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Journal articles on the topic 'Chemical kinetics'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 March 2025

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1

Yablonsky, Gregory, Daniel Branco, Guy Marin, and Denis Constales. "New Invariant Expressions in Chemical Kinetics." Entropy 22, no. 3 (March 24, 2020): 373. http://dx.doi.org/10.3390/e22030373.

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This paper presents a review of our original results obtained during the last decade. These results have been found theoretically for classical mass-action-law models of chemical kinetics and justified experimentally. In contrast with the traditional invariances, they relate to a special battery of kinetic experiments, not a single experiment. Two types of invariances are distinguished and described in detail: thermodynamic invariants, i.e., special combinations of kinetic dependences that yield the equilibrium constants, or simple functions of the equilibrium constants; and “mixed” kinetico-thermodynamic invariances, functions both of equilibrium constants and non-thermodynamic ratios of kinetic coefficients.

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2

Moses, Julianne I. "Chemical kinetics on extrasolar planets." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2014 (April 28, 2014): 20130073. http://dx.doi.org/10.1098/rsta.2013.0073.

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Chemical kinetics plays an important role in controlling the atmospheric composition of all planetary atmospheres, including those of extrasolar planets. For the hottest exoplanets, the composition can closely follow thermochemical-equilibrium predictions, at least in the visible and infrared photosphere at dayside (eclipse) conditions. However, for atmospheric temperatures , and in the uppermost atmosphere at any temperature, chemical kinetics matters. The two key mechanisms by which kinetic processes drive an exoplanet atmosphere out of equilibrium are photochemistry and transport-induced quenching. I review these disequilibrium processes in detail, discuss observational consequences and examine some of the current evidence for kinetic processes on extrasolar planets.

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3

Zhong, Wei, and Zhou Tian. "Application of Genetic Algorithm in Chemical Reaction Kinetics." Applied Mechanics and Materials 79 (July 2011): 71–76. http://dx.doi.org/10.4028/www.scientific.net/amm.79.71.

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In this paper, a summary of Genetic Algorithm methods developed recent years applied in chemical reaction kinetics was presented. The applications of the Genetic Algorithm in reduction of the chemical reaction kinetics, estimation of the chemical kinetic parameters and calculation of the chemical kinetic equations were expounded here. Eventually, the confronted problem and developing trend of the application of Genetic Algorithm methods in chemical kinetics were reviewed.

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4

Fedoseev, V. B., and Е. N. Fedoseeva. "Kinetics of chemical reactions in spray." Kinetika i kataliz 65, no. 2 (September 28, 2024): 107–15. http://dx.doi.org/10.31857/s0453881124020016.

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The number of observations demonstrating a significant effect of droplet sizes on the kinetics of chemical processes has increased with the expansion of the scope of application of spray technology. The equations linking the concentrations of reagents, the volume of droplets, the initial composition of the solution, the composition of the gas medium and the speed of processes are formulated within the framework of formal chemical kinetics. Using the example of second-order reactions (coupling, exchange, condensation, polymerization, polycondensation), it is shown that size kinetic effects occur when chemical processes are accompanied by changes in the droplet sizes in equilibrium with the gas medium. The results of computer simulation of condensation reaction and polycondensation process reproducing size effects are presented. Kinetic curves obtained by modeling the polycondensation process are compared with experimental data.

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5

Borgert, C. J., C. Fuentes, and L. D. Burgoon. "Principles of dose-setting in toxicology studies: the importance of kinetics for ensuring human safety." Archives of Toxicology 95, no. 12 (October 8, 2021): 3651–64. http://dx.doi.org/10.1007/s00204-021-03155-4.

