value per CF2 was smaller than that per CH2. Comparison of PFAS sharing the same perfluoroalkyl chain length but with different functional groups demonstrated that KHxd/air was highest for the N-alkyl perfluoroalkanesulfonamidethanols and lowest for the perfluoroalkanes and that the size of the nonfluorinated structure determines the difference in KHxd/air between PFAS groups. Two models, the quantum chemistry-based COSMOtherm model and an iterative fragment selection quantitative structure-property relationship (IFS-QSPR) model, accurately predicted the log KHxd/air values of the PFAS with root-mean-square errors of 0.55 and 0.35, respectively. COSMOtherm showed minor systematic errors for all PFAS, whereas IFS-QSPR exhibited large errors for a few PFAS groups that were outside the model applicability domain. The present data set will be useful as a benchmark of the volatilities of the various PFAS and for predicting other partition coefficient values of PFAS.

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Polar organic chemical integrative sampler (POCIS) contains sorbent, which is typically enclosed between two polyethersulfones (PES) membranes. A significant PES uptake is reported for many contaminants, yet, aqueous concentration is mainly correlated with the sorbent uptake using first-order kinetics. Under high PES sorption, the first-order kinetics often provide erroneous sampling rate for the sorbent phase due to increased membrane resistance. This work evaluated the uptake of four high PES sorbing chemicals, i.e., three Cl- and CH3-substituted nitrobenzenes and one chlorinated aniline using POCIS and the potential of a single-phase PES sampler using laboratory experiments. POCIS calibration results demonstrated that both sorbent and membrane had similar affinity for the target compounds. A rapid PES sorption occurred in the earlier days (<7 days) followed by a gradual increase in the PES phase concentration (equilibrium not achieved after 60 days). Especially, the membrane was the primary sink for 3,4-dichloroaniline and 3,4-dichloronitrobenzene for up to 14 and 31 days, respectively. On the other hand, the single-phase PES sampler showed similar mass uptake as POCIS and reached equilibrium within 19 days under static condition, indicating its potential suitability in the equilibrium regime. PES-water partition coefficient of the target compounds was between 1.2 and 6.5 L/g. Finally, we present a poly-parameter linear-free energy relationship (pp-LFER) using published data to predict the PES-water partition coefficients. The pp-LFER models showed moderate predictability as indicated by R2adj values between 0.7 and 0.9 for both internal and external data set consisting of a wide range of hydrophobic and hydrophilic compounds (-0.1 ≤ logKOW ≤ 7.4). The proposed pp-LFER model can be used to screen high PES-sorbing chemicals to increase the reliability and accuracy of aqueous concentration prediction from POCIS sampling and to select the most appropriate sampling approach for new compounds.

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EXSY (exchange spectroscopy) NMR provides the residue-specific equilibrium constants, K, and residue-specific kinetic rate constants, k, of a polypeptide chain in a two-state exchange in the slow exchange regime. A linear free energy relationship (LFER) discovered in a log k versus log K plot is considered to be a physicochemical basis for smooth folding and conformational changes of protein molecules. For accurate determination of the thermodynamic and kinetic parameters, the measurement bias arising from state-specific differences in the R1 and R2 relaxation rates of 1H and other nuclei in HSQC and EXSY experiments must be minimized. Here, we showed that the time-zero HSQC acquisition scheme (HSQC0) is effective for this purpose, in combination with a special analytical method (Π analysis) for EXSY. As an example, we applied the HSQC0 + Π method to the two-state exchange of nukacin ISK-1 in an aqueous solution. Nukacin ISK-1 is a 27-residue lantibiotic peptide containing three mono-sulfide linkages. The resultant bias-free residue-based LFER provided valuable insights into the transition state of the topological interconversion of nukacin ISK-1. We found that two amino acid residues were exceptions in the residue-based LFER relationship. We inferred that the two residues could adopt special conformations in the transition state, to allow the threading of some side chains through a ring structure formed by one of the mono-sulfide linkages. In this context, the two residues are a useful target for the manipulation of the physicochemical properties and biological activities of nukacin ISK-1.

