Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric

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

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric

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

Impact of Interactions between Metal Oxides to Oxidative Reactivity of Manganese Dioxide

Manganese oxides typically exist as <i>mixtures</i> with other metal oxides in soil–water environments; however, information is only available on their redox activity as <i>single</i> oxides. To bridge this gap, we examined three binary oxide mixtures containing MnO₂ and a secondary metal oxide (Al₂O₃, SiO₂ or TiO₂). The goal was to understand how these secondary oxides affect the oxidative reactivity of MnO₂. SEM images suggest significant heteroaggregation between Al₂O₃ and MnO₂ and to a lesser extent between SiO₂/TiO₂ and MnO₂. Using triclosan and chlorophene as probe compounds, pseudofirst-order kinetic results showed that Al₂O₃ had the strongest inhibitory effect on MnO₂ reactivity, followed by SiO₂ and then TiO₂. Al³⁺ ion or soluble SiO₂ had comparable inhibitory effects as Al₂O₃ or SiO₂, indicating the dominant inhibitory mechanism was surface complexation/precipitation of Al/Si species on MnO₂ surfaces. TiO₂ inhibited MnO₂ reactivity only when a limited amount of triclosan was present. Due to strong adsorption and slow desorption of triclosan by TiO₂, precursor-complex formation between triclosan and MnO₂ was much slower and likely became the new rate-limiting step (as opposed to electron transfer in all other cases). These mechanisms can also explain the observed adsorption behavior of triclosan by the binary oxide mixtures and single oxides

Complexation Facilitated Reduction of Aromatic <i>N</i>‑Oxides by Aqueous Fe^II–Tiron Complex: Reaction Kinetics and Mechanisms

Rapid reduction of carbadox (CDX), olaquindox and several other aromatic <i>N</i>-oxides were investigated in aqueous solution containing Fe^II and tiron. Consistent with previous work, the 1:2 Fe^II–tiron complex, FeL₂^6‑, is the dominant reactive species as its concentration linearly correlates with the observed rate constant <i>k</i>_obs under various conditions. The <i>N</i>-oxides without any side chains were much less reactive, suggesting direct reduction of the <i>N</i>-oxides is slow. UV–vis spectra suggest FeL₂^6‑ likely forms 5- or 7-membered rings with CDX and olaquindox through the N and O atoms on the side chain. The formed inner-sphere complexes significantly facilitated electron transfer from FeL₂^6‑ to the <i>N</i>-oxides. Reduction products of the <i>N</i>-oxides were identified by HPLC/QToF-MS to be the deoxygenated analogs. QSAR analysis indicated neither the first electron transfer nor N–O bond cleavage is the rate-limiting step. Calculations of the atomic spin densities of the anionic <i>N</i>-oxides confirmed the extensive delocalization between the aromatic ring and the side chain, suggesting complex formation can significantly affect the reduction kinetics. Our results suggest the complexation facilitated <i>N</i>-oxide reduction by Fe^II–tiron involves a free radical mechanism, and the subsequent deoxygenation might also benefit from the weak complexation of Fe^II with the <i>N</i>-oxide O atom

Interaction Mechanisms and Predictive Model for the Sorption of Aromatic Compounds onto Nonionic Resins

Understanding interaction mechanisms between porous sorbents and organic compounds is important in selecting or custom-synthesizing an appropriate sorbent. In this study, sorption isotherms of a set of 14 (XAD-4&7) or 11 (MN200) aromatic compounds were measured for three nonionic resins, and a phase conversion approach (from <i>aqueous</i> phase to <i>n</i>-<i>hexadecane</i> or <i>gas</i> phase) was applied to separate sorbate-sorbent interactions from the overall involved interactions. Subsequently, contributions of individual interactions to the overall Δ<i>G</i> were quantified by poly parameter linear free energy relationships (pp-LFERs). Cavity energy (<i><b>V</b></i>), energy costs for creating cavities in bulk water, is the dominant driving force for the sorption from aqueous phase. Meanwhile, sorption was substantially abated by H-bonding accepting capacities of the solutes (<i><b>B</b></i>) due to the high electron accepting capacity of water molecules. Solute’s H-bonding donating capacity (<i><b>A</b></i>) and polarity/polarizability (<i><b>S</b></i>) are predominantly responsible for the <i>n-hexadecane</i> or gas-phase converted sorptions; <i><b>V</b></i> is also important in the gas-phase converted sorption. XAD-7 has larger <i><b>A</b></i> and <i><b>S</b></i> coefficients than XAD-4 and MN200 for both the original and converted analyses, while the opposite is true for <i><b>V</b></i> coefficients. More promisingly, a predictive model, developed based on the sorption of 7 simple aromatic compounds by the resins, can accurately estimate the sorption behaviors of 7 other relatively complex aromatic compounds within a wide range of concentrations

