In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (SN2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method\'s performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

VOCs can be used instead of ammonia as a reducing agent to remove NO, achieving the effect of removing VOCs and NO simultaneously. Due to the high energy consumption and low photocatalytic efficiency required for conventional thermocatalytic purification, photothermal coupled catalytic purification can integrate the advantages of photocatalysis and thermocatalysis in order to achieve the effect of pollutants being treated efficiently with a low energy consumption. In this study, samples loaded with Co and Mn catalysts were prepared using the hydrothermal method on Fe-MOF with various morphologies. The catalytic performance of each catalyst was analyzed by studying the effects of their physicochemical properties through various characterizations, including XRD, SEM, BET, XPS, H2-TPR, TEM and O2-TPD. The characterization results demonstrated that the specific surface area, pore volume, high valence Co and Mn atoms, surface adsorbed oxygen and the abundance of oxygen lattice defects in the catalysts were the most critical factors affecting the performance of the catalysts. Based on the results of the performance tests, the catalysts prepared with an octahedral-shaped Fe-MOF loaded with Co and Mn showed a better performance than those loaded with Co and Mn on a rod-shaped Fe-MOF. The conversions of acetone and NO reached 50% and 64%, respectively, at 240 °C. The results showed that the catalysts were capable of removing acetone and NO at the same time. Compared with the pure Fe-MOF without Co and Mn, the loaded catalysts showed a significantly higher ability to remove acetone and NO simultaneously under the combination of various factors. The key reaction steps for the catalytic conversion of acetone and NO on the catalyst surface were investigated according to the Mars-van Krevelen (MvK) mechanism, and a possible mechanism was proposed. This study presents a new idea for the simultaneous removal of acetone and NOx by photothermal coupling.

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

This study investigates the application of quantum mechanical (QM) and multiscale computational methods in understanding the reaction mechanisms and kinetics of SN2 reactions involving methyl iodide with NH2OH and NH2O-, as well as the Claisen rearrangement of 8-(vinyloxy)dec-9-enoate. Our aim is to evaluate the accuracy and effectiveness of these methods in predicting experimental outcomes for these organic reactions. We achieve this by employing QM-only calculations and several hybrids of QM and molecular mechanics (MM) methods, namely QM/MM, QM1/QM2, and QM1/QM2/MM methodologies. For the SN2 reactions, our results demonstrate the importance of explicitly including solvent effects in the calculations to accurately reproduce the transition state geometry and energetics. The multiscale methods, particularly QM/MM and QM1/QM2, show promising performance in predicting activation energies. Moreover, we observe that the size of the MM active region significantly affects the accuracy of calculated activation energies, highlighting the need for careful consideration during the setup of multiscale calculations. In the case of the Claisen rearrangement, both QM-only and multiscale methods successfully reproduce the proposed reaction mechanism. However, the activation free energies calculated using a continuum solvation model, based on single-point calculations of QM-only structures, fail to account for solvent effects. On the other hand, multiscale methods more accurately capture the impact of solvents on activation free energies, with systematic error correction enhancing the accuracy of the results. Furthermore, we introduce a Python code for setting up multiscale calculations with ORCA, which is available on GitHub at https://github.com/iranimehdi/pdbtoORCA .

Studies have shown that the incorporation of fluorine into materials can improve their properties, but C-F bonds are not readily formed in nature. Although some researchers have studied the reaction of fluorinating alkenes catalyzed by hypervalent iodine, far too little attention has been paid to its reaction mechanism. This study aimed to explore the mechanism of the hypervalent iodine-catalyzed 1,4-difluorination of dienes. We found that the catalyst is favorable for the activation of C1=C2 double bonds through halogen bonds, and then two HFs interact with one F atom in the catalyst via hydrogen bonds, resulting in the cleavage of I-F bonds and the formation of [F-H∙∙∙F]-. Subsequently, the catalyst interacts with C1, and the roaming [F-H···F]- attacks C4 from the opposite side of the catalyst. After the fluorination step is completed, the nucleophile F- substitutes the catalyst via the SN2 mechanism. Our calculations demonstrated that the interaction between HF and F- is favorable for the stabilization of the transition state within the fluorination process for which the presence of two HFs in the reaction is the best. We also observed that [F-H∙∙∙F]- attacking C4 from the opposite side of the catalyst is more advantageous than attacking from the same side. This study therefore offers a novel perspective on the mechanism of the hypervalent iodine-catalyzed fluoridation of dienes.

Precise electrochemical synthesis of commodity chemicals and fuels from CO2 building blocks provides a promising route to close the anthropogenic carbon cycle, in which renewable but intermittent electricity could be stored within the greenhouse gas molecules. Here, we report state-of-the-art CO2-to-HCOOH valorization performance over a multiscale optimized Cu-Bi cathodic architecture, delivering a formate Faradaic efficiency exceeding 95% within an aqueous electrolyzer, a C-basis HCOOH purity above 99.8% within a solid-state electrolyzer operated at 100 mA cm-2 for 200 h and an energy efficiency of 39.2%, as well as a tunable aqueous HCOOH concentration ranging from 2.7 to 92.1 wt%. Via a combined two-dimensional reaction phase diagram and finite element analysis, we highlight the role of local geometries of Cu and Bi in branching the adsorption strength for key intermediates like *COOH and *OCHO for CO2 reduction, while the crystal orbital Hamiltonian population analysis rationalizes the vital contribution from moderate binding strength of η2(O,O)-OCHO on Cu-doped Bi surface in promoting HCOOH electrosynthesis. The findings of this study not only shed light on the tuning knobs for precise CO2 valorization, but also provide a different research paradigm for advancing the activity and selectivity optimization in a broad range of electrosynthetic systems.

