PyDSC: a simple tool to treat differential scanning calorimetry data

Aline Cisse,Leonardo Chiappisi,Giuseppe Lazzara,Judith Peters

doi:10.1007/s10973-020-09775-9

Abstract

Herein, we describe an open-source, Python-based, script to treat the output of differential scanning calorimetry (DSC) experiments, called pyDSC, available free of charge for download at https://github.com/leonardo-chiappisi/pyDSC under a GNU General Public License v3.0. The main aim of this program is to provide the community with a simple program to analyze raw DSC data. Key features include the correction from spurious signals, and, most importantly, the baseline is computed with a robust, physically consistent approach. We also show that the baseline correction routine implemented in the script is significantly more reproducible than different standard ones proposed by proprietary instrument control software provided with the microcalorimeter used in this work. Finally, the program can be easily applied to large amount of data, improving the reliability and reproducibility of DSC experiments.

Highlights

Differential scanning calorimetry (DSC) is a powerful thermo-analytical technique which detects heat changes associated with physical and chemical transformation in biological and non-biological samples
To test the robustness of the pyDSC script, the enthalpy of micellization recorded for a solution of the triblock copolymer EO26–PO40–EO26 was evaluated using pyDSC and two different algorithms provided by the Setaram data analysis software: “linear”—which uses a line from the starting to the end point arbitrarily selected by the user—and “curve”— which generates a spline curve from the selected initial/final points that are selected by user—using different regions for the baseline evaluation
We present pyDSC a simple, pythonbased script to treat differential scanning calorimetry data

Summary

Introduction

Differential scanning calorimetry (DSC) is a powerful thermo-analytical technique which detects heat changes associated with physical and chemical transformation in biological and non-biological samples. Due to the simplicity of the technique, the relatively low-cost of the apparatus, the ease of data analysis, DSC found wide application in very diverse fields of both an academic and industrial research activities. Several excellent reviews covering the use of DSC can be found in the literature. A DSC experiment is used to extract the enthalpy change H of the studied process and the temperature of the transition. This analysis is straightforward, especially when the data exhibit a good signal-to-noise ratio. A DSC curve usually contains an abundance of information, which can be extracted by an in-depth analysis [7, 11, 12, 20, 24, 28, 32]

Methods

Results

Conclusion