An automated computational approach to kinetic model discrimination and parameter estimation

Taylor, Connor J.; Seki, Hikaru; Dannheim, Friederike M.; Willis, M.J.; Clemens, Graeme; Taylor, Brian; Chamberlain, Thomas W.; Bourne, Richard A.

doi:10.1039/d1re00098e

Cited by 24 publications

(18 citation statements)

References 44 publications

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“…More commonly encountered are very fast elementary reactions that occur in some processes, where it is more appropriate to describe an entire reaction with an observed rate rather than the combination of its individual elementary parts. This leads to circumstances where chemical species that are reported to have a zero-order, second-order or even noninteger-order dependence, as it is much more practical to describe the physical model in this way. − …”

Section: Kinetic Modelingmentioning

confidence: 99%

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A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

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Section: Kinetic Modelingmentioning

confidence: 99%

“…At 35 °C: blue solid triangles = experimental data, ---= kinetic fit. At 40 °C: blue solid circles = experimental data, •••••• = kinetic fit 92.…”

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confidence: 99%

A Brief Introduction to Chemical Reaction Optimization

et al. 2023

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“…Our group recently reported the integration of a chemical reaction network (CRN) identification tool 15 with a continuous-flow platform, every feasible reaction model was constructed and evaluated based on their respective suitability to the experimental data. 40,76 This methodology was used to determine physical model information for the formation of the cardioselective beta blocker, Metoprolol, as shown in Scheme 5. This study was found to be 24% cheaper than steady-state kinetic experiments and 106% cheaper than traditional design of experiments approaches to identify kinetic information, when considering material consumption alone.…”

Section: Tandem Experimentation and Analysismentioning

confidence: 99%

Modern advancements in continuous-flow aided kinetic analysis

et al. 2022

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“…Building from the work of Tsu et al, 35 Bourne and coworkers created an automated RNI approach. [36][37][38] Their approach assesses full reaction profiles against all stoichiometrically feasible kinetic models to identify which fits best. It assumes a rate equation, a common assumption in kinetic model discrimination.…”

Section: Introductionmentioning

confidence: 99%