Constructing structure-function relationships is critical for the rational design and development of efficient catalysts for CO2 electroreduction reaction (CO2RR). In2O3 is well-known for its specific ability to produce formic acid. However, how the crystal phase and surface affect the CO2RR activity is still unclear, making it difficult to further improve the intrinsic activity and screen for the most active structure. In this work, cubic and hexagonal In2O3 with different stable surfaces ((111) and (110) for cubic, (120) and (104) for hexagonal) are investigated for CO2RR. Theoretical results demonstrate that the adsorption of reactants on cubic In2O3 is stronger than that on hexagonal In2O3, with the cubic (111) surface being the most active for CO2RR. In experiments, synthesized cubic In2O3 nanosheets with predominantly exposed (111) surfaces exhibited a high HCOO- Faradaic efficiency (87.5%) and HCOO- current density (-16.7 mA cm-2) at -0.9 V vs RHE. In addition, an aqueous Zn-CO2 battery based on a cubic In2O3 cathode was assembled. Our work correlates the phases and surfaces with the CO2RR activity, and provides a fundamental understanding of the structure-function relationship of In2O3, thereby contributing to further improvements in its CO2RR activity. Moreover, the results provide a principle for the directional preparation of materials with optimal phases and surfaces for efficient electrocatalysis.

Understanding the complete reaction network and mechanism of methanol-to-hydrocarbons remains a key challenge in the field of zeolite catalysis and C1 chemistry. Inspired by the identification of the reactive surface methoxy species on solid acids, several direct mechanisms associated with the formation of the first C-C bond in methanol conversion have been recently disclosed. Identifying the stepwise involvement of the initial intermediates containing the first C-C bond in the whole reaction process of methanol-to-hydrocarbons conversion becomes possible and attractive for the further development of this important reaction. Herein, several initial unsaturated aldehydes/ketones containing the C-C bond are identified via complementary spectroscopic techniques. With the combination of kinetic and spectroscopic analyses, a complete roadmap of the zeolite-catalyzed methanol-to-hydrocarbons conversion from the initial C-C bonds to the hydrocarbon pool species via the oxygen-containing unsaturated intermediates is clearly illustrated. With the participation of both Brønsted and Lewis acid sites in H-ZSM-5 zeolite, an initial aldol-cycle is proposed, which can be closely connected to the well-known dual-cycle mechanism in the methanol-to-hydrocarbons conversion.

Electrochemiluminescence (ECL) of luminol is a luminescence process that proceeds in the presence of reactive oxygen species (e.g. hydrogen peroxide (H2O2)) at a suitable electrode potential, the reaction mechanism of which is complicated and remains ambiguous. In this work, we report a visualization approach for measuring the thickness of the ECL layer (TEL) of the luminol/H2O2 system to decipher the reaction process by combined use of the microtube electrode, ECL microscopy, and finite element simulations. With the increase of solution pH, the ECL image captured with the microtube electrode tends to vary from spot to ring, corresponding to the decrease of TEL from >9.1 μm to ca. 4.3 μm. We propose that different intermediates are involved in the course of ECL reaction. At a low pH (e.g. pH < 9), a relatively large TEL is most likely determined by the diffusion of oxidized and deprotonated luminol intermediate that is neutral and has a long lifetime. While at a high pH (e.g. pH in the range of 10 to 12), the ECL reaction is controlled by short-lived radical intermediates of both luminol and superoxide anion. The proposed mechanism is proved theoretically by finite element simulations and experimentally by the apparent effect of concentration ratio of luminol/H2O2.

Aromaticity, in general, can promote a given reaction by stabilizing a transition state or a product via a mobility of π electrons in a cyclic structure. Similarly, such a promotion could be also achieved by destabilizing an antiaromatic reactant. However, both aromaticity and transition states cannot be directly measured in experiment. Thus, computational chemistry has been becoming a key tool to understand the aromaticity-driven reaction mechanisms. In this review, we will analyze the relationship between aromaticity and reaction mechanism to highlight the importance of density functional theory calculations and present it according to an approach via either aromatizing a transition state/product or destabilizing a reactant by antiaromaticity. Specifically, we will start with a particularly challenging example of dinitrogen activation followed by other small-molecule activation, C-F bond activation, rearrangement, as well as metathesis reactions. In addition, antiaromaticity-promoted dihydrogen activation, CO2 capture, and oxygen reduction reactions will be also briefly discussed. Finally, caution must be cast as the magnitude of the aromaticity in the transition states is not particularly high in most cases. Thus, a proof of an adequate electron delocalization rather than a complete ring current is recommended to support the relatively weak aromaticity in these transition states.