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AbstractRegulatory toxicology seeks to ensure that exposures to chemicals encountered in the environment, in the workplace, or in products pose no significant hazards and produce no harm to humans or other organisms, i.e., that chemicals are used safely. The most practical and direct means of ensuring that hazards and harms are avoided is to identify the doses and conditions under which chemical toxicity does not occur so that chemical concentrations and exposures can be appropriately limited. Modern advancements in pharmacology and toxicology have revealed that the rates and mechanisms by which organisms absorb, distribute, metabolize and eliminate chemicals—i.e., the field of kinetics—often determine the doses and conditions under which hazard, and harm, are absent, i.e., the safe dose range. Since kinetics, like chemical hazard and toxicity, are extensive properties that depend on the amount of the chemical encountered, it is possible to identify the maximum dose under which organisms can efficiently metabolize and eliminate the chemicals to which they are exposed, a dose that has been referred to as the kinetic maximum dose, or KMD. This review explains the rationale that compels regulatory toxicology to embrace the advancements made possible by kinetics, why understanding the kinetic relationship between the blood level produced and the administered dose of a chemical is essential for identifying the safe dose range, and why dose-setting in regulatory toxicology studies should be informed by estimates of the KMD rather than rely on the flawed concept of maximum-tolerated toxic dose, or MTD.

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6

Udgaonkar, Jayant B., and George P. Hess. "Acetylcholine receptor kinetics: Chemical kinetics." Journal of Membrane Biology 93, no. 2 (June 1986): 93–109. http://dx.doi.org/10.1007/bf01870803.

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7

Cruz Camacho, Elkin Alejandro, Juan Andrés Montoya Arguello, and Jesús Alberto Ágreda Bastidas. "CHEMical KINetics SimuLATOR (Chemkinlator): A friendly user interface for chemical kinetics simulations." Revista Colombiana de Química 49, no. 1 (January 1, 2020): 40–47. http://dx.doi.org/10.15446/rev.colomb.quim.v1n49.83298.

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CHEMical KINetics SimuLATOR is a Graphical User Interface for the simulation of reaction mechanisms. The interface allows the user to see and change the parameters of a reaction network within a single window. Chemkinlator comes with built-in support for three types of kinetic simulations: Time Series, which computes the concentration of all species in an interval of time in a defined model; Bifurcation diagrams, which are the result of running several Time Series simulations over gradually different kinetic rate constants; and Flow/Temperature time series, which takes into account the effect of flow in the Continuous-flow well-Stirred Tank Reactor, and the effect of temperature on the rates constants according to the Arrhenius equation. In our research group, Chemkinlator has been the primary tool used to test the predictions made by algorithms that analyze homochirality phenomena. Chemkinlator is written in C++14 and Qt, and it uses the Fortran subroutine DLSODE to solve the differential equations associated with the reaction networks. Chemkinlator is open source software under the Apache 2.0 license and can be downloaded freely from https://gitlab.com/homochirality/chemkinlator.

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8

Tran, Hai Nguyen. "Differences between Chemical Reaction Kinetics and Adsorption Kinetics: Fundamentals and Discussion." Journal of Technical Education Science, no. 70B (June 28, 2022): 33–47. http://dx.doi.org/10.54644/jte.70b.2022.1154.

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Adsorption kinetics is an essential part in adsorption studies. The pseudo-first-order (PFO) and pseudo-second-order (PSO) models are frequently used to model the experimental dataset of time-dependent adsorption. The differential equations (based on reaction rate and rate law) of the PFO and PSO models are similar to those of chemical reactions (i.e., first and second order-kinetic reactions). The adsorption kinetics is illustrated through the plot of qt (the amount of adsorbate adsorbed by adsorbent at time t) vs. time. This plot includes two important regions (kinetic and equilibrium). The adsorption rate constant (k1(PFO) or k2(PSO), respectively) of the PFO or PSO models needs to be calculated from two regions. The appropriate selection of initial adsorbate concentrations for studying adsorption kinetics should be based on adsorption isotherm to ensure that adsorption sites in adsorbent (material) are highly (nearly fully) covered by adsorbate (solute). Only in this case, the rate constant of the adsorption is accurately obtained.

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9

Bosch, Hans. "Comprehensive chemical kinetics, vol. 23, kinetics and chemical technology." Applied Catalysis 20, no. 1-2 (January 1986): 326–27. http://dx.doi.org/10.1016/0166-9834(86)80038-0.

