scholarly journals PyDSC: a simple tool to treat differential scanning calorimetry data

Author(s):  
Aline Cisse ◽  
Judith Peters ◽  
Giuseppe Lazzara ◽  
Leonardo Chiappisi

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.

Author(s):  
Sooky Winkler ◽  
Alexander Penlidis ◽  
Stephen F. Corbin ◽  
Mary A. Wells ◽  
Michael J. Benoit ◽  
...  

2003 ◽  
Vol 3 (34) ◽  
pp. 169-178 ◽  
Author(s):  
Valerij Ya. Grinberg ◽  
Tatiana V. Burova ◽  
Natalia V. Grinberg ◽  
Alexander Ya. Mashkevich ◽  
Irina G. Plashchina ◽  
...  

2011 ◽  
Vol 133 (01) ◽  
pp. 30-33
Author(s):  
Peter Fryer ◽  
Serafim Bakalis

This article discusses a study focusing on developing a mathematical model for creating and modifying the structure of chocolates. In the experimental study at the University of Birmingham's Centre for Formulation Engineering, researchers cooled and heated chocolate through rapid programmed temperature changes and then studied what happened using differential scanning calorimetry. The data was fitted to six kinetic processes. To make the modeling easier, the system of six polymorphs and liquid chocolate was simplified to model only three materials: stable solids, unstable solids, and melt. Then equations were developed to describe the nucleation of crystals, growth of stable and unstable phases, and the melting of the stable and unstable solids. The model developed simplifies the number of crystal forms, but this simplification makes it possible to model differential scanning calorimetry data. Once fitted to differential scanning calorimetry data over a range of cooling rates, the model can then be used both to predict behavior and to explain what is happening in the process. The model can be used to show how ‘frozen cone’ methods work.


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