Single-atoms (N, P, S) encapsulation of Ni-doped graphene/PEDOT hybrid materials as sensors for H2S gas applications: intuition from computational study

This comprehensive study was dedicated to augmenting the sensing capabilities of Ni@GP_PEDOT@H2S through the strategic functionalization with nitrogen, phosphorus, and sulfur heteroatoms. Governed by density functional theory (DFT) computations at the gd3bj-B3LYP/def2svp level of theory, the investigation meticulously assessed the performance efficacy of electronically tailored nanocomposites in detecting H2S gas-a corrosive byproduct generated by sulfate reducing bacteria (SRB), bearing latent threats to infrastructure integrity especially in the oil and gas industry. Impressively, the analysed systems, comprising Ni@GP_PEDOT@H2S, N_Ni@GP_PEDOT@H2S, P_Ni@GP_PEDOT@H2S, and S_Ni@GP_PEDOT@H2S, unveiled both structural and electronic properties of noteworthy distinction, thereby substantiating their heightened reactivity. Results of adsorption studies revealed distinct adsorption energies (- 13.0887, - 10.1771, - 16.8166, and - 14.0955 eV) associated respectively with N_Ni@GP_PEDOT@H2S, P_Ni@GP_PEDOT@H2S, S_Ni@GP_PEDOT@H2S, and Ni@GP_PEDOT systems. These disparities vividly underscored the diverse strengths of the adsorbed H2S on the surfaces, significantly accentuating the robustness of S_Ni@GP_PEDOT@H2S as a premier adsorbent, fuelled by the notably strong sulfur-surface interactions. Fascinatingly, the sensor descriptor findings unveiled multifaceted facets pivotal for H2S detection. Ultimately, molecular dynamic simulations corroborated the cumulative findings, collectively underscoring the pivotal significance of this study in propelling the domain of H2S gas detection and sensor device innovation.

Multi-order graph attention network for water solubility prediction and interpretation

The water solubility of molecules is one of the most important properties in various chemical and medical research fields. Recently, machine learning-based methods for predicting molecular properties, including water solubility, have been extensively studied due to the advantage of effectively reducing computational costs. Although machine learning-based methods have made significant advances in predictive performance, the existing methods were still lacking in interpreting the predicted results. Therefore, we propose a novel multi-order graph attention network (MoGAT) for water solubility prediction to improve the predictive performance and interpret the predicted results. We extracted graph embeddings in every node embedding layer to consider the information of diverse neighboring orders and merged them by attention mechanism to generate a final graph embedding. MoGAT can provide the atomic-specific importance scores of a molecule that indicate which atoms significantly influence the prediction so that it can interpret the predicted results chemically. It also improves prediction performance because the graph representations of all neighboring orders, which contain diverse range of information, are employed for the final prediction. Through extensive experiments, we demonstrated that MoGAT showed better performance than the state-of-the-art methods, and the predicted results were consistent with well-known chemical knowledge.

Discovery of rosin-based acylhydrazone derivatives as potential antifungal agents against rice Rhizoctonia solani for sustainable crop protection

BACKGROUND

The use of fungicides to protect crops from diseases is an effective method, and novel environmentally friendly plant-derived fungicides with enhanced performance and low toxicity are urgent requirements for sustainable agriculture.

RESULTS

Two kinds of rosin-based acylhydrazone compounds were designed and prepared. Based on the antifungal activity assessment against Rhizoctonia solani, Fusarium oxysporum, Phytophthora capsici, Sclerotinia sclerotiorum, and Botrytis cinerea, acylhydrazone derivatives containing a thiophene ring were screened and showed an inhibitory effect on rice R. solani. Among them, Compound 4n, with an electron-withdrawing group on the benzene ring structure attached to the thiophene ring, showed optimal activity, and the EC₅₀ value was 0.981 mg L^-1 , which was lower than that of carbendazim. Furthermore, it was indicated that 4n could affect the mycelial morphology, cell membrane permeability and microstructure, cause the generation of reactive oxygen species in fungal cells, and damage the nucleus and mitochondrial physiological function, resulting in the cell death of R. solani. Meanwhile, Compound 4n exhibited a better therapeutic effect on in vivo rice plants. However, the induction activity of 4n on the defense enzyme in rice leaf sheaths showed that 4n stimulates the initial resistance of rice plants by removing active oxygen, thereby protecting the cell membrane or enhancing the strength of the cell wall. Through the quantitative structure-activity relationship study, the quantitative chemical and electrostatic descriptors significantly affect the binding of 4n with the receptor, which improves its antifungal activity.

CONCLUSION

This study provides a basis for exploiting potential rosin-based fungicides in promoting sustainable crop protection. © 2022 Society of Chemical Industry.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code.