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10

Derouane, EricG. "Comprehensive chemical kinetics, vol. 23 kinetics and chemical technology." Journal of Molecular Catalysis 39, no. 3 (March 1987): 389–90. http://dx.doi.org/10.1016/0304-5102(87)80086-x.

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11

Schmalzried, Hermann. "Chemical kinetics at solid-solid interfaces." Pure and Applied Chemistry 72, no. 11 (January 1, 2000): 2137–47. http://dx.doi.org/10.1351/pac200072112137.

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The kinetics of solid-solid interfaces controls in part the course of heterogeneous reactions in the solid state, in particular in miniaturized systems. In this paper, the essential situations of interface kinetics in solids are defined, and the basic formal considerations are summarized. In addition to the role interfaces play as resistances for transport across them, they offer high diffusivity paths laterally and thus represent two-dimensional reaction media. Experimental examples will illustrate the kinetic phenomena at static and moving boundaries, including problems such as exchange fluxes, boundary-controlled solid-state reactions, interface morphology, nonlinear phenomena connected with interfaces, and reactions in and at boundaries, among others.

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12

Constales, Denis, Gregory Yablonsky, Yiming Xi, and Guy Marin. "Egalitarian Kinetic Models: Concepts and Results." Energies 14, no. 21 (November 2, 2021): 7230. http://dx.doi.org/10.3390/en14217230.

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In this paper, two main ideas of chemical kinetics are distinguished, i.e., a hierarchy and commensuration. A new class of chemical kinetic models is proposed and defined, i.e., egalitarian kinetic models (EKM). Contrary to hierarchical kinetic models (HKM), for the models of the EKM class, all kinetic coefficients are equal. Analysis of EKM models for some complex chemical reactions is performed for sequences of irreversible reactions. Analytic expressions for acyclic and cyclic mechanisms of egalitarian kinetics are obtained. Perspectives on the application of egalitarian models for reversible reactions are discussed. All analytical results are illustrated by examples.

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13

Burgess, Donald R., and Jeffrey A. Manion. "70 Years of Evaluated Chemical Kinetics Data in the Journal of Physical and Chemical Reference Data, the National Standard Reference Data System Series, and the NBS Kinetics Data Center." Journal of Physical and Chemical Reference Data 51, no. 2 (June 1, 2022): 021501. http://dx.doi.org/10.1063/5.0091497.

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We provide an overview of 70 years of evaluated chemical kinetic data published in the Journal of Physical and Chemical Reference Data (dating to 1972), the National Standard Reference Data System series (dating to 1965), as part of the National Bureau of Standards Chemical Kinetics Data Center (dating to 1951), and the National Institute of Standards and Technology Chemical Kinetics Database (SRD 17) (dating to 1990).

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14

Burgess, Donald R., and Jeffrey A. Manion. "70 Years of Evaluated Chemical Kinetics Data in the Journal of Physical and Chemical Reference Data, the National Standard Reference Data System Series, and the NBS Kinetics Data Center." Journal of Physical and Chemical Reference Data 51, no. 2 (June 1, 2022): 021501. http://dx.doi.org/10.1063/5.0091497.

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We provide an overview of 70 years of evaluated chemical kinetic data published in the Journal of Physical and Chemical Reference Data (dating to 1972), the National Standard Reference Data System series (dating to 1965), as part of the National Bureau of Standards Chemical Kinetics Data Center (dating to 1951), and the National Institute of Standards and Technology Chemical Kinetics Database (SRD 17) (dating to 1990).

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15

Andersen, Ir Bart. "Aquatic chemical kinetics." Colloids and Surfaces 65, no. 4 (August 1992): 303–4. http://dx.doi.org/10.1016/0166-6622(92)80185-5.

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16

Brown, M. E. "Fundamental Chemical Kinetics." Thermochimica Acta 362, no. 1-2 (November 2000): 185. http://dx.doi.org/10.1016/s0040-6031(00)00583-9.

