AVS2009 Session TR+NS-WeM: Nanomechanics and Nanotribology

Unfortunately, conventional friction force microscopy (FFM) has limitations inherent to the experimental configuration when it comes to contact area dependent measurements: Apart from the rather limited variety of material combinations, the fixed tip radius makes it especially difficult to analyze effects as a function of the contact area. In order to analyze the contact area dependence of interfacial friction, the friction between two objects in relative motion with a well-defined contact area should be measured instead of the friction between tip and surface. Therefore we have manipulated nanometer scale metallic particles on atomically flat surfaces by contact mode atomic force microscopy techniques and quantitative information on interfacial friction has been extracted from the lateral manipulation of these nanoparticles¹. In previous experiments² we found two distinct frictional states during particle sliding of Sb-particles on HOPG substrate: Some particles show finite friction increasing linearly with interface area, thus reinforcing Amonton’s law at the nanoscale, other particles assume a state of frictionless or ‘superlubric’ sliding.

In this contribution we show new measurements which were concentrated on the particles exhibiting vanishing friction. By optimizing our experimental sensitivity we succeeded for the first time to analyze the contact area dependence of friction force of these low friction particles. In contrast to the ‘Amonton’-like particles, interfacial friction of the low friction particles showed strongly nonlinear contact area dependence. The experimental results are compared to theoretical considerations, which predict that the shear stress of sliding superlubric particles should decrease with increasing particle size.

²Dietzel et al., Phys. Rev. Lett. 101, 125505 (2008).

*JAH acknowledges support from The Air Force Office of Scientific Research (AFSOR) as part of the Extreme Friction MURI and from The Office of Naval Research. JAH & RWC also acknowledge support from AFOSR’s Aerospace, Chemical, and Material Sciences Directorate.

The topography of a surface can be characterized by three fractal parameters: its surface roughness, its roughness exponent, and its lateral correlation length [1]. In 2001, T.S. Chow [2] predicted that a decrease in adhesion by orders of magnitude would follow from increasing the roughness exponent of a surface (i.e. by making it smoother), and that adhesion could also be lowered by decreasing its lateral correlation length (shorter wavelength). In 2007 [3], we published a simple analytical model, together with experimental data, that demonstrated the strong influence of surface roughness on adhesion. This year, using atomic force microscopy, experimental data were collected from MEMS sidewalls of varying topography but with the same chemical treatment in order to untangle which of the three fractal parameters is – or are – most important in determining adhesion. The data are inconsistent with Chow’s predictions; his assumption was that only the asperities on the surface contribute to the adhesion. In contrast, our numerical simulations of the tip-sample adhesion for surfaces with varying fractal parameters include both the asperities and the bulk of the sample.

Together with the simulations, the previous and current experimental data support the conclusion that surface roughness is a significant predictor of adhesion, with the adhesion dropping by more than an order of magnitude for a roughness change from 1 to 10 nm. For the roughness exponent, the simulations follow the same trend as Chow’s predictions, in that adhesion should decrease with increasing roughness exponent (smoother), but rather than predicting orders of magnitude change, the simulations reveal only a 20% decrease as the roughness exponent changes from 0 to 1. Here, the experimental data were more consistent with the simulation than Chow’s predictions, although they were not conclusive. The scatter in the data was large, and the range of the roughness exponent only varied from 0.85 to 0.99, for which the simulations predict a change in adhesion of approximately 10%. For the lateral correlation length, the experiment showed a wide range of adhesion values for smaller correlation lengths and low adhesion for larger correlation lengths (longer wavelengths); we are still investigating the theoretical basis of this observation. We hope to contribute to the understanding of adhesion so as to minimize stiction in MEMS.

Although optical coatings are generally designed for their functional requirements it is often the mechanical properties of the system which limits performance. For instance, the major in-service failure mechanism of modern solar control coatings for architectural glass can be scratch damage. Many of these coatings are multilayer structures made from individual layers of less than 100nm thickness and different coating architectures are possible (i.e. different layer materials, thicknesses and stacking order). To assess their mechanical response, coated samples may be subjected to indentation and scratch tests; however, it is not always possible to predict the failure mechanisms of such coatings and modelling approaches have to be developed to understand the deformation mechanisms. This presentation will focus on the problems of measuring the plasticity and fracture properties of very thin coatings (<100nm) and the use of multi-scale modelling approaches to predict performance for coatings on glass.

In the new Age of Molecular Engineering, one of the objectives is to cognitively design molecules that if condensed, provide materials with anticipated properties. In regards of tribological systems, such as lubricants, the objective would be to offer chemists with molecular design parameters based on which molecules could be synthesized that dissipate energy in a targeted fashion. Thereby, the input would come either directly from experimental observations, or indirectly via computer simulations that are based on models that are relevant to the materials.

This is in contrast to the majority of current research efforts in tribology or nanotribology. State-of-the-art molecular models in tribology focus on generic periodic potentials that are thermally and mechanically inert. Experimental methods provide mostly only phenomenological parameters, such as the friction coefficient, or process descriptive parameters, such as the stick-slip phenomena. Such generic “black-box” approaches, fail to address frictional energy dissipation that are linked to material intrinsic molecular or submolecular modes of relaxation, and hence, do not provide the necessary input for a cognitive molecular design strategy for an effective tribological system.

This talk will highlight the importance of material intrinsic relaxation modes for frictional dissipation involving organic systems. Phenomenological friction analysis data, i.e., friction coefficients from friction-load curves, will be contrasted with spectroscopic data from an intrinsic friction analysis (IFA), involving a time-temperature superposition analysis of friction-velocity isotherms. Both data analysis methodologies, involving lateral force microscopy, reveal an astonishing correspondence regarding the dissipated energy and the energy involved in activating intrinsic relaxation modes and cooperative phenomena. Specifically, we will address surface and sub-surface energy relaxations in amorphous macromolecular model system (e.g., polystyrene) and their relevance to frictional energy dissipation within well defined loading regimes. Depending on the coupling strength (cause for cooperativity) between molecular actuators involved (e.g., rotating side chains or translating polymer backbones) the dissipation in energy can carry a significant entropic energy contribution, accounting for up to 80% of the apparent Arrhenius activation energy. The IFA methodology discussed in this paper that provides direct insight into the enthalpic and entropic energy contributions of friction dissipation processes is shown to be well suited as an analysis tool towards cognitive molecular engineering in tribology.