Evidence map›Paper›PMID 36615621›Full record

ArticleMolecules (Basel, Switzerland)2023

Advanced Fitting Method for the Kinetic Analysis of Thermogravimetric Data.

Ivan Bondarchuk, Sergey Bondarchuk, Alexander Vorozhtsov, Alexander Zhukov

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

4 authors.

Ivan BondarchukLaboratory for High Energy and Special Materials National Research, Tomsk State University, Lenin Avenue, 36, 634050 Tomsk, Russia.ORCID 0000-0001-6995-0547
Sergey BondarchukLaboratory for High Energy and Special Materials National Research, Tomsk State University, Lenin Avenue, 36, 634050 Tomsk, Russia.ORCID 0000-0001-6842-9143
Alexander VorozhtsovLaboratory for High Energy and Special Materials National Research, Tomsk State University, Lenin Avenue, 36, 634050 Tomsk, Russia.
Alexander ZhukovCenter for Additive Technologies National Research Tomsk State University, Lenin Avenue, 36, 634050 Tomsk, Russia.

Funding

Russian Science Foundation 22-69-00108
6 · The paper itself

Abstract

The article considered the solution of the inverse problem of chemical kinetics of the analysis of experimental data of a thermogravimetric experiment at a constant sample heating rate. The fitting method for identifying the parameters of a kinetic triplet using the integral method for a model of a solid-state reaction based on the modified Arrhenius equation is described. The effectiveness of the proposed approach was confirmed by solving test cases for low, medium, and high rates of material conversion. Unlike other methods, setting the parameters of the reaction mechanism is not required, as they are determined by the solution. Solutions for real data of TGA studies with high and low sample heating rates were compared with the results obtained by other authors and experimental data. A description of the full cycle of calculations used to identify kinetic parameters from thermogravimetric experimental data is given, from the derivation of calculated relationships to the implementation of a short (three to five formulas) program code for MS Excel spreadsheets. The presented code is easy to verify and reproduce and can be modified to solve various problems.

Indexed as

Hot TemperatureKineticsThermogravimetryfitting methodgeneral temperature integralkinetic analysiskinetic tripletmodified Arrhenius equationsolid-state reaction mechanismthermogravimetric analysis

Identifiers

PMID36615621
PMCPMC9823974

What OpenQuestion holds

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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.