Computer‐assisted synthetic planning considering reaction kinetics based on transition state automated generation method

Theoretical Study on Reaction Pathways of Methyl Radical with Ethylamine

Reaction kinetics for the hydrothermal carbonisation of cellulose in a two-phase pathway

Time-resolved Fourier transform infrared spectroscopy (FTIR) in combination with photo-induced release of (18)O-labeled caged nucleotide has been employed to address mechanistic issues of GTP hydrolysis by Ras protein. Infrared spectroscopy of Ras complexes with nitrophenylethyl (NPE)-[alpha-(18)O(2)]GTP, NPE-[beta-(18)O(4)]GTP, or NPE-[gamma-(18)O(3)]GTP upon photolysis or during hydrolysis afforded a substantially improved mode assignment of phosphoryl group absorptions. Photolysis spectra of hydroxyphenylacyl-GTP and hydroxyphenylacyl-GDP bound to Ras and several mutants, Ras(Gly(12))-Mn(2+), Ras(Pro(12)), Ras(Ala(12)), and Ras(Val(12)), were obtained and yielded valuable information about structures of GTP or GDP bound to Ras mutants. IR spectra revealed stronger binding of GDP beta-PO(3)(2-) moiety by Ras mutants with higher activity, suggesting that the transition state is largely GDP-like. Analysis of the photolysis and hydrolysis FTIR spectra of the [beta-nonbridge-(18)O(2), alphabeta-bridge-(18)O]GTP isotopomer allowed us to probe for positional isotope exchange. Such a reaction might signal the existence of metaphosphate as a discrete intermediate, a key species for a dissociative mechanism. No positional isotope exchange was observed. Overall, our results support a concerted mechanism, but the transition state seems to have a considerable amount of dissociative character. This work demonstrates that time-resolved FTIR is highly suitable for monitoring positional isotope exchange and advantageous in many aspects over previously used methods, such as (31)P NMR and mass spectrometry.

Reaction solvents are capable of significantly enhancing the kinetic rate and selectivity of chemical reactions through the solvation effects. However, the commonly used reaction kinetic models considering the solvation effects are based on the transition state theory, which is generally limited to the demanding exploration of transition states. In this paper, a reaction solvent screen framework is established for screening potential reaction solvents, where a novel reaction kinetic model is proposed based on the frontier molecular orbital theory correlating the reaction rate constants with the numbers of hydrogen bond donors/acceptors of solvents and the solvation HOMO/LUMO energies of reactants, which quantify the solvation effects on reaction systems independent of transition states and are calculated through the implicit solvation model using the solvent dielectric constants. To facilitate high-throughput screening of reaction solvents in a wide search space, a tailor-made text-based deep learning model is then developed for fast and accurate predictions of dielectric constants. Finally, two case studies, namely, SNAr and Menschutkin reactions, are presented to demonstrate the feasibility and effectiveness of the proposed reaction solvent screen framework.

Accurate prediction of chemical reaction barrier heights is crucial, yet traditional quantum mechanical methods are computationally expensive, and existing deep learning models often rely on costly three-dimensional transition state geometries or struggle with out-of-distribution generalization. This work introduces ReactFormer, a novel deep learning framework designed to predict reaction barrier heights with high accuracy solely from two-dimensional molecular graphs of reactants, thus eliminating the need for explicit transition state information. ReactFormer employs a reaction-aware graph Transformer architecture, which includes multi-view reactant encoding and a unique Virtual Reaction Center Node, enabling it to effectively capture complex reaction features and implicitly infer transition state characteristics. Evaluated on the RDB7 dataset, ReactFormer achieves a Mean Absolute Error of 2.40 $\pm$ 0.12 kcal/mol on the random split, matching or even slightly surpassing the performance of three-dimensional dependent models. Furthermore, ReactFormer demonstrates significantly improved computational efficiency and robust generalization across various out-of-distribution scenarios, including splits based on molecular size, target barrier height, reaction core, and reactant scaffold. An ablation study confirms the critical contributions of its core components. ReactFormer represents a significant step towards efficient, accurate, and generalizable deep learning models for chemical reaction dynamics, fostering advancements in drug discovery, materials science, and catalysis.

Although natural gas hydrates and hydrate exploration have been extensively studied for decades, the reaction kinetics and nucleation mechanism of hydrate formation is not fully understood. In its early stage, gas hydrate formation can be assumed to be an autocatalytic kinetic reaction with nucleation and initial growth. In this work, a reaction kinetics model has been established to form structure II methane–propane hydrate in an isochoric reactor. The computational model consists of six pseudo-elementary reactions for three dynamic processes: (1) gas dissolution into the bulk liquid, (2) a slow buildup of hydrate precursors for nucleation onset, and (3) rapid and autocatalytic hydrate growth after onset. The model was programmed using FORTRAN, with initiating parameters and rate constants that were derived or obtained from data fitted using experimental results. The simulations indicate that the length of nucleation induction is determined largely by an accumulation of oligomeric hydrate precursors up to a threshold value. The slow accumulation of precursors is the rate-limiting step for the overall hydrate formation, and its conversion into hydrate particles is critical for the rapid, autocatalytic reaction. By applying this model, the memory effect for hydrate nucleation was studied by assigning varied initial amounts of precursor or hydrate species in the simulations. The presence of pre-existing precursors or hydrate particles could facilitate the nucleation stage with a reduced induction time, and without affecting hydrate growth. The computational model with the performed simulations provides insight into the reaction kinetics and nucleation mechanism of hydrate formation.