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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.

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Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R2 are 0.940 and 0.976) than the published NN-LFER model (R2 are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log Ce, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

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Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log Kstorage-lipid/water), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log Kstorage-lipid/water, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

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PURPOSE: Undesired drug sorption on laboratory material surfaces reduces the performance of analytical methods and results in the generation of unreliable data. Hence, we characterized the sorption of drugs and evaluated the sorption extent using a linear free energy relationship (LFER) model with Abraham solvation parameters of drugs. Furthermore, to prevent sorption, the effects of additives, such as organic solvents and salts, were evaluated. METHODS: The sorption of fifteen model drugs (concentration: 2 µM), with various physicochemical properties, on materials in 0.2% dimethyl sulfoxide aqueous solutions was evaluated. Drug sorption extent on the materials was determined using high-performance liquid chromatography. The obtained results were analyzed using an LFER model with Abraham solvation parameters of the drugs. The effect of additives on the sorption of itraconazole, one of the most hydrophobic drugs among those tested in this study, was investigated. RESULTS: Sorption was dependent on the physicochemical properties of drugs, rather than the type of materials used, and additives altered the rate of drug sorption. Equations were developed to evaluate the sorption extent (nmol) of drugs to glass and polypropylene using the Abraham solvation parameters of the drugs. CONCLUSIONS: LFER modeling with Abraham solvation parameters of drugs enabled us to evaluate drug sorption on materials. All the additives altered the rate of drug sorption, and some organic solvents effectively prevented sorption. The developed LFER model would be useful for assessment of the sorption properties of compounds in in vitro evaluations in drug discovery research and various other biochemical fields.

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It is of great importance to predict the adsorption of micropollutants onto CNTs, which is not only useful for exploring their potential adsorbent applications, but also helpful for better understanding their fate and risks in aquatic environments. This study experimentally examined the adsorption affinities of thirty-one micropollutants on four multi-walled CNTs (MWCNTs) with different functional groups (non-functionalized, -COOH, -OH, and -NH2). The properties of each adsorbent were predicted based on the linear free energy relationship (LFER) model. The experimental results showed that MWCNTs-COOH has remarkable adsorption affinities for positively charged compounds (1.996-3.203 log unit), whereas MWCNTs-NH2 has high adsorption affinities for negatively charged compounds (1.360-3.073 log unit). Regarding neutral compounds, there was no significant difference in adsorption affinities of all types of CNTs. According to modeling results, the adsorption affinity can be accurately predicted using LFER models with R2 in the range of 0.81-0.91. Based on the developed models, the adsorption mechanism and contribution of individual intermolecular interactions to the overall adsorption were interpreted. For non-functionalized MWCNTs, molecular interactions induced by molecular volume and H-bonding basicity predominantly contribute to adsorption, whereas for functionalized MWCNTs, the Coulombic interaction due to the charges is an important factor.

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The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air-water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R2 = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA's model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model-a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)-performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA's Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.

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Electronic interactions in donor-π-linker-acceptor systems with barbituric acid as an electron acceptor and possible electron donor were investigated to screen promising candidates with a push-pull character based on experimental and quantum chemical studies. The tautomeric properties of 5-benzylidenebarbituric acid derivatives were studied with NMR spectra, spectrophotometric determination of the pKa values, and quantum chemical calculations. Linear solvation energy relationships (LSER) and linear free energy relationships (LFER) were applied to the spectral data - UV frequencies and 13C NMR chemical shifts. The experimental studies of the nature of the ground and excited state of investigated compounds were successfully interpreted using a computational chemistry approach including ab initio MP2 geometry optimization and time-dependent DFT calculations of excited states. Quantification of the push-pull character of barbituric acid derivatives was performed by the 13CNMR chemical shift differences, Mayer π bond order analysis, hole-electron distribution analysis, and calculations of intramolecular charge transfer (ICT) indices. The results obtained show, that when coupled with a strong electron-donor, barbituric acid can act as the electron-acceptor in push-pull systems, and when coupled with a strong electron-acceptor, barbituric acid can act as the weak electron-donor.

