Artificial Intelligence (AI) tools and methods are dramatically innovating the application protocols of most polymer characterization techniques. In this paper, we demonstrate that, with the aid of custom-made and properly trained machine learning algorithms, analytical Crystallization Elution Fractionation (aCEF) can be changed from an ancillary to a standalone approach usable to identify and categorize commercially relevant polyolefin materials without any prior information. The proposed protocols are fully operational for monomaterials, whereas for multimaterials, integration with AI-aided 13C NMR is a realistic intermediate step.
AI-Aided Crystallization Elution Fractionation (CEF) Assessment of Polyolefin Resins / Brighel, L., Scuotto, G.M.L., Antinucci, G., Cipullo, R., Busico, V.. - In: POLYMERS. - ISSN 2073-4360. - 17:12(2025), p. 1597. [10.3390/polym17121597]
AI-Aided Crystallization Elution Fractionation (CEF) Assessment of Polyolefin Resins
Antinucci, Giuseppe;Cipullo, Roberta;Busico, Vincenzo
2025
Abstract
Artificial Intelligence (AI) tools and methods are dramatically innovating the application protocols of most polymer characterization techniques. In this paper, we demonstrate that, with the aid of custom-made and properly trained machine learning algorithms, analytical Crystallization Elution Fractionation (aCEF) can be changed from an ancillary to a standalone approach usable to identify and categorize commercially relevant polyolefin materials without any prior information. The proposed protocols are fully operational for monomaterials, whereas for multimaterials, integration with AI-aided 13C NMR is a realistic intermediate step.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


