Síntese, caracterização e estudo da estabilidade de ferrofluídos preparados a partir de nanopartículas de ferrita de cobalto

• Ano de defesa: 2012
• Autor(a) principal: Lemes, Maykon Alves
• Orientador(a): Oliveira, Anselmo Elcana de
• Banca de defesa: Oliveira, Anselmo Elcana de, Oliveira, Sérgio Botelho de, Braga, Jez William Batista
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Química (IQ)
• Departamento: Instituto de Química - IQ (RG)
• País: Brasil
• Palavras-chave em Português: Ferrofluídos; Quimiometria; Análise de imagens
• Palavras-chave em Inglês: Ironfluids; Chemometrics; Image analysis
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::QUIMICA
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3272

Cobalt ferrite nanoparticles were synthesized in the present work by the coprecipitation method from copper (II) and iron (III) chlorides in an alkaline solution under stirring at a speed of 20,000 rpm and heating at 70 oC. Oleic and stearic acids were used as surfactants, and the resulting magnetic gels were then dispersed in mineral oil. XRD, FTIR, TGA, Mössbauer spectroscopy, and VSM were used for characterizing the cobalt ferrite nanoparticles, and DLS and digital images for the resulting colloidal dispersion. The nanoparticle dispersion was also investigated by a 23-1 fractional factorial design. Digital images acquired by a CCD camera in RGB format were used to estimate the stability of the nanofluids. Multivariate image analysis in conjunction with PCA was employed to evaluate sedimentation times of the synthesized nanofluids. PLS, PCR, and MLR combined with genetic and successive projection algorithms were also used to estimate both the sedimentation time and the concentration of the nanoparticles in the mineral-oil based nanofluids. The square of the productmoment correlation coefficient through the digital image data for the multivariate calibration methods were higher than 0.93. The best prediction performance for estimating the sedimentation time of the nanoparticles was achieved by GA-PLS (RMSEP = 0.77 min and r2 = 0.9997). On the other hand, PCR presented the best results for estimating the concentration of the nanoparticles in the nanofluids (RMSEP = 0.029% min and r2 = 0.9991).