Relevant bibliographies by topics / Chemical reaction

Academic literature on the topic 'Chemical reaction'

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Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Chemical reaction"

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DE LACY COSTELLO, B. P. J., I. JAHAN, A. ADAMATZKY, and N. M. RATCLIFFE. "CHEMICAL TESSELLATIONS." International Journal of Bifurcation and Chaos 19, no. 02 (February 2009): 619–22. http://dx.doi.org/10.1142/s0218127409023238.

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We report a simple set of chemical reactions based on the reaction of a range of metal salts with potassium ferricyanide loaded gels that spontaneously produce complex and colorful tessellations of the plane. These reactions provide a great resource for scientific demonstrations, whilst also constituting an important class of nonlinear pattern forming reaction.
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Kikuchi, Shin, Hiroyuki Ohshima, and Kenro Hashimoto. "ICONE19-43782 Reaction Path Analysis of Sodium-Water Reaction Phenomena in support of Chemical Reaction Model Development." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_304.

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Blurock, Edward S. "Reaction: System for Modeling Chemical Reactions." Journal of Chemical Information and Modeling 35, no. 3 (May 1, 1995): 607–16. http://dx.doi.org/10.1021/ci00025a032.

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Marris, Emma. "Chemical reaction." Nature 437, no. 7060 (October 2005): 807–9. http://dx.doi.org/10.1038/437807a.

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Challen, John. "Chemical Reaction." Electric and Hybrid Vehicle Technology International 2021, no. 3 (November 2021): 46–50. http://dx.doi.org/10.12968/s1467-5560(22)60257-4.

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Sieniutycz, Stanisław. "A Fermat-like Principle for Chemical Reactions in Heterogeneous Systems." Open Systems & Information Dynamics 09, no. 03 (September 2002): 257–72. http://dx.doi.org/10.1023/a:1019708629128.

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We formulate a variational principle of Fermat type for chemical kinetics in heterogeneous reacting systems. The principle is consistent with the notion of ‘intrinsic reaction coordinate’ (IRC), the idea of ‘chemical resistance’ (CR) and the second law of thermodynamics. The Lagrangian formalism applies a nonlinear functional of entropy production that follows from classical (single-phase) nonequilibrium thermodynamics of chemically reacting systems or its extension for multiphase systems involving interface reactions and transports. For a chemical flux, a “law of bending” is found which implies that — by minimizing the total resistance — the chemical ray spanned between two given points takes the shape assuring its relatively large part in a region of lower chemical resistivity (a ‘rarer’ region of the medium). In effect, the chemical flux bends into the direction that ensures its shape consistent with the longest residence of the chemical complex in regions of lower resistivity. The dynamic programming method quantifies the “chemical rays” and related wavefronts along the reaction coordinate.
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Schwaller, Philippe, Benjamin Hoover, Jean-Louis Reymond, Hendrik Strobelt, and Teodoro Laino. "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions." Science Advances 7, no. 15 (April 2021): eabe4166. http://dx.doi.org/10.1126/sciadv.abe4166.

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Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.
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Dunning, Thom H., Elfi Kraka, and Robert A. Eades. "Insights into the mechanisms of chemical reactions. Reaction paths for chemical reactions." Faraday Discussions of the Chemical Society 84 (1987): 427. http://dx.doi.org/10.1039/dc9878400427.

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Wu, Jun-Lin, Zhi-Hui Li, Ao-Ping Peng, Xing-Cai Pi, and Xin-Yu Jiang. "Utility computable modeling of a Boltzmann model equation for bimolecular chemical reactions and numerical application." Physics of Fluids 34, no. 4 (April 2022): 046111. http://dx.doi.org/10.1063/5.0088440.