Kinetic Modeling of API Oxidation: (1) The AIBN/H2O/CH3OH Radical "Soup"

Stress testing of active pharmaceutical ingredients (API) is an important tool used to gauge chemical stability and identify potential degradation products. While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown. As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing. Here we applied ab initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation. We generated a detailed kinetic model for a representative azobis(isobutyronitrile) (AIBN)/H₂O/CH₃OH stress-testing system with a varied cosolvent ratio (50%/50%-99.5%/0.5% vol water/methanol) for 5.0 mM AIBN and representative pH values of 4-10 at 40 °C that was stirred and open to the atmosphere. At acidic conditions, hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration. At acidic conditions, the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while, at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system. The present work reveals the prominent species in a common model API stress testing system at various cosolvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates the usage of novel software tools for automated chemical kinetic model generation and ab initio refinement.

Development of a Detailed Kinetic Model for the Oxidation of n-Butane in the Liquid Phase

The chemistry underlying liquid-phase oxidation of organic compounds, the main cause of their aging, is characterized by a free-radical chain reaction mechanism. The rigorous simulation of these phenomena requires the use of detailed kinetic models that contain thousands of species and reactions. The development of such models for the liquid phase remains a challenge as solvent-dependent thermokinetic parameters have to be provided for all the species and reactions of the model. Therefore, accurate and high-throughput methods to generate these data are required. In this work, we propose new methods to generate these data, and we apply them for the development of a detailed chemical kinetic model for n-butane autoxidation, which is then validated against literature data. Our approach for model development is based on the work of Jalan et al. [J. Phys. Chem. B 2013, 117, 2955-2970] who used Gibbs free energies of solvation [ΔsolvG(T)] to correct the data of the gas-phase kinetic model. In our approach, an equation of state (EoS) is used to compute ΔsolvG as a function of temperature for all the chemical species in the mechanism. Currently, ΔsolvG(T) of free radicals cannot be computed with an EoS and it was calculated for their parent molecule (H-atom added on the radical site). Theoretical calculations with the implicit solvent model were performed to quantify the impact of this assumption and showed that it is acceptable for radicals in n-butane and probably in all n-alkanes. New rate rules were proposed for the most important reactions of the model, based on theoretical calculations and the literature data. The developed detailed kinetic model for n-butane autoxidation is the first proposed model in the literature and was validated against the experimental data from the literature. Simulations showed that the main autoxidation products, sec-butyl hydroperoxides and 2-butanol, are produced from H-abstractions from n-butane by sec-C4H9OO radicals and the C4H9OO + C4H9OO reaction, respectively. The uncertainty of the product ratio ("butanone + 2-butanol"/"2-butoxy + 2-butoxy") of the latter reaction remains high in the literature, and our simulations suggest a 1:1 ratio in n-butane solvent.

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks

Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, drug binding, and bioavailability of drugs. In this work, we present a deep learning method based on graph networks to accurately predict solvation free energies of small organic molecules. The proposed model, comprising three phases, namely, message passing, interaction, and prediction, is able to predict solvation free energies in any generic organic solvent with a mean absolute error of 0.16 kcal/mol. In terms of accuracy, the current model outperforms all of the proposed machine learning-based models so far. The atomic interactions predicted in an unsupervised manner are able to explain the trends of free energies consistent with chemical wisdom. Further, the robustness of the machine learning-based model has been tested thoroughly, and its capability to interpret the predictions has been verified with several examples.

Operando Modeling of Multicomponent Reactive Solutions in Homogeneous Catalysis: from Non-standard Free Energies to Reaction Network Control

Optimization and execution of chemical reactions are to a large extend based on experience and chemical intuition of a chemist. The chemical intuition is rooted in the phenomenological Le Chatelier's principle that teaches us how to shift equilibrium by manipulating the reaction conditions. To access the underlying thermodynamic parameters and their condition-dependencies from the first principles is a challenge. Here, we present a theoretical approach to model non-standard free energies for a complex catalytic CO2 hydrogenation system under operando conditions and identify the condition spaces where catalyst deactivation can potentially be suppressed. Investigation of the non-standard reaction free energy dependencies allows rationalizing the experimentally observed activity patterns and provides a practical approach to optimization of the reaction paths in complex multicomponent reactive catalytic systems.

Can we safely predict solvation Gibbs energies of pure and mixed solutes with a cubic equation of state?

Solvation Gibbs energies are basically defined as a chemical potential change when transferring a fixed molecule from a perfect gas to a real liquid mixture. This quantity is of special interest for many practical applications as it quantifies the degree of affinity of a solute for its solvent. Few methods are currently available in the literature for the prediction of solvation Gibbs energies. In this article, a new approach is proposed: the use of a predictive cubic equation of state (EoS). The UMR-PRU (Universal Mixing Rule Peng-Robinson UNIFAC) EoS has been selected for its known capacity to semi-predict behaviors of complex systems including polar and associating compounds (by semi-prediction, it is meant that the EoS predicts binary interaction parameters but requires pure-component properties as input parameters). UMR-PRU predictions have been compared to experimental data extracted from the extensive CompSol database (containing around 22 000 pure component data and 70 000 binary data). Accurate predictions were obtained (a mean absolute deviation of 0.36 kcal/mol was obtained for all the binary data). Finally, when using a fully-predictive approach (i.e. pure-component EoS parameters are predicted from group-contribution methods), the prediction accuracy is roughly preserved.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Thiosemicarbazone organocatalysis: tetrahydropyranylation and 2-deoxygalactosylation reactions and kinetics-based mechanistic investigation

The first use of thiosemicarbazone-based organocatalysis was demonstrated on both tetrahydropyranylation and 2-deoxygalactosylation reactions. The organocatalysts were optimised using kinetics-based selection. The best catalyst outperformed previously reported thiourea catalysts for tetrahydropyranylation by 50-fold. Hammett investigations of both the organocatalyst and the substrate indicate an oxyanion hole-like reaction mechanism.

