We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
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?
- Authors
Pereira, Emiliane Daher; de Souza Jr, Fernando Gomes; Pal, Kaushik; da Silveira Maranhão, Fabíola; Filho, Romildo Dias Toledo; Hasparyk, Nicole Pagan; de Melo Monteiro, Vinicius; Dantas, Maria Clara Nascimento; Rodrigues, João Gabriel Passos
- 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.
- Subjects
NANOPARTICLES; LOW temperatures; BIOMASS liquefaction; LATENT heat; PHASE change materials; CHEMICAL models; MACHINE learning
- Publication
Topics in Catalysis, 2022, Vol 65, Issue 19/20, p1984
- ISSN
1022-5528
- Publication type
Article
- DOI
10.1007/s11244-022-01728-w