Dissertação PPG.EGPSA
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/3
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Resultados da Pesquisa
Item Desenvolvimento de método para obtenção de trilhas condutivas com grafeno por esfoliação em fase líquida aplicadas à eletrônica impressa na Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) VALADÃO, Suélem Cabral; LEITE, Jandecy CabralThis study presents the development and characterization of conductive tracks based on graphene obtained via liquid-phase exfoliation (LPE), applied to printed circuit boards (PCBs) on FR4 substrates. Graphene synthesis was carried out by dispersing graphite flakes in a surfactant-assisted solvent, followed by ultrasonic exfoliation for 30 minutes. The resulting material was characterized using XRD, SEM, and Raman spectroscopy, confirming the presence of multilayer graphene with moderate structural defects. Conductive inks were formulated with different proportions of Jutaicica resin, a natural Amazonian binder, achieving good adhesion to the substrate. Tests indicated average track thickness of ~72 µm and electrical conductivity of 0.75 and 0.91 (Ω.cm)-¹ for inks 1 and 2, respectively. The findings demonstrate the potential of LPE graphene combined with Amazonian biodiversity inputs for the formulation of functional and sustainable conductive inks, aligned with sustainability principles and Industry 4.0.Item Desenvolvimento de um sistema de monitoramento inteligente para determinação do ponto ótimo para o abate de animais de produção utilizando visão computacional e inteligência artificia(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) FARIAS, Djalma Farias e; NASCIMENTO, Manoel Henrique ReisBeef cattle production faces challenges in accurately identifying the ideal slaughter time for Nelore cattle, affecting yield and animal welfare. Traditional monitoring methods, such as manual weighing and visual inspection, may compromise meat quality and increase costs. This study developed an intelligent monitoring system using computer vision and artificial intelligence to determine the optimal slaughter moment for Nelore cattle. The system applies deep learning algorithms and video cameras to analyze animals in real time, considering productive and morphological parameters. A cost-benefit analysis was performed based on hardware, software, and operational returns. Results showed increased productivity, improved slaughter precision, and an estimated 268.42% ROI over five years, demonstrating the technical and economic feasibility of AI-based livestock monitoring.