International Journal of Chemical Kinetics最新文献

We present ReactionMechanismSimulator.jl (RMS), a modern differentiable software for the simulation and analysis of chemical kinetic mechanisms, including multiphase systems. RMS has already been applied to problems in combustion, pyrolysis, polymers, pharmaceuticals, catalysis, and electrocatalysis. RMS is written in Julia, making it easy to develop and allowing it to take advantage of Julia's extensive numerical computing ecosystem. In addition to its extensive library of optimized analytic Jacobians, RMS can generate and use Jacobians computed using automatic differentiation and symbolically generated analytic Jacobians. RMS is demonstrated to be faster than Cantera and Chemkin in several benchmarks. RMS also implements an extensive set of features for analyzing chemical mechanisms, including a library of easy-to-call plotting functions, molecular structure resolved flux diagram generation, crash analysis, traditional sensitivity analysis, transitory sensitivity analysis, and an automatic mechanism analysis toolkit. RMS implements efficient adjoint and parallel forward sensitivity analyses. We also demonstrate the ease of adding new features to RMS.

This research investigates the kinetics of methylene blue (MB) discoloration using ambient air cold plasma, with a focus on the impact of agitation speed (100 and 750 rpm). The study revealed pseudo-first-order kinetics for MB discoloration, pinpointing optimal conditions at 35.00°C and 100 rpm. These parameters minimized half-life times, correlated with observed k_obs values. A decreasing pH trend, more pronounced at 750 rpm, was attributed to increased acidic nitrogen species (HNO₃ and HNO₂) production, adversely affecting dye discoloration. Concurrently, enhanced electrolyte concentration was noted from rising conductivity due to plasma production of reactive species followed by solubilization in the aqueous phase. The calculated thermodynamic activation parameters comprised: E_a = 7.96 kJ mol⁻¹, ΔH^‡ = +5.53 kJ mol⁻¹, ΔS^‡ = −253.23 J K⁻¹ mol⁻¹, and ΔG^‡ = +79.77 kJ mol⁻¹ (100 rpm); and E_a = 12.94 kJ mol⁻¹, ΔH^‡ = +13.78 kJ mol⁻¹, ΔS^‡ = −239.06 J K⁻¹ mol⁻¹, and ΔG^‡ = +80.58 kJ mol⁻¹, (750 rpm). The lowest E_a and ΔG^‡ values at 100 rpm reinforced lower agitation favoring the reaction. The study demonstrated a linear decay of the reaction rate constant with the square root of ionic strength. This result, besides the negative activation entropy and moderate activation enthalpy led to a proposition to the determinant step for the transition state formation, involving an associative step between a solvated electron and the protonated substrate. The optimal dye discoloration rate and energy yield were observed at 35.00°C and 100 rpm, with values of 97.2% and 3.371 × 10⁻² g kW⁻¹ h⁻¹, respectively.

Given its role as a primary side product and a potential soft oxidant in the oxidative coupling of methane (OCM), understanding the effect of CO₂ co-feeding on OCM emerges as a key milestone to optimize the process. To grasp the molecular impact of CO₂, a mechanistic investigation over a La-Sr/CaO catalyst was carried out via microkinetic modeling. Seven catalyst descriptors with a precise physico-chemical meaning were regressed for both pure O₂ and CO₂ co-feeding in order to assess eventual structural changes induced in the catalyst by the presence of CO₂ in the feed. Global significance was achieved in both regressions and experimental trends were successfully reproduced by the specifically determined catalyst descriptors. CO₂ co-feeding is deemed responsible for generating a new active phase, for example, by converting metal oxides into (oxy-)carbonates, among others, resulting in a decrease in active site density (D₁₆) from 10 × 10⁻⁵ mol/m² to 7 × 10⁻⁵ mol/m². In the presence of the CO₂-induced phase, the catalyst exhibits higher attraction for unsaturated hydrocarbons as indicated by the higher initial sticking probabilities of CH₃• (D₁₁) and C₂H₄ (D₁₅), which increase from 4.9 × 10⁻⁴ to 8 × 10⁻² and from 2.1 × 10⁻² to 3 × 10⁻², respectively. Additionally, there are also lower the overall energy barriers for the activation of hydrocarbons on the catalyst, stemming from the decrease in the H abstraction enthalpy from CH₄ (D₁) from 14 to 6 kJ/mol. The operating conditions, in particular the O₂ content, are critical in distinguishing the effect of CO₂ co-feeding. While at typical operating conditions, CO₂ promotes the total oxidation of methane, in the prerequisite of reduced amount of O₂, it may also act as an additional oxygen donor. This work provides molecular details on the CO₂ induced changes in catalyst properties but also provides unprecedent quantified insights of the reaction mechanism underlying experimental observations.

