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.
2025
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1005872
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