Heat capacity measurements by a Setaram μDSC3 evo microcalorimeter: estimation of deviation in the measurement, advanced data analysis by mathematical gnostics, and prediction by the artificial neural network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985858%3A_____%2F24%3A00598190" target="_blank" >RIV/67985858:_____/24:00598190 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10973-024-13505-w" target="_blank" >https://link.springer.com/article/10.1007/s10973-024-13505-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10973-024-13505-w" target="_blank" >10.1007/s10973-024-13505-w</a>

Result language

angličtina

Original language name

Heat capacity measurements by a Setaram μDSC3 evo microcalorimeter: estimation of deviation in the measurement, advanced data analysis by mathematical gnostics, and prediction by the artificial neural network

Original language description

The aim of the work is to study the variation in the isobaric heat capacity measurement due to changes in the amount of sample and the calibration standard using a Setaram μDSC3 evo microcalorimeter batch cells to provide a guideline toward the selection of the sample amount to minimize heat capacity measurement error in μDSC. Moreover, overall variation, variation due to the sample amount, and variation due to the calibration standard (reference) amount in heat capacity measurement were estimated for different amounts of the sample or/and the calibration standard material. In the present work, heat capacity measurements were taken for [C4mim][Tf2N] (1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide) ionicnliquid as a sample material and 1-butanol as a calibration standard. A novel non-statistical approach, mathematical gnostics (MG), was used for data analysis of measured heat capacities data. Moreover, the artificial neural network (ANN) model was developed to predict the deviation in the heat capacity measurement with 99.83% accuracy and 0.9939 R2 score. The Python package PyCpep based on the trained ANN model was developed to predict the deviation in the heat capacity measurement.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10403 - Physical chemistry

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Thermal Analysis and Calorimetry

ISSN

1388-6150

e-ISSN

1588-2926

Volume of the periodical

150

Issue of the periodical within the volume

1

Country of publishing house

HU - HUNGARY

Number of pages

13

Pages from-to

313-325

UT code for WoS article

001309307400001

EID of the result in the Scopus database

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