Evidence map›Paper›PMID 34354841›Full record

ArticleReaction chemistry & engineering2021

An automated computational approach to kinetic model discrimination and parameter estimation.

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

Abstract read
In one paragraph

Article in Reaction chemistry & engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  14. QuantitativeThe journal of physical chemistry. A · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Connor J TaylorInstitute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK t.w.chamberlain@leeds.ac.uk R.A.Bourne@leeds.ac.uk.ORCID https://orcid.org/0000-0002-4002-5937
Hikaru SekiDepartment of Chemistry, University of Cambridge Cambridge CB2 1EW UK.ORCID https://orcid.org/0000-0001-9663-7791
Friederike M DannheimDepartment of Chemistry, University of Cambridge Cambridge CB2 1EW UK.ORCID https://orcid.org/0000-0003-4651-0850
Mark J WillisSchool of Engineering, University of Newcastle Newcastle upon Tyne NE1 7RU UK.
Graeme ClemensChemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca Macclesfield UK.
Brian A TaylorChemical Development, Pharmaceutical Technology & Development, Operations, AstraZeneca Macclesfield UK.
Thomas W ChamberlainInstitute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK t.w.chamberlain@leeds.ac.uk R.A.Bourne@leeds.ac.uk.ORCID https://orcid.org/0000-0001-8100-6452
Richard A BourneInstitute of Process Research and Development, School of Chemistry and School of Chemical and Process Engineering, University of Leeds Leeds LS2 9JT UK t.w.chamberlain@leeds.ac.uk R.A.Bourne@leeds.ac.uk.ORCID https://orcid.org/0000-0001-7107-6297

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products,

Identifiers

PMID34354841
PMCPMC8315272

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.