ArticleACS omega2025
Enhancement and Machine Learning-Based Prediction of Tribological Properties of PC/PBT/GNPs Nanocomposites.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
1 author.
Funding
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Abstract
Ternary polycarbonate-poly-(butylene terephthalate)/graphene nanoplatelets (PC-PBT/GNP) nanocomposites were fabricated by melt-compounding. The nanofiller dispersion, microstructural changes, and mechanical and tribological properties of the produced samples were investigated. The friction and wear performance of the produced samples were evaluated with a pin-on-disc test rig under 5 and 10 N loads against an AISI 52100 steel ball to evaluate the effect of GNP filler fraction on the friction and wear performance of PC-PBT blends subject to polymer-metal contact in automotive and aviation industries. The impact strength, tensile modulus, and flexural modulus of the neat PC-PBT blend were improved by 78, 46, and 38%, respectively, with the optimum nanofiller fraction of 5 wt %. In parallel to the improved mechanical properties, ∼86 and ∼90% reduction in specific wear rates were achieved under 5 and 10 N loads, respectively, compared to the neat sample, which is attributable to multiple factors such as increased stiffness contact surface, intrinsic lubricating characteristics of GNPs, a more tribo-layer-oriented wear regime at higher filler fractions, and increased crystallinity via the reduced extent of transesterification. The Least-Squares Boosting (LSBoost) machine learning model provided the highest prediction accuracy with
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Registered trials
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