Aqueous-phase reforming (APR) is an interesting technique for generating hydrogen (H₂) from biofeeds. In this work, APR of model compounds of wet biomass for H₂ production was investigated. Glycerol, sorbitol, and glycine were the chosen model compounds. They represent polyols and amino acids in wet biomass such as waste sludge and microalgal biomass. The Pt/Al₂O₃ catalyst was preferred and it was characterized using nitrogen adsorption–desorption, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS) techniques. APR trials were performed in a continuous fixed-bed reactor. The reaction conditions chosen for this work were: temperature (T) 453–498 K, pressure (P) 1.2–2.4 MPa, feed concentration 5–15 wt%, and weight hourly space velocity (WHSV) 0.15–0.6 g reactant/(g catalyst h). The best conditions for H₂ production by the APR process were found to be T = 498 K, P = 2.4 MPa, and feed concentration = 15 wt%. Among the chosen model compounds, glycerol exhibited the highest H₂ selectivity (82.7%) and H₂ yield (21.6%) at 498 K. The analysis of kinetic data suggested first-order reaction kinetics for all the model compounds. The values of activation energy for the reactions with glycerol (55.4 kJ/mol), sorbitol (51.6 kJ/mol), and glycine (45.7 kJ/mol) were determined. Thus, APR is a promising route for effectively producing H₂-bearing gaseous products with high heating value from wet biomass.

This study investigates the thermal degradation kinetics of mesoporous triazine-based polymers, namely triazine-amine and triazine-ether polymers. The synthesis, physicochemical characterization, and catalytic applications of these polymers were discussed in our previous report. Herein, the thermal stability parameters, including kinetic triplets and thermodynamic parameters, were determined using thermogravimetric analysis (TGA) and non-isothermal mathematical approximations such as Coats-Redfern, Broido, and Horowitz–Metzger methods. Triazine-ether polymers exhibit thermal stability within the range of 200°C–300°C, while triazine-amine polymer demonstrates superior thermal stability, reaching up to 450°C. According to the Coats-Redfern method, the degradation follows reaction orders of 0.5 ≤ n ≤ 1. The activation energy of triazine-amine polymer is notably high, particularly at the third degradation stage (e.g., 89.0 kJ/mol by the Broido method), attributed to its high nitrogen content. Conversely, the higher carbon content of triazine-ether polymers reduces their activation energy to approximately 30 kJ/mol at all stages and thus, facilitates the degradation process. Thermodynamically, the degradation process is favorable yet non-spontaneous, with intermediate states of the polymers exhibiting higher entropy, indicative of their enhanced degradation capability.

The current research highlighted the usage of serpentine clay to remove Nile red dye from an aqueous solution. At first serpentine clay minerals were analyzed by various analytical techniques like Fourier transform infrared spectroscopy (FTIR), X ray diffraction (XRD), and thermal gravimetric analysis (TGA) analysis. From the characterization results it was found that the clay was determined to be a separate group. Sorption studies investigated the impacts of adsorbent dosage, initial pH, initial dye concentration, and temperature on Nile red color elimination. From the test results it was found that the capacity of adsorption was seen to increase from 32.4 mg/g to a high value of 43.8 mg/g by raising the pH value from 2 to 6. Adsorption on serpentine clay decreased from 234.7 to 33.2 mg/g due to an increase in the adsorbent dosage. The removal capacity of Nile red dye increased from 12.2% to 88.5% with the rise in the adsorbent dosage. This rise in the Nile red dye removal may be observed due to the increase in the area as well as the pore volume of the surface. Experimental study was carried out to study the effect of initial concentration of adsorbate on adsorption at a pH of 6, adsorbent dosage of 3 g/L, and at a temperature of 28°C. The removal efficiency of the Nile red dye was reduced from 96.7% to 42.6%. To determine the temperature effect on the removal of Nile red dye by the clay, the initial pH value was set to 6, and the temperature was set at 28, 38, 48, and 58°C. Without reaching the equilibrium conditions, at a time of 30 min, the removal efficiency of dye rises from 60% to 81% due to the temperature rise. The experimental findings indicated that the adsorption of the dye on the clay followed the “Langmuir adsorption” isotherm rather than the Freundlich adsorption isotherm. Adsorption on clay minerals follows the pseudo-second-order adsorption kinetics compared to pseudo-first-order adsorption kinetics.