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A Boltzmann model equation (kinetic model) involving the chemical reaction of a multicomponent gaseous mixture is derived based on Groppi's work [“A Bhatnagar–Gross–Krook-type approach for chemically reacting gas mixtures,” Phys. Fluids 16, 4273 (2004)], in which the relaxation parameters of elastic collision frequency for rigid elastic spheres are obtained based on the collision term, and the pivotal collision frequency of the chemical reaction is deduced from the chemical reaction rate that is determined by the direct simulation Monte Carlo (DSMC) method. This kinetic model is shown to be conservative, and the H theorem for an endothermic reaction is proven. In the framework of the gas-kinetic unified algorithm, the discrete velocity method, finite volume method, and implicit scheme are applied to solve the proposed kinetic model by introducing a suitable boundary condition at the wall surface. For hypersonic flows around a cylinder, the proposed kinetic model and the corresponding numerical methods are verified for both endothermic and exothermic reactions by comparison of the model's results with results from the DSMC method. The different influences of endothermic and exothermic reactions are also given. Finally, the proposed kinetic model is also used to simulate an exothermic reaction-driven flow in a square cavity.
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Lazaridis, Filippos, Aditya Savara, and Panos Argyrakis. "Reaction efficiency effects on binary chemical reactions." Journal of Chemical Physics 141, no. 10 (September 14, 2014): 104103. http://dx.doi.org/10.1063/1.4894791.

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Dissertations / Theses on the topic "Chemical reaction"

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Steele, Aaron J. "Collective behavior in chemical systems." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5386.

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Thesis (Ph. D.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains vii, 126 p. : ill. (some col.) + video files. Includes supplementary video files in a zip file. Includes abstract. Includes bibliographical references.
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Degrand, Elisabeth. "Evolving Chemical Reaction Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257491.

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One goal of synthetic biology is to implement useful functions with biochemical reactions, either by reprogramming living cells or programming artificial vesicles. In this perspective, we consider Chemical Reaction Networks (CRNs) as a programming language. Recent work has shown that continuous CRNs with their dynamics described by ordinary differential equations are Turing complete. That means that any function over the reals that is computable by a Turing machine in arbitrary precision, can be computed by a CRN over a finite set of molecular species. The proof uses an algorithm which, given a computable function presented as the solution of a PIVP (PolynomialInitial Values Problem), generates a finite CRN to implement it. In the generated CRNs, the molecular concentrations play the role of information carriers, similarly to proteins in cells. In this Master’s Thesis, we investigate an approach based on an evolutionary algorithm to build a continuous CRN that approximates a real function given a finite set of the values of the function. The idea is to use a two-level parallel genetic algorithm. A first algorithm is used to evolve the structure of the network, while the other one enables us to optimize the parameters of the CRNs at each step. We compare the CRNs generated by our method on different functions. The CRNs found by evolution often give good results with quite unexpected solutions.
Ett mål med syntetisk biologi är att genomföra användbara funktioner med biokemiska reaktioner, antingen genom omprogrammering av levande celler eller programmering av artificiella vesiklar. I detta perspektiv anser vi Chemical Reaction Networks (CRNs) som ett programmeringsspråk. Det senaste arbetet har visat att kontinuerliga CRNs med dynamik som beskrivs av vanliga differentialekvationer är Turingkompletta. Det betyder att en funktion över de realla talen som kan beräknas av en Turing-maskin i godtycklig precision, kan beräknas av en CRN över en ändlig uppsättning molekylära arter. Beviset använder en algoritm som, givet en beräkningsbar funktion som presenteras som lösningen av ett PIVP (Polynomial Initial Values Problem), genererar en ändlig CRN för att implementera den. I de genererade CRN:erna spelar molekylkoncentrationerna rollen som informationsbärare, på samma sätt som proteiner i celler. I detta examensarbete undersöker vi ett tillvägagångssätt baserat på en evolutionär algoritm för att bygga en kontinuerlig CRN som approximerar en verklig funktion med en ändlig uppsättning av värden för funktionen. Tanken är att använda parallell genetisk algoritm i två nivåer. En första algoritm används för att utveckla nätets struktur, medan den andra möjliggör att optimera parametrarna för CRN:erna vid varje steg. Vi jämför de CRN som genereras av vår metod på olika funktioner. De CRN som hittas av evolutionen ger ofta bra resultat med ganska oväntade lösningar.
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Knight, Daniel William. "Reactor behavior and its relation to chemical reaction network structure." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1438274630.