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17

Lecca, Paola. "Stochastic chemical kinetics." Biophysical Reviews 5, no. 4 (July 30, 2013): 323–45. http://dx.doi.org/10.1007/s12551-013-0122-2.

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18

Field, Richard. "Chemical reaction kinetics." Scholarpedia 3, no. 10 (2008): 4051. http://dx.doi.org/10.4249/scholarpedia.4051.

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19

Peter, L. M. "Comprehensive chemical kinetics." Journal of Electroanalytical Chemistry and Interfacial Electrochemistry 251, no. 2 (September 1988): 429–30. http://dx.doi.org/10.1016/0022-0728(88)85204-5.

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20

Derouane, EricG. "Comprehensive chemical kinetics." Journal of Molecular Catalysis 40, no. 1 (April 1987): 125. http://dx.doi.org/10.1016/0304-5102(87)80013-5.

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21

Drever, James I. "Aquatic Chemical Kinetics." Geochimica et Cosmochimica Acta 55, no. 5 (May 1991): 1489. http://dx.doi.org/10.1016/0016-7037(91)90324-x.

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22

Midgley, D. "Aquatic chemical kinetics:." Talanta 38, no. 6 (June 1991): 687. http://dx.doi.org/10.1016/0039-9140(91)80158-v.

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23

garfinkle, Moishe. "The thermodynamic natural path in chemical reaction kinetics." Discrete Dynamics in Nature and Society 4, no. 2 (2000): 145–64. http://dx.doi.org/10.1155/s1026022600000145.

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The Natural Path approach to chemical reaction kinetics was developed to bridge the considerable gap between the Mass Action mechanistic approach and the non-mechanistic irreversible thermodynamic approach. The Natural Path approach can correlate empirical kinetic data with a high degree precision, as least equal to that achievable by the Mass-Action rate equations, but without recourse mechanistic considerations. The reaction velocities arising from the particular rate equation chosen by kineticists to best represent the kinetic behavior of a chemical reaction are the natural outcome of the Natural Path approach. Moreover, by virtue of its thermodynamic roots, equilibrium thermodynamic functions can be extracted from reaction kinetic data with considerable accuracy. These results support the intrinsic validity of the Natural Path approach.

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24

Bratos, S., M. Wulff, J. Cl Leicknam, and Q. Kong. "Ultrafast chemical kinetics: Elementary chemical act." Chemical Physics Letters 619 (January 2015): 88–91. http://dx.doi.org/10.1016/j.cplett.2014.11.055.

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25

Yablonsky, Gregory S., Denis Constales, and Guy B. Marin. "Joint kinetics: a new paradigm for chemical kinetics and chemical engineering." Current Opinion in Chemical Engineering 29 (September 2020): 83–88. http://dx.doi.org/10.1016/j.coche.2020.06.007.

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26

Lomova, Tatyana N., Mariya E. Klyueva, Elena Yu Tyulyaeva, and Nataliya G. Bichan. "Use of chemical kinetics for the description of metal porphyrin reactivity." Journal of Porphyrins and Phthalocyanines 16, no. 09 (September 2012): 1040–54. http://dx.doi.org/10.1142/s1088424612500769.

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The results of use of chemical kinetics receptions, approaches and methods for the study of porphyrins and their metal complexes reactivity are discussed on an example of oxidation, acid-basic, and catalytic reactions of rhodium, palladium, and rhenium complexes of porphyrin in liquid solutions. The peculiarity of the porphyrin reaction rates is analyzed in a brief context of general provisions of the chemical kinetics. The opportunity to use the quasistationarity principle at the definition of the kinetic equation of the reactions with participation of metal porphyrins is shown. The transition from the process kinetic description to consideration of its mechanism is explored.