A Modified Polanyi-based Model for Mechanistic Understanding of Adsorption of Phenolic Compounds onto Polymeric Adsorbents

To obtain mechanistic insight into adsorption of phenolic compounds by two representative polymeric adsorbents, XAD-4 (polystyrene) and XAD-7 (polymethacrylate), a modified Polanyi-based Dubinin-Ashtakhov (D–A) model was developed based on a unique combination of the Polanyi theory, polyparameter linear energy relationships and infinitely dilute solution in <i>n</i>-hexadecane as the reference state. The adsorption potential in the D–A model ε = –<i>RT</i>ln­(<i>C</i>_w/<i>C</i>_w^sat) was redefined by replacing the term (<i>C</i>_w/<i>C</i>_w^sat) with the normalized equivalent concentration in <i>n</i>-hexadecane (<i>C</i>_HD), where <i>C</i>_w is the aqueous equilibrium concentration and <i>C</i>_w^sat is the aqueous solubility of the solute. Using the new reference state allows quantitative comparison among various solutes. By fitting adsorption isotherms to the modified model using ε_HD = –<i>RT</i>ln­(<i>C</i>_HD/10 000), a new normalizing factor (<i>E</i>_m) was obtained to quantify the contributions of specific interactions (i.e., H-bonding, dipolar/polarizability, etc.) to the overall adsorption energy. Significant linear correlations were established between “<i><b>A</b></i>”, the hydrogen-bond acidity, and “<i>E</i>_m” for the investigated compounds, suggesting that, in addition to hydrophobic interactions, hydrogen-bonding is predominantly responsible for the adsorption of phenols by XAD-4 and XAD-7. Additionally, adsorption capacity and affinity of phenolates were significantly less than those of phenols; another model was proposed to accurately predict the effect of pH on the adsorption behavior of phenols

Sorption Mechanism and Predictive Models for Removal of Cationic Organic Contaminants by Cation Exchange Resins

Understanding the sorption mechanism of organic contaminants on cation exchange resins (CXRs) will enable application of these resins for the removal of cationic organic compounds from contaminated water. In this study, sorption of a diverse set of 12 organic cations and 8 neutral aromatic solutes on two polystyrene CXRs, MN500 and Amberlite 200, was examined. MN500 showed higher sorbed concentrations due to its microporous structure. The sorbed concentrations followed the same trend of aromatic cations > aliphatic cations > neutral solutes for both resins. Generally, solute–solvent interactions, nonpolar moiety of the solutes, and resin matrix can affect selectivity of the cations. Sorbed concentrations of the neutral compounds were significantly less than those of the cations, indicating a combined effect of electrostatic and nonelectrostatic interactions. By conducting multiple linear regression between Gibbs free energy of sorption and Abraham descriptors for all 20 compounds, polarity/polarizability (<i>S</i>), H-bond acidity (<i>A</i>), induced dipole (<i>E</i>), and electrostatic (<i>J</i>⁺) interactions were found to be involved in the sorption of the cations by the resins. After converting the aqueous sorption isotherms to sorption from the ideal gas-phase by water-wet resins, a more significant effect of <i>J</i>⁺ was observed. Predictive models were then developed based on the linear regressions and validated by accurately estimating the sorption of different test set compounds with a root-mean-square error range of 0.91–1.1 and 0.76–0.85 for MN500 and Amberlite 200, respectively. The models also accurately predicted sorption behavior of aniline and imidazole between pH 3 and 10

Elucidating the Role of Electron Shuttles in Reductive Transformations in Anaerobic Sediments

Model studies have demonstrated that electron shuttles (ES) such as dissolved organic matter (DOM) can participate in the reduction of organic contaminants; however, much uncertainty exists concerning the significance of this solution phase pathway for contaminant reduction in natural systems. To compare the identity and reactivity of ES in anaerobic sediments with those in model systems, two chemical probes (4-cyano-4‘-aminoazobenzene (CNAAzB) either free or covalently bound to glass beads) were synthesized that allowed for differentiation between surface-associated and solution-phase electron-transfer processes. The feasibility of these chemical probes were demonstrated in abiotic model systems (Fe(II)/Fe(III) oxide) and biotic model systems (Fe(II)/Fe(III) oxide or river sediment amended with S. putrefaciens strain cells). Experiments in the abiotic systems revealed that the addition of model hydroquinones and chemically reduced DOM increased reduction rates of free CNAAzB, whereas no enhancement in reactivity was observed with the addition of model quinones or DOM. Bound CNAAzB was also reduced by model hydroquinones and reduced DOMbut not by model quinones and untreated DOMin the abiotic model systems, indicating that Fe(II)/Fe(III) oxides do not function as a bulk reductant for the reduction of ES. Addition of model quinones or untreated DOM to the biotic models systems with sediment increased reduction rates of bound CNAAzB, which correlated well with the dissolved organic carbon content. In natural sediment slurries, reduction rates of bound CNAAzB correlated well with parameters for organic carbon (OC) content of both sediments and supernatants. Our results support a scenario in which reducible organic contaminants will compete with iron oxides for the electron flow generated by the microbially mediated oxidation of organic carbon and subsequent reduction of quinone functional groups associated with DOM