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Du, Yimian. "Bifurcation analysis in chemical reaction network." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511282.

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Hayes, Michael Y. "Theoretical studies of chemical reaction dynamics." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273678.

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Ritchie, Grant A. D. "Laser studies of chemical reaction dynamics." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325785.

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English, Philip J. "Automated discovery of chemical reaction networks." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500929.

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The identification of models of chemical reaction networks is of importance in the safe, economic and environmentally sensitive development of chemical products. Qualitative models of a network of interactions are used in the design of drugs and other therapies. Quantitative models of the behaviour of reaction networks are the foundation of the science of reaction engineering (e.g. see Levenspiel, 1999); allowing the use of simulation software in the rapid development of commercial scale production processes. This work extends the existing methods reported by Burnham et al. (2006); adopting the global basis fonction method first applied to this problem by Crampin et al. (2004a).
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Domijan, Mirela. "Mathematical aspects of chemical reaction networks." Thesis, University of Warwick, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495019.

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Chemical and biological processes present a challenge when it comes to modelling and analysis. The models usually have to take into account many chemicals and complex interactions and in turn, they are often described by large ODE systems with complicated nonlinear terms. If there is a lack of quantitative information about the chemical interactions, there will also be parameter uncertainty in the systems. Such systems present a challenge to analyse. In response, an increasing consensus calls for emphasis on the underlying chemical reaction network structure and the use of network information to predict possible system dynamics.
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Xu, Jin, and 徐进. "A study of chemical reaction optimization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48199242.