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27

Otsuka, Keigo, Taiki Inoue, Rong Xiang, Shohei Chiashi, Yuichiro K. Kato, and Shigeo Maruyama. "(Invited) Kinetic Selectivity of Chemical Vapor Deposition Growth of Carbon Nanotubes." ECS Meeting Abstracts MA2022-01, no. 10 (July 7, 2022): 767. http://dx.doi.org/10.1149/ma2022-0110767mtgabs.

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Single-walled carbon nanotubes have been a candidate for outperforming silicon in ultrascaled transistors, but the realization of nanotube-based integrated circuits requires dense arrays of purely semiconducting species. In order to directly growth such nanotube arrays on wafers, control over kinetics and thermodynamics in tube-catalyst systems plays a key role, and further progress requires the comprehensive understanding of seemingly contradictory reports on the growth kinetics. Here, we propose a universal kinetic model that decomposes the growth rates of nanotubes into the adsorption and removal of carbon atoms on the catalysts, and provide its quantitative verification by ethanol-based isotope labeling experiments. While the removal of carbon from catalysts dominates the growth kinetics under a low supply of precursors, our kinetic model and experiments demonstrate that chiral angle-dependent growth rates emerge when sufficient amounts of carbon and etching agents are co-supplied. The kinetic maps, as a product of generalizing the model, include several kinetic selectivities that emerge depending on the balance of gases with opposing effects. Our findings not only resolve discrepancies existing in literature, but also offer rational strategies to control chirality, length, and density of nanotube arrays for practical applications. Part of this work was supported by JSPS (KAKENHI JP20K15137, JP20H00220) and JST (CREST JPMJCR20B5).

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28

Nandiyanto, Asep Bayu Dani, Risti Ragadhita, and Meli Fiandini. "How to Calculate and Determine Chemical Kinetics: Step-by-Step Interpretation of Experimental Data to Get Reaction Rate and Order." Indonesian Journal of Science and Technology 9, no. 3 (July 9, 2024): 759–74. https://doi.org/10.17509/ijost.v9i3.74749.

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Chemical kinetics is a fundamental field of study that focuses on the rates of chemical reactions and the factors influencing them. Determining the rate and order of the chemical reaction is an important aspect of understanding the reaction mechanism. This article presents a step-by-step guide to calculate and determine the rate and order of the reaction through a comprehensive analysis of experimental data. We discussed the concept of chemical kinetics, the integration of the kinetic order method, and the curve-fitting analysis technique. Several case studies are also included to provide practical applications of the theory outlined. This proposed guide aims to provide students, educators, researchers, and practitioners with a systematic approach to specify and verify prospective kinetic models, enabling them in decision-making, improving, and optimizing chemical reaction processes.

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29

Kutlugil’dina, Galiya G. "Kinetic scheme of apple pectin oxidative transformations under the action of the ozone-oxygen mixture." Butlerov Communications 61, no. 2 (February 29, 2020): 79–89. http://dx.doi.org/10.37952/roi-jbc-01/20-61-2-79.

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Mathematical modeling of apple pectin oxidative transformations (AP) under the action of the ozone-oxygen mixture in aqueous solutions (the reaction system "AP + O3 + O2 + H2O") has been carried out. The kinetic scheme of the oxidation process was compiled basing on the well-known ideas of liquid-phase oxidation mechanisms of organic compounds (taking into account the currently known experimental results on AP oxidation). Using the "KhimKinOptima" software package for the proposed scheme, the inverse and direct chemical kinetics problems were solved. The well-known literature data on the rate constants of elementary stages were used. The rate constants of the oxidation key stages have been determined after solving the chemical kinetics inverse problem with the index method of the observed and calculated kinetic data global optimization. It turned out that the rate constants of the individual stages obtained by calculation are in good agreement with the values of the rate constants taken from literary sources. The chemical kinetics direct problem has been solved with the found rate constants and allowed obtaining kinetic curves of all participants in the apple pectin ozonized oxidation. It was found that the kinetic curve of the accumulation of carboxyl groups, obtained experimentally, completely coincided with the theoretical dependence. It has been also shown that the proposed apple pectin oxidative conversion scheme in the "AP + O3 + O2 + H2O" reaction system allows one to explain all the currently available experimental results. The apple pectin ozonized oxidation under another initiator (Н2О2 + FeSO4) has been studied to confirm the kinetic scheme. To do this, 3 new stages has been introduced into the scheme proposed, characterizing the catalytic decomposition of hydrogen peroxide under a transition metal (Fe2+). By solving the chemical kinetics direct problem, the accumulation kinetic curves of the final reaction products were obtained. It has been found that the carboxyl groups accumulation kinetics in the reaction system "AP + O3 + O2 + H2O2 + FeSO4 + H2O" after the supplementary experiment coincided with the theoretical kinetic curve. Thereby, the accuracy of the apple pectin proposed oxidative conversion scheme is confirmed.

