Tribol. Properties of Polymers with Bio-Carbon Lubricants

The production of replacement parts for worn components still consumes more primary energy than, for example, the entire aviation industry before the COVID-19 pandemic. In order to manufacture tribological plastics in line with climate goals and within the remaining CO2 budget, their consistent decarbonization is essential. While biobased polymers are already on the market, tribologically critical solid lubricants such as graphite and polytetrafluoroethylene (PTFE) are still very CO2 and energy-intensive. In the case of PTFE, there are also concerns regarding long-term health and environmental damage (“forever chemicals”).

Therefore, biobased alternatives to graphite and PTFE were investigated in collaboration with the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB). Agricultural byproducts were pyrolyzed to increase their carbon content, then ground and processed into a composite material with a polyamide 6 matrix. Dry sliding tests against steel showed that these materials exhibited friction coefficients and wear rates only slightly higher than those of a reference material with fully synthetic graphite. The sliding surfaces had only minor grooves in the sliding direction and no signs of disruption. Measurements of the transfer film formation demonstrated that significantly less abrasion adhered to the steel counterpart compared to synthetic graphite, which explains the observed absence of adhesive damage and fatigue, as well as the lower contribution of adhesion to overall friction.

IVW and ATB are planning a joint cooperation in publicly funded projects with the industry in order to promote the use of composite materials with biobased solid lubricants in industrial applications.

Field of competence

Industry sectors

Project status

  • Current


Andreas Gebhard

Manager Tribology

Special expertise: tribology of plastics, tribometers and tribometry, transfer films, development of plastic composites, laboratory information management, data modeling