Oxidative Transformation of Triclosan and Chlorophene by Manganese Oxides

The antibacterial agents triclosan (5-chloro-2-(2,4-dichlorophenoxy)phenol) and chlorophene (4-chloro-2-(phenylmethyl)phenol) show similar susceptibility to rapid oxidation by manganese oxides (δ-MnO2 and MnOOH) yielding MnII ions. Both the initial reaction rate and adsorption of triclosan to oxide surfaces increase as pH decreases. The reactions are first-order with respect to the antibacterial agent and MnO2. The apparent reaction orders to H+ were determined to be 0.46 ± 0.03 and 0.50 ± 0.03 for triclosan and chlorophene, respectively. Dissolved metal ions (MnII, ZnII, and CaII) and natural organic matter decrease the reaction rate by competitively adsorbing and reacting with MnO2. Product identification indicates that triclosan and chlorophene oxidation occurs at their phenol moieties and yields primarily coupling and p-(hydro)quinone products. A trace amount of 2,4-dichlorophenol is also produced in triclosan oxidation, suggesting bond-breaking of the ether linkage. The experimental results support the mechanism that after formation of a surface precursor complex of the antibacterial agent and the surface-bound MnIV, triclosan and chlorophene are oxidized to phenoxy radicals followed by radical coupling and further oxidation to form the end products. Compared to several structurally related substituted phenols (i.e., 2-methyl-4-chlorophenol, 2,4-dichlorophenol, 3-chlorophenol, and phenol), triclosan and chlorophene exhibit comparable or higher reactivities toward oxidation by manganese oxides. The higher reactivities are likely affected by factors including electronic and steric effects of substituents and compound hydrophobicity. Once released into the environment, partitioning of triclosan and chlorophene to soils and sediments is expected because of their relatively hydrophobic nature. Results of this study indicate that manganese oxides in soils will facilitate transformation of these antibacterial agents

Reconstruction of Adsorption Potential in Polanyi-Based Models and Application to Various Adsorbents

The equilibrium Polanyi adsorption potential was reconstructed as ε = −<i>RT</i> ln­(<i>C</i>_a(or H)/δ) to correlate the characteristic energy (<i>E</i>) of Polanyi-based models (<i>q</i>_e = <i>f</i>[ε/<i>E</i>]) with the properties or structures of absorbates, where <i>q</i>_e is the equilibriumn adsorption capacity, <i>C</i>_a(or H) is the converted concentration from the equilibrium aqueous concentration at the same activity and corresponds to the adsorption from the gas or <i>n</i>-hexadecane (HD) phase by the water-wet adsorbent, and “δ” is an arbitrary divisor to converge the model fitting. Subsequently, the modified Dubinin–Astakhov model based on the reconstructed ε was applied to aqueous adsorption on activated carbon, black carbon, multiwalled carbon nanotubes, and polymeric resin. The fitting results yielded <i>intrinsic</i> characteristic energies <i>E</i>_a, derived from aqueous-to-gas phase conversion, or <i>E</i>_H, derived from aqueous-to-HD phase conversion, which reflect the contributions of the overall or specific adsorbate–adsorbent interactions to the adsorption. Effects of the adsorbate and adsorbent properties on <i>E</i>_a or <i>E</i>_H then emerge that are unrevealed by the original characteristic energy (<i>E</i>_o), i.e., adsorbates with tendency to form stronger interactions with an adsorbent have larger <i>E</i>_a and <i>E</i>_H. Additionally, comparison of <i>E</i>_a and <i>E</i>_H allows quantitative analysis of the contributions of nonspecific interactions, that is, a significant relationship was established between the nonspecific interactions and Abraham’s descriptors for the adsorption of all 32 solutes on the four different adsorbents: (<i>E</i>_a – <i>E</i>_H) = 24.7 × <i><b>V</b></i> + 9.7 × <i><b>S</b></i> – 19.3 (<i>R</i>² = 0.97), where <i><b>V</b></i> is McGowan’s characteristic volume for adsorbates, and <i><b>S</b></i> reflects the adsorbate’s polarity/polarizability