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30

Trninić, Marta. "Mathematical modelling of primary and secondary pyrolysis: State of the art." FME Transactions 48, no. 4 (2020): 733–44. http://dx.doi.org/10.5937/fme2004733t.

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Pyrolysis process converts biomass into liquid, gaseous and solid fuels. Chemical kinetics play a key role in explaining the characteristics of pyrolysis reactions and developing mathematical models. Many studies have been undertaken to understand the kinetics of biomass pyrolysis; however, due to the heterogeneity of biomass and the complexity of the chemical and physical changes that occur during pyrolysis, it is difficult to develop a simple kinetic model that is applicable in every case. In this review, different methods to describe biomass primary and secondary pyrolysis with different types of kinetic mechanisms are discussed.

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31

Kuwahara, Kazunari, Yoshihiro Hiramura, Shintaro Ohmura, Masahiro Furutani, Yasuyuki Sakai, and Hiromitsu Ando. "OS3-3 Chemical Kinetics Study on Effect of Pressure on Hydrocarbon Ignition Process(OS3 Application of chemical kinetics to combustion modeling,Organized Session Papers)." Proceedings of the International symposium on diagnostics and modeling of combustion in internal combustion engines 2012.8 (2012): 128–33. http://dx.doi.org/10.1299/jmsesdm.2012.8.128.

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32

Alexander, M. H. "Chemical Kinetics Under Test." Science 331, no. 6016 (January 27, 2011): 411–12. http://dx.doi.org/10.1126/science.1201509.

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33

PRIGOGINE, ILYA. "Chemical Kinetics and Dynamics." Annals of the New York Academy of Sciences 988, no. 1 (May 2003): 128–32. http://dx.doi.org/10.1111/j.1749-6632.2003.tb06091.x.

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34

Novak, Igor. "Chemical Kinetics without Calculus." Journal of Chemical Education 75, no. 12 (December 1998): 1574. http://dx.doi.org/10.1021/ed075p1574.

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35

Myers, R. Thomas. "Ants and chemical kinetics." Journal of Chemical Education 67, no. 9 (September 1990): 761. http://dx.doi.org/10.1021/ed067p761.

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36

Lyman, John L., and Redus Holland. "Oxygen fluoride chemical kinetics." Journal of Physical Chemistry 92, no. 26 (December 1988): 7232–41. http://dx.doi.org/10.1021/j100337a015.

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37

Denuault, G. "Research in chemical kinetics." Journal of Electroanalytical Chemistry 385, no. 2 (April 1995): 284–85. http://dx.doi.org/10.1016/0022-0728(95)90219-8.

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38

Lengyel, S. "Chemical kinetics and thermodynamics." Computers & Mathematics with Applications 17, no. 1-3 (1989): 443–55. http://dx.doi.org/10.1016/0898-1221(89)90173-9.

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39

Schmalzried, H. "Chemical Kinetics of Solids." Materials Science Forum 239-241 (January 1997): 381–86. http://dx.doi.org/10.4028/www.scientific.net/msf.239-241.381.

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40

Schirmer, W. "Research in Chemical Kinetics." Zeitschrift für Physikalische Chemie 190, Part_2 (January 1995): 310–11. http://dx.doi.org/10.1524/zpch.1995.190.part_2.310.