Multi-fluid modeling biomass fast pryolysis with particle shrinkage model for complex reaction kinetics

Kinetic model for modern double-layered Pd/Rh TWC as a function of metal loadings and mileage

Initial state-selected reaction probabilities for the H+CH(4)→H(2)+CH(3) reaction are computed for vanishing total angular momentum by full-dimensional calculations employing the multiconfigurational time-dependent Hartree approach. An ensemble of wave packets completely describing reactivity for total energies up to 0.58 eV is constructed in the transition state region by diagonalization of the thermal flux operator. These wave packets are then propagated into the reactant asymptotic region to obtain the initial state-selected reaction probabilities. Reaction probabilities for reactants in all rotational states of the vibrational 1A(1), 1F(2), and 1E levels of methane are presented. Vibrational excitation is found to decrease reactivity when reaction probabilities at equivalent total energies are compared but to increase reaction probabilities when the comparison is done at the basis of equivalent collision energies. Only a fraction of the initial vibrational energy can be utilized to promote the reaction. The effect of rotational excitation on the reactivity differs depending on the initial vibrational state of methane. For the 1A(1) and 1F(2) vibrational states of methane, rotational excitation decreases the reaction probability even when comparing reaction probabilities at equivalent collision energies. In contrast, rotational energy is even more efficient than translational energy in increasing the reaction probability when the reaction starts from the 1E vibrational state of methane. All findings can be explained employing a transition state based interpretation of the reaction process.

Multicomponent reaction kinetics for the ozonation of [formula omitted]-nitrophenol and its decomposition products under acidic conditions at constant pH

Reaction kinetics measurements and analysis of reaction pathways for conversions of acetic acid, ethanol, and ethyl acetate over silica-supported Pt

The effect of citrate ions in reaction solution on reaction kinetics of Pt deposition on Pd via SLRR of Cu UPD monolayer has been studied[i]. Experiments involving both, single crystal Pd and Pd-nanoparticle substrates were performed. The results suggest that citrate has a suppressive role on Pt deposition kinetics which is manifested through the zero order reaction kinetics during red-ox process[ii]. The citrate adsorbed phase on Pd surface has mainly transport hindering role for upcoming Pt ions. The IR data suggest that Pt monolayers produced from citrate containing electrolyte have weaker electrosorption ability for CO than bulk Pt and Pd. This information should be useful in further application of citrates as reactant and additive for Pt deposition via SLRR which can be used to fine tune Pt monolayer reactivity on different substrates[iii]. The reaction kinetics model was developed to analyze our kinetics data where the citrate effect was described as an extra barrier for the Pt ion transport. The model is capable of predicting the reaction rate constants from known concentrations of the citrate and platinum ions in the solution using only two model parameters, X and B3. Both parameters have the real physical meaning which could be directly visualized as the additional diffusion path for Pt ions due to the presence of adsorbed citrate layer on CuUPD/Pd-NP surface. The analytical work could be further applied in design and development of more sophisticated apparatus for Pt-shell Pd-core catalyst synthesis. This aspects will be discuss in the conclusion of the talk. The authors acknowledge the private funding of this work and the support from NSF Chemistry division under the contract # 0955922.

Hydroperoxyl () is detected in the effluent of an atmospheric pressure plasma jet (PJ) by cavity ring-down absorption spectroscopy. Laser radiation at is used to determine concentrations directly in the plasma effluent at atmospheric pressure as well as indirectly after sampling through a pinhole and expanding to . To unravel the reaction kinetics, the argon feed gas is humidified and the PJ is equipped with a gas curtain device that controls the surrounding atmosphere. Varying the curtain gas composition from pure nitrogen to pure oxygen leads to a five to six-fold increase in concentrations towards higher oxygen concentrations. A reaction kinetics model considering near-field and far-field reactions describes the reaction pathways that determine the hydroperoxyl concentration and is in good agreement with the measurements at both low and atmospheric pressure.

Reaction kinetics analysis of branched-chain alkyl esters of palmitic acid and cold flow properties

Continuous stirred-tank reactors (CSTRs) are integral to industrial chemical processes due to their uniform mixing and steady-state operation. The saponification of ethyl acetate with sodium hydroxide is a widely used model reaction for studying kinetics in CSTRs. Understanding the sensitivity of reaction kinetics to operating conditions is critical for enhancing reactor performance, ensuring consistent product quality, and minimizing inefficiencies. Reaction kinetics in a CSTR are influenced by reactant concentrations, temperature, and flow rate. Misidentifying or poorly optimizing these parameters can result in suboptimal performance and reduced conversion efficiency, posing challenges for industrial applications. This study conducts a detailed sensitivity analysis to identify the key parameters influencing the saponification reaction in a CSTR. It quantifies how variations in these parameters affect conversion efficiency and overall reaction performance, providing actionable insights for process optimisation. A reaction kinetics model was selected and validated using robust pilot-plant experimental data. Parameters, including reactor temperature, were systematically analysed through numerical simulations to assess their sensitivity and impact on system performance. The validated model demonstrated strong agreement with experimental results, yielding RMSE values ranging from 0.0007 to 0.0013 and R² values between 0.9996 and 0.9999 across 30, 40, 50, and 60 °C. Maximum deviation remained below 0.6 %, highlighting the model’s predictive accuracy and industrial applicability. Sensitivity analysis showed the greatest marginal benefit between 30 and 40 °C, with a peak sensitivity ratio of 0.00072, indicating that small temperature adjustments in this range have the greatest impact on the final conversion of ethyl acetate. This study sets itself apart from classical saponification research by integrating pilot-plant data with MATLAB-based analysis, offering a validated dataset and a meaningful contribution to chemical reaction engineering.