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Complex optimization problems are prevalent in various fields of science and engineering. However, many of them belong to a category of problems called NP- hard (nondeterministic polynomial-time hard). On the other hand, due to the powerful capability in solving a myriad of complex optimization problems, metaheuristic approaches have attracted great attention in recent decades. Chemical Reaction Optimization (CRO) is a recently developed metaheuristic mimicking the interactions of molecules in a chemical reaction. With the flexible structure and excellent characteristics, CRO can explore the solution space efficiently to identify the optimal or near optimal solution(s) within an acceptable time. Our research not only designs different versions of CRO and applies them to tackle various NP-hard optimization problems, but also investigates theoretical aspects of CRO in terms of convergence and finite time behavior. We first focus on the problem of task scheduling in grid computing, which involves seeking the most efficient strategy for allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resource into account, and task scheduling is formulated as an optimization problem with three objective functions. Then, four different kinds of CRO are designed to solve this problem. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications. Secondly, we study stock portfolio selection, which pertains to deciding how to allocate investments to a number of stocks. Here we adopt the classical Markowitz mean-variance model and consider an additional cardinality constraint. Thus, the stock portfolio optimization becomes a mixed-integer quadratic programming problem. To solve it, we propose a new version of CRO named Super Molecule-based CRO (S-CRO). Computational experiments suggest that S-CRO is superior to canonical CRO in solving this problem. Thirdly, we apply CRO to the short adjacent repeats identification problem (SARIP), which involves detecting the short adjacent repeats shared by multiple DNA sequences. After proving that SARIP is NP-hard, we test CRO with both synthetic and real data, and compare its performance with BASARD, which is the previous best algorithm for this problem. Simulation results show that CRO performs much better than BASARD in terms of computational time and finding the optimal solution. We also propose a parallel version of CRO (named PCRO) with a synchronous communication scheme. To test its efficiency, we employ PCRO to solve the Quadratic Assignment Problem (QAP), which is a classical combinatorial optimization problem. Simulation results show that compared with canonical sequential CRO, PCRO can reduce the computational time as well as improve the quality of the solution for instances of QAP with large sizes. Finally, we perform theoretical analysis on the convergence and finite time behavior of CRO for combinatorial optimization problems. We explore CRO convergence from two aspects, namely, the elementary reactions and the total system energy. Furthermore, we also investigate the finite time behavior of CRO in respect of convergence rate and first hitting time.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Galagali, Nikhil. "Bayesian inference of chemical reaction networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104253.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 189-198).
The development of chemical reaction models aids system design and optimization, along with fundamental understanding, in areas including combustion, catalysis, electrochemistry, and biology. A systematic approach to building reaction network models uses available data not only to estimate unknown parameters, but to also learn the model structure. Bayesian inference provides a natural approach for this data-driven construction of models. Traditional Bayesian model inference methodology is based on evaluating a multidimensional integral for each model. This approach is often infeasible for reaction network inference, as the number of plausible models can be very large. An alternative approach based on model-space sampling can enable large-scale network inference, but its efficient implementation presents many challenges. In this thesis, we present new computational methods that make large-scale nonlinear network inference tractable. Firstly, we exploit the network-based interactions of species to design improved "between-model" proposals for Markov chain Monte Carlo (MCMC). We then introduce a sensitivity-based determination of move types which, when combined with the network-aware proposals, yields further sampling efficiency. These algorithms are tested on example problems with up to 1000 plausible models. We find that our new algorithms yield significant gains in sampling performance, with almost two orders of magnitude reduction in the variance of posterior estimates. We also show that by casting network inference as a fixed-dimensional problem with point-mass priors, we can adapt existing adaptive MCMC methods for network inference. We apply this novel framework to the inference of reaction models for catalytic reforming of methane from a set of ~/~ 32000 possible models and real experimental data. We find that the use of adaptive MCMC makes large-scale inference of reaction networks feasible without the often extensive manual tuning that is required with conventional approaches. Finally, we present an approximation-based method that allows sampling over very large model spaces whose exploration remains prohibitively expensive with ex-act sampling methods. We run an MCMC algorithm over model indicators and for each visited model approximate the model evidence via Laplace's method. Limited and sparse available data tend to produce multi-modal posteriors over the model indicators. To perform inference in this setting, we develop a population-based approximate model inference MCMC algorithm. Numerical tests on problems with around 109 models demonstrate the superiority of our population-based algorithm over single-chain MCMC approaches.
by Nikhil Galagali.
Ph. D.
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Books on the topic "Chemical reaction"

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A, Mashelkar R., Kumar R, and Indian Academy of Sciences, eds. Reactions and reaction engineering. Bangalore: Indian Academy of Sciences, 1987.

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Baxter, Roberta. Chemical reaction. Detroit, MI: Kidhaven Press, 2005.

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Caroline, Anderson. Chemical Reaction. Toronto: Harlequin, 2003.

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1931-, Tominaga Hiroo, and Tamaki Masakazu, eds. Chemical reaction and reactor design. Chichester, England: J. Wiley, 1997.

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Jyri-Pekka, Mikkola, and Warna P, eds. Chemical reaction engineering and reactor technology. Boca Raton: Taylor & Francis, 2009.

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Tapio, Salmi, Mikkola Jyri-Pekka, and Wärnå Johan. Chemical Reaction Engineering and Reactor Technology. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315200118.

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Reaction kinetics and reactor design. 2nd ed. New York: M. Dekker, 2000.

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Chemical reaction technology. Berlin: De Gruyter, 2015.

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Ancheyta, Jorge. Chemical Reaction Kinetics. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119226666.

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Chemical reaction engineering. 3rd ed. New Delhi: Wiley India, 2007.

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Book chapters on the topic "Chemical reaction"

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Liu, Zhen. "Chemico: Chemical Reaction." In Multiphysics in Porous Materials, 173–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93028-2_16.

