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

Abstract 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 $$\mu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> DSC3 evo microcalorimeter batch cells to provide a guideline toward the selection of the sample amount to minimize heat capacity measurement error in $$\mu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> DSC. 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 [C 4 mim][Tf 2 N] (1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide) ionic liquid 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 R 2 score. The Python package PyCpep based on the trained ANN model was developed to predict the deviation in the heat capacity measurement.