As part of a series of studies of hydrogen-atom transfer or tautomerization reactions of imidic acid-amide species, $�H$─$�O$─$�C$═$�N$─ $��\rightleftharpoons �O�$═$�C$─$�\text{NH}$─, we report the rate constants for a set of 16 four-membered cyclic compounds at low, 50–300 K, and high, 500–1500 K, temperatures. The compounds are labeled according to the two ring groups X and Y, which can be $�{\text{CH}}_2$, NH, CH, N, O, or C(O) and which are at some remove from the reactive site. These rate constants are for both the direct reaction and for that mediated by an additional water molecule, which facilitates the hydrogen transfer reaction. In the latter case, we show that the rate of reaction from a pre-reaction complex is rapid at temperatures down to 50 K and dominated by quantum mechanical effects as evaluated by small-curvature and quantized-reaction-states tunneling. In addition, we present thermochemical data such as enthalpies of formation, entropies, isobaric heat capacities, and enthalpy functions for these largely unknown species, which span a range of compounds from $�\beta$-propiolactone to 1,3-diazetidine-2,4-dione.

A longstanding project of the chemical kinetics community is to predict reaction rates and the behavior of reacting systems, even for systems where there are no experimental data. Many important reacting systems (atmosphere, combustion, pyrolysis, partial oxidations) involve a large number of reactions occurring simultaneously, and reaction intermediates that have never been observed, making this goal even more challenging. Improvements in our ability to compute rate coefficients and other important parameters accurately from first principles, and improvements in automated kinetic modeling software, have partially overcome many challenges. Indeed, in some cases quite complicated kinetic models have been constructed which accurately predicted the results of independent experiments. However, the process of constructing the models, and deciding which reactions to measure or compute ab initio, relies on accurate estimates (and indeed most of the numerical rate parameters in most large kinetic models are estimates.) Machine-learned models trained on large datasets can improve the accuracy of these estimates, and allow a better integration of quantum chemistry and experimental data. The need for continued development of shared (perhaps open-source) software and databases, and some directions for improvement, are highlighted. As we model more complicated systems, many of the weaknesses of the traditional ways of doing chemical kinetic modeling, and of testing kinetic models, have been exposed, identifying several challenges for future research by the community.

In the realm of combustion kinetic modeling, the norm involves employing thousands of reactions to delineate the chemical conversion of hundreds of species. Notably, theoretically predicted rate coefficients and branching ratios, derived through the RRKM/master equation (ME) model, play an increasing role in kinetic modeling. Thus minimizing the uncertainty of theoretical prediction across wide working conditions is crucial to refine a kinetic model. The present study takes ethyl (C₂H₅) + oxygen (O₂) reaction system to show that combined forward and reverse uncertainty analysis can be used to further constrain calculated rate coefficients and branching ratios, which were already calculated by high-level quantum chemistry methods. Forward global uncertainty analysis with the artificial neural network-high dimensional model representation (ANN-HDMR) method is employed to select key parameters affecting total rate coefficients of C₂H₅ + O₂ and branching ratios of C₂H₅ + O₂ = C₂H₄ + HO₂ (C1). Reverse uncertainty analysis with Bayesian method was then applied to refine the key input parameters based on experimental data at working conditions selected by sensitivity entropy. Although the target RRKM/ME model system was built on high level theoretical calculations, the combined forward and reverse uncertainty analyses are still able to reduce uncertainties of predicted total rate coefficients of C₂H₅ + O₂ and branching ratios for C1 across a wide range of working conditions. Specifically, the uncertainties of total rate coefficient and C1 branching ratio have been reduced from 1.46 and 1.52 to 1.30 and 1.36 at 298 K and 1 Torr. The analysis process proposed in the present work effectively extrapolates the constraint ability of accurate measured data at one condition to wide working conditions based on the RRKM/ME model.

For net-zero carbon emissions, hydrogen/ammonia blends have drawn considerable attention for the application in industrial combustion devices. Various chemical mechanisms have been developed to describe the oxidation and combustion of hydrogen/ammonia mixtures at certain conditions. A comprehensive evaluation and comparison of the performance of these mechanisms is thus of high interest, especially in terms of their application for particular computational studies. Thus, this work aims to compare the existing chemical mechanisms in terms of their performance for the combustion of hydrogen/ammonia mixtures over a wide range of experimental conditions. In addition to previous literature studies, the model performance is evaluated by using two different methods for the assessment of prediction accuracy. Besides the conventional measure of point-wise differences between model and data, the curve-matching method is also applied, which quantifies the dependence of model response on physical conditions additionally, by comparing the similarity between the curve shapes of the predicted and measured results. Extensive experimental data are taken into account in the model evaluation, including 136 datasets obtained from various facilities in the past 10 years. Nineteen mechanisms are compared, which were published in recent five years. It is revealed that these models give strongly different numerical results for combustion targets, such as laminar burning velocities, ignition delay times, and species concentrations. The chemical mechanisms of Zhang et al. (2021), Han et al. (2023), Mei et al. (2019), Li et al. (2019), and Stagni et al. (2020) show relatively satisfactory performance over the entire investigated domain. Moreover, it is found that the estimated prediction accuracy of chemical mechanisms is highly sensitive to model evaluation methods.