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01.11.2022 | Original Paper

Oligo(Butylene-Succinate) and Nanocatalyst Effect Prediction: Could a Neural Network Determine the Lowest Melting Temperature of this Phase-Changing Material Better than a Classic Approach?

verfasst von: Emiliane Daher Pereira, Fernando Gomes de Souza Jr, Kaushik Pal, Fabíola da Silveira Maranhão, Romildo Dias Toledo Filho, Nicole Pagan Hasparyk, Vinicius de Melo Monteiro, Maria Clara Nascimento Dantas, João Gabriel Passos Rodrigues

Erschienen in: Topics in Catalysis | Ausgabe 19-20/2022

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Abstract

Phase change materials (PCMs) are functional materials that can absorb thermal energy in latent heat. Among these, the environmentally-friendly Bio-PCMs, obtained from renewable sources, have drawn the attention of researchers because of its advantages. This work aimed to produce a Bio-PCM based on oligo (butyl succinate) with the lowest melting temperature. To this end, different reaction conditions were tested and the information obtained were studied with nonlinear modeling and machine learning to investigate the ideal condition to be used. The best synthesis condition from the Neural Network model was prepared and provided material with a maximum melting temperature of 48 ºC. Thus, the novelty of this work is to combine classical knowledge from Polymer Chemistry with modeling via Neural Networks, with that is possible to minimize experimental time, leading to results that target academic efforts towards goal properties, shortening the time needed to convert new chemical platform species into disruptive new materials.

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Titel: Oligo(Butylene-Succinate) and Nanocatalyst Effect Prediction: Could a Neural Network Determine the Lowest Melting Temperature of this Phase-Changing Material Better than a Classic Approach?
verfasst von: Emiliane Daher Pereira
Fernando Gomes de Souza Jr
Kaushik Pal
Fabíola da Silveira Maranhão
Romildo Dias Toledo Filho
Nicole Pagan Hasparyk
Vinicius de Melo Monteiro
Maria Clara Nascimento Dantas
João Gabriel Passos Rodrigues
Publikationsdatum: 01.11.2022
Verlag: Springer US
Erschienen in: Topics in Catalysis / Ausgabe 19-20/2022
Print ISSN: 1022-5528
Elektronische ISSN: 1572-9028
DOI: https://doi.org/10.1007/s11244-022-01728-w

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