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A Novel Method to Evaluate Nanofluid Stability Using Multivariate Image Analysis

Abstract

Multivariate image analysis(MIA)-based approach in conjunction with chemometrics is proposed to evaluate the stability of nanofluids prepared by dispersing cobalt ferrite nanoparticles in mineral insulating oil under vigorous mechanical stirring (20.000 rpm, Ultra-Turrax). Three different magnetic fluids were evaluated: a) oleic acid-coated magnetic fluid (OAMF) at 0.00001% (m/v); b) stearic acid-coated magnetic fluid (SAMF) at 0.01% (m/v); and c) non-coated magnetic fluid (NCMF) at 1% (m/v). Magnetic nanoparticles as powder or dispersed in oil were characterized by XRD, FTIR, Mössbauer, and DLS. Glass test-tubes were filled with magnetic fluid and its digital images were recorded during 67 days for OAMF, 20 days for SAMF, and 90 min for NCMF. According to the principal component analyses of the acquired digital images OAMF remained stable during 39 days. On the other hand, the less stable fluids, SAMF and NCMF, presented a drastic reduction of their sedimentation rates after 10 days, and 26 min, respectively. Multivariate regression methods (MLR, PCR, and PLS) combined with genetic algorithm (GA-MLR, GA-PCR, and GA-PLS) were also employed in order to estimate NCMF sedimentation times and cobalt ferrite concentrations in OAMF. GA-PLS provided the best sedimentation time estimates and PCR showed better performance when estimated the cobalt ferrite nanoparticle concentrations. As a result, the proposed method is efficient, fast, non-destructive, low-coast, accurate, can be employed from low to high concentrated nanofluids, and even when they are dark in color.

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Publication details

The article was received on 10 Mar 2017, accepted on 05 Sep 2017 and first published on 06 Sep 2017


Article type: Technical Note
DOI: 10.1039/C7AY00645D
Citation: Anal. Methods, 2017, Accepted Manuscript
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    A Novel Method to Evaluate Nanofluid Stability Using Multivariate Image Analysis

    A. E. de Oliveira, M. A. Lemes and D. Rabelo, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY00645D

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