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A methodology for the estimation of the different phase volumes in HILIC is presented. For a ZIC-HILIC column the mobile phase volume (hold-up volume) is determined in several acetonitrile- and methanol-water compositions by a Linear Free Energy Relationships (LFER) homologous series approach involving n-alkyl-benzenes, -phenones, and -ketones. We demonstrate that the column works as a HILIC column when the mobile phase contains high and medium proportions of methanol or acetonitrile. However, for acetonitrile contents below 20%, or 40% for methanol, same column works in RPLC. In between, a mixed HILIC-RPLC behavior is observed, and solutes of low molecular volume are retained as in HILIC mode, but the largest ones show RPLC retention. From the homologous series retention data and pycnometric measurements involving the pure organic solvents and their mixtures with water, the mean solvent composition of the water-rich transition layers between column functionalization and the bulk mobile phase, which act as stationary phase, is estimated. Finally, the phase ratio between stationary and mobile phases is also estimated for each eluent composition, allowing the calculation of the corresponding stationary phase volumes. All volumes are strongly dependent on the water content in the eluent, especially when acetonitrile is selected as mobile phase constituent. In HILIC mode, when the water content in the hydroorganic mobile phase increases, the volumes of mobile phase decrease, but the volumes of stationary phase (mainly the water layer adsorbed onto the bonded-phase and the water-enriched interface) increase. However, at high water concentrations, where the column works in RPLC mode, the mobile phase volume increases and the stationary phase (which is now the bonded zwitterion) volume decreases when increasing the water percentage in the mobile phase.

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A cyclic voltammetry study of a series of iron(III) TAML activators of peroxides of several generations in acetonitrile as solvent reveals reversible or quasireversible FeIII/IV and FeIV/V anodic transitions, the formal reduction potentials (E°') for which are observed in the ranges 0.4-1.2 and 1.4-1.6 V, respectively, versus Ag/AgCl. The slope of 0.33 for a linear E°'(IV/V) against E°'(III/IV) plot suggests that the TAML ligand system plays a bigger role in the FeIII/IV transition, whereas the second electron transfer is to a larger extent an iron-centered phenomenon. The reduction potentials appear to be a convenient tool for analysis of various properties of iron TAML activators in terms of linear free energy relationships (LFERs). The values of E°'(III/IV) and E°'(IV V-1 ) correlate 1) with the pKa values of the axial aqua ligand of iron(III) TAMLs with slopes of 0.28 and 0.06 V, respectively; 2) with the Stern-Volmer constants KSV for the quenching of fluorescence of propranolol, a micropollutant of broad concern; 3) with the calculated ionization potentials of FeIII and FeIV TAMLs; and 4) with rate constants kI and kII for the oxidation of the resting iron(III) TAML state by H2 O2 and reactions of the active forms of TAMLs formed with donors of electrons S, respectively. Interestingly, slopes of log kII versus E°'(III/IV) plots are lower for fast-to-oxidize S than for slow-to-oxidize S. The log kI versus E°'(III/IV) plot suggests that the manmade TAML catalyst can never be as reactive toward H2 O2 as a horseradish peroxidase enzyme.

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Predicting plant cuticle-water partition coefficients (Kcw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental Kcw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict Kcw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logKcw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R2adj,tra) of 0.93, bootstrapping coefficient (Q2BOOT) of 0.92, external validation coefficient (Q2ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13-31%), double bond (9-17%) or nitrogen-containing heterocycles (9-17%), π/n-electron pairs interactions exhibit obvious positive contributions to logKcw. In conclusion, the proposed pp-LFER model is beneficial for predicting logKcw of potential organic pollutants directly from their molecular structures.

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Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

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