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Machine Learning Approach for Application-Tailored Nanolubricants’ Design


[ 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, electrical engineering and space technology
[2.7] Civil engineering, geodesy and transport
[2.8] Materials engineering
[2.9] Mechanical engineering

Year of publication


Published in


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

Article type

scientific article

Publication language


  • carbon nanotubes
  • nanolubricants
  • machine learning

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


Pages (from - to)

1765 - 1 - 1765 - 17





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


Impact Factor


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