2019 journal article

Nanotribological Performance Factors for Aqueous Suspensions of Oxide Nanoparticles and Their Relation to Macroscale Lubricity


By: B. Acharya, T. Pardue, L. Su, A. Smirnov, D. Brenner & J. Krim

author keywords: Nanotribology; QCM; Nanolubricants; Oxide nanoparticles; Nanoparticle additives; Al2O3; TiO2; SiO2; Fe2O3
Source: Web Of Science
Added: August 5, 2019

Quartz crystal microbalance (QCM) measurements of nanotribological properties of statistically diverse materials combinations of nanoparticles and substrate electrodes in aqueous suspensions are reported and compared to macroscale measurements of the same materials combinations for a subset of the nanoparticle combinations. Four ceramic nanoparticles, TiO2, SiO2, Al2O3, and maghemite (γ-Fe2O3) and ten substrate materials (Au, Al, Cr, Cu, Mo, Ni, Pt, SiO2, Al2O3, and SS304) were studied. The QCM technique was employed to measure frequency and motional resistance changes upon introduction of nanoparticles into the water surrounding its liquid-facing electrode. This series of experiments expanded prior studies that were often limited to a single nanoparticle - solid liquid combination. The variations in QCM response from one nanoparticle to another are observed to be far greater than the variation from one substrate to another, indicating that the nanoparticles play a larger role than the substrates in determining the frictional drag force levels. The results were categorized according to the direction of the frequency and motional resistance changes and candidate statistical performance factors for the datasets were generated. The performance factors were employed to identify associations between the QCM atomic scale results and the macroscale friction coefficient measurements. Macroscale measurements of friction coefficients for selected systems document that reductions (increases) in motional resistance to shear, as measured by the QCM, are linked to decreases (increases) in macroscale friction coefficients. The performance factors identified in the initial study therefore appear applicable to a broader set of statistically diverse samples. The results facilitate full statistical analyses of the data for identification of candidate materials properties or materials genomes that underlie the performance of nanoparticle systems as lubricants.