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Subramanian K. R. S. Sankaranarayanan

Researcher at Argonne National Laboratory

Publications -  247
Citations -  6892

Subramanian K. R. S. Sankaranarayanan is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Surface acoustic wave & Oxide. The author has an hindex of 38, co-authored 219 publications receiving 5072 citations. Previous affiliations of Subramanian K. R. S. Sankaranarayanan include Anna University & University of South Florida.

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Macroscale superlubricity enabled by graphene nanoscroll formation

TL;DR: Simulations showed that sliding of the graphene patches around the tiny nanodiamond particles led to nanoscrolls with reduced contact area that slide easily against the amorphous diamondlike carbon surface, contributing to superlubricity at engineering scale.
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Carbon-based tribofilms from lubricating oils

TL;DR: In operando formation of carbon-based tribofilms via dissociative extraction from base-oil molecules on catalytically active, sliding nanometre-scale crystalline surfaces is described, enabling base oils to provide not only the fluid but also the solid trib ofilm.
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Machine learning enabled autonomous microstructural characterization in 3D samples

TL;DR: This work introduces an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional samples obtained from molecular dynamics simulations, particle tracking data, or experiments that combines topology classification, image processing, and clustering algorithms.
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Extraordinary Macroscale Wear Resistance of One Atom Thick Graphene Layer

TL;DR: In this paper, the macroscopic wear resistance of one atom thick graphene coating is reported by subjecting it to pin-on-disc type wear testing against most commonly used steel against steel tribo-pair.
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Molecular dynamics simulation study of the melting of Pd-Pt nanoclusters

TL;DR: In this article, the thermal properties of bimetallic Pt-Pd nanoclusters of different sizes and compositions were investigated through molecular dynamics simulations using quantum Sutton-Chen (QSC) many-body potentials.