Molecular dynamics simulation of C-C bond scission in polyethylene and linear alkanes: effects of the condensed phase

The reaction of C-C bond scission in polyethylene chains of various lengths was studied using molecular dynamics under the conditions of vacuum and condensed phase (polymer melt). A method of assigning meaningful rate constant values to condensed-phase bond scission reactions based on a kinetic mechanism accounting for dissociation, reverse recombination, and diffusional separation of fragments was developed. The developed method accounts for such condensed-phase phenomena as cage effects and diffusion of the decay products away from the reaction site. The results of C-C scission simulations indicate that per-bond rate constants decrease by an order of magnitude as the density of the system increases from vacuum to the normal density of a polyethylene melt. Additional calculations were performed to study the dependence of the rate constant on the length of the polymer chain under the conditions of the condensed phase. The calculations demonstrate that the rate constant is independent of the degree of polymerization if polyethylene samples of different lengths are kept at the same pressure. However, if instead molecular systems of different polyethylene chain lengths decompose under the conditions of the same density, shorter chains result in higher pressures and lower rate constants. The observed effect is attributed to a higher degree of molecular crowding (lower fraction of free intermolecular space available for molecular motion) in the case of shorter molecules.

On the coupling of solvent characteristics to the electronic structure of solute molecules

We present the results of a theoretical investigation focusing on the solvent structure surrounding the -1, 0 and +1 charged species of F, Cl, Br and I halogen atoms and F2, Cl2, Br2 and I2 di-halogen molecules in a methanol solvent and its influence on the electronic structure of the solute molecules. Our results show a large stabilizing effect arising from the solute-solvent interactions. Well-formed first solvation shells are observed for all species, the structure of which is strongly influenced by the charge of the solute species. Detailed analysis reveals that coordination number, CN, solvent orientation, θ, and solute-solvent distance, d, are important structural characteristics which are coupled to changes in the electronic structure of the solute. We propose that the fundamental chemistry of any solute species is generally regulated by these solvent degrees of freedom.

Lipophilicity assessment of ruthenium(II)-arene complexes by the means of reversed-phase thin-layer chromatography and DFT calculations

The lipophilicity of ten ruthenium(II)-arene complexes was assessed by reversed-phase thin-layer chromatography (RP-TLC) on octadecyl silica stationary phase. The binary solvent systems composed of water and acetonitrile were used as mobile phase in order to determine chromatographic descriptors for lipophilicity estimation. Octanol-water partition coefficient, logK_OW, of tested complexes was experimentally determined using twenty-eight standard solutes which were analyzed under the same chromatographic conditions as target substances. In addition, ab initio density functional theory (DFT) computational approach was employed to calculate logK_OW values from the differences in Gibbs' free solvation energies of the solute transfer from n-octanol to water. A good overall agreement between DFT calculated and experimentally determined logK_OW values was established (R² = 0.8024–0.9658).

New pathways for formation of acids and carbonyl products in low-temperature oxidation: the Korcek decomposition of γ-ketohydroperoxides

We present new reaction pathways relevant to low-temperature oxidation in gaseous and condensed phases. The new pathways originate from γ-ketohydroperoxides (KHP), which are well-known products in low-temperature oxidation and are assumed to react only via homolytic O-O dissociation in existing kinetic models. Our ab initio calculations identify new exothermic reactions of KHP forming a cyclic peroxide isomer, which decomposes via novel concerted reactions into carbonyl and carboxylic acid products. Geometries and frequencies of all stationary points are obtained using the M06-2X/MG3S DFT model chemistry, and energies are refined using RCCSD(T)-F12a/cc-pVTZ-F12 single-point calculations. Thermal rate coefficients are computed using variational transition-state theory (VTST) calculations with multidimensional tunneling contributions based on small-curvature tunneling (SCT). These are combined with multistructural partition functions (Q(MS-T)) to obtain direct dynamics multipath (MP-VTST/SCT) gas-phase rate coefficients. For comparison with liquid-phase measurements, solvent effects are included using continuum dielectric solvation models. The predicted rate coefficients are found to be in excellent agreement with experiment when due consideration is made for acid-catalyzed isomerization. This work provides theoretical confirmation of the 30-year-old hypothesis of Korcek and co-workers that KHPs are precursors to carboxylic acid formation, resolving an open problem in the kinetics of liquid-phase autoxidation. The significance of the new pathways in atmospheric chemistry, low-temperature combustion, and oxidation of biological lipids are discussed.