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Article


Title

Machine Learning Approach for Application-Tailored Nanolubricants’ Design

Authors

[ 1 ] Instytut Silników Spalinowych i Napędów, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ 2 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 3 ] Instytut Maszyn Roboczych i Pojazdów Samochodowych, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ 4 ] Instytut Badań Materiałowych i Inżynierii Kwantowej, Wydział Inżynierii Materiałowej i Fizyki Technicznej, Politechnika Poznańska | [ 5 ] Instytut Fizyki, Wydział Inżynierii Materiałowej i Fizyki Technicznej, Politechnika Poznańska | [ 6 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee | [ D ] phd student

Scientific discipline (Law 2.0)

[2.2] Automation, electronics and electrical engineering
[2.6] Civil engineering and transport
[2.7] Materials engineering
[2.8] Mechanical engineering

Year of publication

2022

Published in

Nanomaterials

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 10

Article type

scientific article

Publication language

english

Keywords
EN
  • carbon nanotubes
  • nanolubricants
  • machine learning
Abstract

EN The fascinating tribological phenomenon of carbon nanotubes (CNTs) observed at the nanoscale was confirmed in our numerous macroscale experiments. We designed and employed CNT-containing nanolubricants strictly for polymer lubrication. In this paper, we present the experiment characterising how the CNT structure determines its lubricity on various types of polymers. There is a complex correlation between the microscopic and spectral properties of CNTs and the tribological parameters of the resulting lubricants. This confirms indirectly that the nature of the tribological mechanisms driven by the variety of CNT–polymer interactions might be far more complex than ever described before. We propose plasmonic interactions as an extension for existing models describing the tribological roles of nanomaterials. In the absence of quantitative microscopic calculations of tribological parameters, phenomenological strategies must be employed. One of the most powerful emerging numerical methods is machine learning (ML). Here, we propose to use this technique, in combination with molecular and supramolecular recognition, to understand the morphology and macro-assembly processing strategies for the targeted design of superlubricants.

Date of online publication

22.05.2022

Pages (from - to)

1765 - 1 - 1765 - 17

DOI

10.3390/nano12101765

URL

https://www.mdpi.com/2079-4991/12/10/1765

Comments

Article Number: 1765

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100.0

Impact Factor

5.719 [List 2021]

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