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41

Meyer, K. "Chemical Kinetics of Solids." Zeitschrift für Physikalische Chemie 193, Part_1_2 (January 1996): 213. http://dx.doi.org/10.1524/zpch.1996.193.part_1_2.213.

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42

Derouane, E. G. "Kinetics of chemical processes." Journal of Molecular Catalysis 69, no. 2 (October 1991): 281. http://dx.doi.org/10.1016/0304-5102(91)80152-s.

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43

Wigent, Rodney J. "ChemInform Abstract: Chemical Kinetics." ChemInform 44, no. 41 (September 19, 2013): no. http://dx.doi.org/10.1002/chin.201341276.

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44

Edeleva, Mariya, Paul H. M. Van Steenberge, Maarten K. Sabbe, and Dagmar R. D’hooge. "Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art." Polymers 13, no. 18 (September 7, 2021): 3027. http://dx.doi.org/10.3390/polym13183027.

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In recent decades, quantum chemical calculations (QCC) have increased in accuracy, not only providing the ranking of chemical reactivities and energy barriers (e.g., for optimal selectivities) but also delivering more reliable equilibrium and (intrinsic/chemical) rate coefficients. This increased reliability of kinetic parameters is relevant to support the predictive character of kinetic modeling studies that are addressing actual concentration changes during chemical processes, taking into account competitive reactions and mixing heterogeneities. In the present contribution, guidelines are formulated on how to bridge the fields of computational chemistry and chemical kinetics. It is explained how condensed phase systems can be described based on conventional gas phase computational chemistry calculations. Case studies are included on polymerization kinetics, considering free and controlled radical polymerization, ionic polymerization, and polymer degradation. It is also illustrated how QCC can be directly linked to material properties.

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45

Mihajlovic, Ivan, Nada Strbac, Zivan Zivkovic, and Ilija Ilic. "Kinetics and mechanism of As2S2 oxidation." Journal of the Serbian Chemical Society 70, no. 6 (2005): 869–77. http://dx.doi.org/10.2298/jsc0506869m.

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The kinetics of realgar (As2S2) oxidation was studied under isothermal and non-isothermal conditions. The obtained values of the activation energy indicate that the process occurs in the kinetic domainwith the realgar particles being converted to As2O3 and As4O6 (g). The very fast reaction rates were limited by the chemical reaction. The kinetic equation was found to be: ?ln (1??) = 4.56 x 103 x e(?8780/T) x t. The proposed reaction mechanism and chemical transformation investigated by ICP?AES, EDXRF and thermal analysis are discussed.

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46

Henderson, R. A. "Kinetics of Homogeneous Multistep Reactions: Comprehensive Chemical Kinetics." Journal of Organometallic Chemistry 645, no. 1-2 (February 2002): 290–91. http://dx.doi.org/10.1016/s0022-328x(01)01337-7.

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47

Ruiz-Gutiérrez, Gema, Araceli Rodríguez-Romero, Antonio Tovar-Sánchez, and Javier R. Viguri Fuente. "Analysis and Modeling of Sunscreen Ingredients’ Behavior in an Aquatic Environment." Oceans 3, no. 3 (August 2, 2022): 340–63. http://dx.doi.org/10.3390/oceans3030024.

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Sunscreens have become a product based on increasingly complex formulations that include, among many ingredients, a mixture of UV filters to provide optimal sun ultraviolet radiation protection. A significant group of scientific works deals with the impact of UV filters in aquatic media. However, the knowledge of the mechanism and kinetics of the compound’s direct release, fate, and its transformation and interaction with living organisms is necessary to assess its environmental occurrence and behavior and to predict potential and real impacts on the aquatic environment. This review outlines the existing analysis and modeling of the release and behavior of sunscreen’s ingredients in the marine environment, including aquatic organisms. The physical-chemical properties, photodegradation, and release kinetics of particles and chemicals into the water are studied by hydrodynamic and kinetic models. Direct photolysis of chemicals is modeled as pseudo-first-order kinetics, while the indirect pathway by the reaction of sunscreen with reactive oxygen species is described as second-order kinetics. The interaction of UV filters with marine biota is studied mainly by toxicokinetic models, which predict their bio-accumulation in the organisms’ tissues. These models consider the chemicals’ uptake and excretion, as well as their transfer between different internal animal organs, as a first-order kinetic process. The studies analyzed in the present work represent a driver of change for the beauty and personal care industry, in order to seek new ecological alternatives through the application of R&D tactics.

