International Journal of Chemical Kinetics最新文献

The oxidation of ammonium sulfite must be inhibited to improve the economy and cycle performance of the ammonia flue gas desulfurization process. A stirred bubbling reactor was used to investigate the macrokinetics of oxidation inhibition of ammonium sulfite by sodium thiosulfate in wet ammonia desulfurization. The effects of initial $�{\mathrm{SO}}_3^{�2�-�}$ concentration, Na₂S₂O₃ concentration, pH value, gas flow rate, and temperature on the oxidation rate were discussed. The results show that the apparent activation energy of the reaction is 37.3 kJ/mol. The macrokinetics equation of sodium thiosulfate inhibiting sulfite oxidation in the ammonia desulfurization process was established. Combined with the kinetic model, it is inferred that the rate of sodium thiosulfate inhibiting ammonium sulfite oxidation is controlled by the intrinsic chemical reaction. The results can provide a reference for the regeneration of ammonium sulfite in the wet ammonia desulfurization process.

Long path length FTIR-smog chamber techniques were used to study the title reactions in 650 Torr of N₂, oxygen, or air diluent at 296 ± 3 K. Values of k(Cl + (Z)-CF₂HCF = CHCl)═(6.6 ± 0.7) × 10⁻¹¹ and k(OH + (Z)-CF₂HCF═CHCl)═(4.1 ± 0.7) × 10⁻¹² cm³ molecule⁻¹ s⁻¹ were measured. The IR spectrum of (Z)-CF₂HCF═CHCl is reported. The atmospheric lifetime of (Z)-CF₂HCF═CHCl is determined by the reaction with OH and is approximately 2.8 days. Reaction of (Z)-CF₂HCF═CHCl with Cl atoms gives HC(O)Cl and CF₂HC(O)F as major primary products. Under environmental conditions, the OH radical initiated oxidation gives CF₂HC(O)F and HC(O)Cl in yields of (98 ± 8)% and (100 ± 4)%, respectively. Accounting for non-uniform horizontal and vertical mixing leads to a 100-year time-horizon global warming potential value for (Z)-CF₂HCF═CHCl of essentially zero.

Many important chemical kinetic systems require detailed chemical kinetic models to resolve. These detailed kinetic models can involve thousands of species and hundreds of thousands of chemical reactions, making them difficult to construct by hand. Modern automatic mechanism generation algorithms can mostly be divided into two classes: rule and rate based. Rule-based generators choose species based on user defined constraints on species and reaction classes. Rate-based generators generate a much larger set of potentially important species and reactions and then choose which ones to add based on running simulations of species and reactions deemed important and calculating the flux to potentially important species. In principle, the latter is preferable, as it requires the user to make far fewer assumptions about what is important in the system. However, while the effectiveness of the rate-based approach has been demonstrated in a wide variety of systems, it has also been demonstrated to have difficulty picking up important low-flux chemistries. Here we present a discussion of the challenges associated with rate-based mechanism generation and new algorithms that are able to efficiently mitigate these challenges improving species selection during mechanism generation in a set of case studies.

Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However, such potentials are only as good as the data they are trained on, and building a comprehensive training set can be a costly process. Therefore, it is important to extract as much information from training data as possible without further increasing the computational cost. One way to accomplish this is by training on molecular forces in addition to energies. This allows for three additional labels per atom within the molecule. Here we develop a neural network potential energy surface for studying a hydrogen transfer reaction between two isomers of $��C_5�H_5�$. We show that, for a much smaller training set, force training not only improves the accuracy of the model compared to only training on energies, but also provides more accurate and smoother first and second derivatives that are crucial to run dynamics and extract vibrational frequencies in the context of transition-state theory. We also demonstrate the importance of choosing the proper force to energy weight ratio for the loss function to minimize the model test error.

