Estudo cinético do comportamento térmico do oxicarbonato de nióbio obtido por síntese solvotérmica assistida por micro-ondas.

• Ano de defesa: 2021
• Autor(a) principal: Tiago Henrique Silva Madalena
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICX - DEPARTAMENTO DE QUÍMICA; Programa de Pós-Graduação em Química; UFMG
• Palavras-chave em Português: Oxicarbonato de nióbio; Comportamento térmico; Modelos cinéticos; Método isoconversional; Rede neural MLP; Niobium oxycarbonate; Thermal behavior; Kinetic models isoconversional method; MLP neural network; Físico-química; Compostos de nióbio Síntese; Cinética química; Decomposição química; Redes neurais (Computação); Microondas; Análise térmica; Nióbio
• Link de acesso: http://hdl.handle.net/1843/38565

In this work it was performed a kinetic study of the thermal behavior of a niobium oxycarbonate, Nb2O4CO3.H2O. This compound is a new and original material recently obtained at the Department of Chemistry / UFMG. This oxycarbonate was prepared from a microwave-assisted solvothermal method, resulting nanocrystals with a well-defined crystalline habit and particle size ranging from 200 to 500 nm. This new material decomposes from a multi-step process with an intermediate amorphous phase, resulting in crystalline T-Nb2O5 with concomitant gas production (H2O, CO and CO2) and CO2 absorption. An isoconversional kinetic analysis was used to study these complex decomposition events. The Kissinger-Akahira-Sunose (KAS) methodology was used to accurately obtain the activation energy and the pre-exponential factor values associated to the kinetic constants. Different kinetic models were used for a mathematical description of these data. However, single kinetic models were not adequate to describe these complex decomposition events. An accurate analysis was performed from a new theoretical approach based on the construction of a multilayer perceptron neural network (MLP). This neural network uses the different kinetic models in a combined and simultaneous modeling for an accurate analysis of such thermal decomposition events. These different decomposition events where successfully described by a combination of first-order kinetic model with 3D diffusion and geometrical contraction models.