Advancing analysis of differential scanning calorimetry data – Converging DSC and AI

Differential scanning calorimetry (DSC) is a prominent analytical technique in materials science, offering detailed insights into the thermal properties and behavior of materials. DSC provides valuable information for understanding material composition, structure, and performance by measuring the heat flow associated with thermal events. Interpreting DSC curves is a complex process that requires substantial expertise, and misinterpretation can lead to inaccurate conclusions about material properties. Integrating artificial intelligence (AI) into DSC presents a transformative opportunity to significantly enhance the accuracy, precision, and reliability of thermal analysis. By employing advanced AI algorithms, researchers can analyze DSC data in real time, allowing immediate insights into thermal transitions, such as glass transition, crystallization, and melting. This review outlines how the convergence of DSC and AI can not only expedite the research process but also standardize data interpretation, minimize human error, and reduce reliance on specialized operator expertise, thereby empowering non-experts to interpret DSC data with greater confidence and accuracy. This integration enhances the accessibility, reproducibility, and credibility of thermal data derived from advanced thermal analysis techniques across various scientific and industrial sectors.