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Nilsson, Lars-Olof, Miguel-Ángel Climent, and Oliver Weichold. "Chemical Reaction." In Methods of Measuring Moisture in Building Materials and Structures, 43–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74231-1_6.

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Diersch, Hans-Jörg G. "Chemical Reaction." In FEFLOW, 167–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38739-5_5.

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Tapio, Salmi, Mikkola Jyri-Pekka, and Wärnå Johan. "Chemical Reaction Engineering." In Chemical Reaction Engineering and Reactor Technology, 402–10. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315200118-11.

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Herges, Rainer. "Reaction Planning (Computer Aided Reaction Design)." In Chemical Structures, 385–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73975-0_40.

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Himadri, Roy Giratali. "Chemical Kinetics." In Reaction Engineering Principles, 25–94. Boca Raton : Taylor & Francis, 2016. | “A CRC title.”: CRC Press, 2018. http://dx.doi.org/10.1201/9781315367781-3.

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Schmal, Martin, and José Carlos Pinto. "Chemical equilibrium." In Chemical Reaction Engineering, 27–36. 2nd ed. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003046608-2.

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Ramaswamy, Ramakrishna. "Chaos in Chemical Dynamics." In Reaction Dynamics, 101–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-09683-3_4.

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Wincek, John C. "Chemical Reaction Safety." In Handbook of Loss Prevention Engineering, 637–79. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527650644.ch24.

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Carreón-Calderón, Bernardo, Verónica Uribe-Vargas, and Juan Pablo Aguayo. "Chemical Reaction Equilibrium." In Thermophysical Properties of Heavy Petroleum Fluids, 273–306. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58831-1_7.

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Conference papers on the topic "Chemical reaction"

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Kao, W., J. P. Singh, F. Y. Yueh, and R. L. Cook. "Study of the High Temperature Multiplex HCℓ CARS Spectrum." In Laser Applications to Chemical Analysis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/laca.1992.wc5.

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Coherent Anti-Stokes Raman Spectroscopy (CARS) is a laser based advanced combustion diagnostic technique and is being used to measure the temperature and major species concentration in a turbulent, harsh, high luminescence and reactive combustion environment.1 Recently, it has also been applied to a Coal Fired Flow Facility (CFFF), which is a unique practical combustion environment.2 Only a few CARS applications to chemical reaction have been reported. One of the important chemical reactions is a chlorination reaction. CH4 + Cℓ2 → CH3Cℓ + HCℓ. This is being used to manufacture the halogenated hydrocarbon. HCℓ is one of the product species in the reaction. So, there is a possibility to apply the multiplex HCℓ CARS spectrum for temperature measurements in a chemical reactor. This type of measurement should provide the information about mixing of CH4 and Cℓ2 and also the completion of the reaction.
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Cho, Yong Ju, Naren Ramakrishnan, and Yang Cao. "Reconstructing chemical reaction networks." In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401912.

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Cardelli, Luca, Mirco Tribastone, Max Tschaikowski, and Andrea Vandin. "Comparing Chemical Reaction Networks." In LICS '16: 31st Annual ACM/IEEE Symposium on Logic in Computer Science. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2933575.2935318.

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Zhong, Ming, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, and Jiawei Han. "ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision." In Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.767.

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Casey, Abigail, and Gregory E. Triplett. "Microfluidic reaction design for real time chemical reactions monitoring." In Frontiers in Biological Detection: From Nanosensors to Systems XIII, edited by Benjamin L. Miller, Sharon M. Weiss, and Amos Danielli. SPIE, 2021. http://dx.doi.org/10.1117/12.2575995.

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Kumar, Ashutosh, and Robin Marlar Rajendran. "Expediting Chemical Enhanced Oil Recovery Processes with Prediction of Chemical Reaction Yield Using Machine Learning." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211832-ms.