This study combines experimental and theoretical explorations. The corrosion inhibition performance of different concentrations (50, 100, 250, and 500 ppm) of folic acid in 3.5 wt% NaCl solution on Q235 steel was tested by weight loss and electrochemical test. The corrosion inhibition efficiency increased with the gradual increase of folic acid concentration and reached a maximum of 87% at 500 ppm folic acid. The experimental results for electrochemistry and weight loss are in good agreement with the simulation calculations. The adsorption of folic acid on the steel surface obeyed the Langmuir isotherm, and the adsorption process was a combination of chemisorption and physisorption. The contact angle test also yielded the maximum increase in hydrophobicity of the specimen surface at the added folic acid concentration of 500 ppm. The corrosion morphology after the addition of corrosion inhibitor was relatively flat. The adsorption orientation of folic acid molecules on the steel surface in an aqueous environment was investigated using density functional theory (DFT)/molecular dynamics (MD) simulations. The microscopic mechanism of action of folic acid corrosion inhibitors is clarified.

In the current report, both response surface methodology (RSM) and artificial neural network (ANN) were employed to develop an innovative way for removing crystal violet (CV) from aqueous media using Haloxylon salicornicum (HS) as a cost-effective, eco-friendly adsorbent. HS was characterized using scanning electron microscopy (SEM) and Fourier-transform infrared (FTIR) spectroscopy. The effects of operational parameters such as adsorbent dosage, initial dye concentration, and pH on HS were studied using a central composite design (CCD). A comparative analysis of the model findings and experimental measurements revealed high correlation coefficients (R2ANN = 0.994, R2RSM = 0.971), indicating both models accurately predicted HS. The predictive performance of the ANN and RSM models was evaluated using metrics such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE), mean square error (MSE), and the correlation coefficient (R²). The results indicate that the ANN model provides greater accuracy compared to the RSM model. The experimental data were analyzed using both linear and nonlinear forms of pseudo-first and pseudo-second order kinetic models (LPFO, NLPFO, LPSO, and NLPSO). Statistical error analysis was conducted to identify the best-fitting kinetic or isotherm models for the adsorption data. The adsorption process of CV/HS was best described by NLPSO and LPSO. Additionally, the adsorption isotherms were analyzed using linear and nonlinear regression methods. The findings indicated that the linear Langmuir and Freundlich isotherms provided a more accurate fit compared to the nonlinear models, demonstrating greater effectiveness in accounting for the adsorption parameters. Thermodynamic investigations clearly demonstrate that the biosorption of CV is spontaneous and exothermic. This cost-effective adsorbent is highly promising for treating textile wastewater.

In the original article published in 2009, Equation (3) after correction was used to determine values of β_n; hence, there are no corrections to values of β_n in Table V.

We apologize for this error.

Fcc-Ti_xAl_1–xN (TiAlN) coatings synthesized via chemical vapor deposition (CVD) reduce cutting tool wear. Although CVD conditions reportedly influence coating quality, no quantitative guidelines are yet available. To quantitatively study the film-forming mechanism of TiAlN CVD, the gas composition over the surface must be known. Therefore, we developed a gas-phase elementary reaction model for TiAlN CVD derived from TiCl₄/AlCl₃/NH₃. First, we constructed a novel thermodynamic dataset including molecules that contained both Ti and Al, and calculated the equilibrium composition. Thermal equilibrium calculations in the gas phase showed that the most stable species were AlCl₃ and TiCl₃ rather than TiCl₄. An elementary reaction model was constructed based on the kinetics of the gas-phase species that were generated. Kinetic analysis revealed that gas-phase reactions were largely absent under our reactor conditions. The thermal equilibrium calculations indicated that TiCl₄ may have given rise to TiCl₃. Thus, other reaction pathways of TiCl₄ to TiCl₃ were explored. We calculated the reaction rate constants of 12 reactions of Ti species and added them to the model, which revealed that TiCl₄ decomposed to TiCl₃ via TiCl₃NH₂. Under our conditions, TiCl₄ and TiCl₃NH₂ are the major Ti species and AlCl₃ and AlCl₂NH₂ are the major Al species, which suggests that some of these species may form films. Unlike in the earlier reaction model, NH₂ and H radicals were produced, which may have contributed to the surface reactions. For reactors with large Surface/Volume ratio of reactor, the effects of gas-phase reactions should be considered. Our reaction model enables estimation of the partial pressures of reactor gas species and will therefore aid study of the TiAlN deposition mechanism.