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Kol'tsov, Nikolay I. "SOLUTION OF THE INVERSE PROBLEM OF CHEMICAL KINETICS BASED ON FUZZY LOGIC MODELS." ChemChemTech 67, no. 12 (November 12, 2024): 54–63. https://doi.org/10.6060/ivkkt.20246712.7067.

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The possibility of using “soft” calculation methods to solve the inverse problem of chemical kinetics to identify fuzzy kinetic laws based on the data of stationary experiments without a priori specifying the type of the kinetic law has been investigated. “Soft” computing combines artificial intelligence methods such as fuzzy logic, neural networks, genetic algorithms, etc. (F. Rosenblatt, L. Zadeh, J. Holland, etc.). Combinations of these methods will allow the creation of various hybrid systems. Dynamic kinetic models of chemical reactions are described by systems of ordinary differential equations (clear deterministic models), on the basis of which, using various optimization methods (least squares, etc.), the inverse problem of chemical kinetics is solved. B. Kosko proved that any mathematical system can be approximated as accurately as desired by a non-deterministic or weakly deterministic model based on fuzzy logic using simple algorithmic constructions of the “if ... then” type (conditional operators) without direct quantitative or qualitative analysis of systems of differential equations. A fuzzy kinetic model is an algorithmic design based on fuzzy algebra, which allows one to calculate the reaction rate for any values of reagent concentrations of interest to the experimenter. This model allows solving the inverse problem without a priori specifying the type of kinetic law, does not have an unambiguous formulaic representation and can be described by regression (clear) equations. In this article, based on fuzzy logic methods, regression kinetic models are obtained and studied to solve the inverse problem for a simple model reaction and the acetylene hydrochlorination reaction. It has been shown that clear regression models demonstrate good accuracy and can be used to establish alternative kinetic laws and mechanisms for chemical reactions. For citation: Kol'tsov N.I. Solution of the inverse problem of chemical kinetics based on fuzzy logic models. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 12. P. 54-63. DOI: 10.6060/ivkkt.20246712.7067.

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Zhang, Xiaodong, Min Xu, Rongfeng Sun, and Li Sun. "Study on Biomass Pyrolysis Kinetics." Journal of Engineering for Gas Turbines and Power 128, no. 3 (March 1, 2004): 493–96. http://dx.doi.org/10.1115/1.2135816.

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Pyrolysis is the most fundamental process in thermal chemical conversion of biomass, and pyrolysis kinetic analysis is valuable for the in-depth exploration of process mechanisms. On the basis of thermal gravity analysis of different kinds of biomass feedstock, thermal kinetics analysis was performed to analyze the pyrolysis behavior of biomass. With the apparent kinetic parameters derived, a kinetic model was proposed for the main reaction section of biomass pyrolysis process. The pyrolysis characteristics of three kinds of biomass material were compared in view of corresponding biochemical constitution. Through model simulation of different pyrolysis processes, the diversity in pyrolysis behavior of different kinds of biomass feedstock was analyzed and pyrolysis mechanism discussed. The results derived are useful for the development and optimization of biomass thermal chemical conversion technology.

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Otero, Toribio F., and Jose G. Martinez. "Structural and Biomimetic Chemical Kinetics: Kinetic Magnitudes Include Structural Information." Advanced Functional Materials 23, no. 4 (September 10, 2012): 404–16. http://dx.doi.org/10.1002/adfm.201200719.

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