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Abstract Chemical enhanced oil recovery involves enormous combinations of chemicals, surfactants, etc. The reservoir properties such as temperature, capillary pressure, permeability keeps changing, making the process of identification of suitable chemicals even more challenging. Data driven modelling holds solutions for the complexity involved in identification of most suitable parameters for chemical enhanced oil recovery. Over the last decade, Artificial Intelligence has found its numerous applications in different branches of chemistry. From the selection of chemicals to the operating conditions during synthesis all can be estimated by the use of deep learning models. This paper presents yield prediction which is of high economic significance for chemical enhanced oil recovery, because they enable calculation of investment versus return. These models give us the conversion of reaction into products before performing the lab experiment. This will help chemists in selecting high performance chemicals for specific reservoirs without spending time on costly iterative chemical processes. These models require application of deep learning architectures like transformers and natural language processing techniques like tokenization for the prediction task. Encoder models like BERT are used for receiving the information on chemical reactions in text-based form for a reaction which is then combined with a regression extension layer to give us the desired reaction yield. We demonstrate our model on a HTE dataset with an excellent prediction score. Efforts are also made on the USPTO patent dataset which covers a wide variety of chemical reaction space. The USPTO patent dataset consists of almost every chemical reaction published since late 1970s till 2006. Diverse techniques starting with Multi Layer Perceptrons, Sequence to sequence modelling, Long short term memory models and finally transformers are employed for the improvement of accuracy of patent reactions. The paper presents detailed comparative results of predicting chemical reaction yield, and the enhancements that it will bring to Chemical Enhanced Oil Recovery. Reaction yield prediction models receive very little attention in spite of their enormous potential of determining the reaction conversion rates and its contribution to chemical enhanced oil recovery processes . The paper introduces a novel approach of modelling chemical reaction yield with deep learning models to the petroleum community. Unprecedented result of accuracy beyond 90% in predicting chemical reactions yield and its significance in chemical enhanced oil recovery has been proposed in the paper.
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Sierra Murillo, Jose Daniel. "Chemical laser based on polyatomic chemical reaction dynamics." In XXIII International Symposium on High Power Laser Systems and Applications, edited by Tomáš Mocek. SPIE, 2022. http://dx.doi.org/10.1117/12.2653041.

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Doty, David. "Timing in chemical reaction networks." In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973402.57.

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Kama Huang, Tao Hong, Xingpeng Liu, and Huacheng Zhu. "Microwave propagation in chemical reaction." In 2016 IEEE International Conference on Microwave and Millimeter Wave Technology (ICMMT). IEEE, 2016. http://dx.doi.org/10.1109/icmmt.2016.7761757.

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Chaves, M., and E. D. Sontag. "Observers for chemical reaction networks." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076512.

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Reports on the topic "Chemical reaction"

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Aris, R., and R. W. Carr. Continuous chemical reaction chromatography. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/7070042.

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Powers, T. B. Chemical reaction in a DCRT. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/663145.

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Evelyn M. Goldfield. Chemical Reaction Dynamics in Nanoscle Environments. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/891931.

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Carr, R. W. Continuous chemical reaction chromatography. Final report. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/510307.

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Keshavamurthy, Srihari. Semiclassical methods in chemical reaction dynamics. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/91884.

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Lager, Daniel, Lia Kouchachvili, and Xavier Daguenet. TCM measuring procedures and testing under application conditions. IEA SHC Task 58, May 2021. http://dx.doi.org/10.18777/ieashc-task58-2021-0004.

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Abstract:
This Subtask aims to have reliable thermal analysis methods/protocols and procedures for the characterization of aterial and reaction properties for sorption and chemical reactions of thermal energy storage (TES) applications. One goal is an inventory of already standardized measurement procedures for TCM as well as of needed characterization procedures.
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Nelson Butuk. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks. Office of Scientific and Technical Information (OSTI), September 2006. http://dx.doi.org/10.2172/902508.

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Nelson Butuk. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/881862.

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Flynn, G. Laser enhanced chemical reaction studies. Technical progress report. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10159752.

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Ziaul Huque. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/947008.

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