PyDSC: a simple tool to treat differential scanning calorimetry data
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PyDSC: a simple tool to treat differential scanning calorimetry data Aline Cisse1,2 · Judith Peters1,2 · Giuseppe Lazzara3 · Leonardo Chiappisi1,4 Received: 12 December 2019 / Accepted: 30 April 2020 © The Author(s) 2020
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. Keywords DSC · Baseline correction · Python · Phase transition · Protein conformation · Polymer stability
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. With focus on colloidal and biophysical science, we address the reader to some covering topics such as protein conformation [16, 18, 31], lipid phase transitions [13], (bio) polymer stability [26], and on mixed systems [17, 25, 30].
* Leonardo Chiappisi [email protected] 1
Institut Max von Laue - Paul Langevin, 71 avenue des Martyrs, 38042 Grenoble, France
2
Laboratoire Interdisciplinaire de Physique, Univ. Grenoble Alpes, CNRS, 140 Rue de la Physique, 38000 Grenoble, France
3
Dipartimento di Fisica e Chimica, Università degli Studi di Palermo, Viale delle Scienze pad 17, 90128 Palermo, Italy
4
Stranski Laboratorium für Physikalische und Theoretische Chemie, Institut für Chemie, Technische Universität Berlin, Strasse des 17. Juni 124, 10623 Berlin, Germany
In many cases, a DSC experiment is used to extract the enthalpy change 𝛥H 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. However, 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]. For every, even simple, analysis of a DSC curve, a correct evaluation of the baseline represents a critical step, whose importance is
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