L. Li, N. Alsharif, V. Saygin, K.A. Brown
Boston University,
United States
Keywords: polymer, nanomaterials, scanning probe, characterization, machine learning
Summary:
A fascinating feature of polymers is that their internal length scales are often commensurate with the scales at which they are structured, giving rise to size-dependent material properties. While it is crucial to understand how confinement affects polymers for advanced applications in nanotechnology, unambiguously identifying these size-dependent properties is challenging. Scanning probe techniques in particular are useful for measuring properties at the nanoscale, but converting measurements into material properties is highly nontrivial. Here, we discuss our recent progress developing a paradigm for unambiguously measuring material properties of nanoscale polymers by combining experimental measurements with finite element modeling (FEM). In particular, through an extensive series of FEM, we found that the apparent modulus of a thin polymer film is increased by a fraction that only depends on the Poisson’s ratio of the polymer and the ratio of the film thickness to the probe-sample contact radius. Using this correction factor, we reconcile an apparent disagreement between nanoindentation and bulk techniques and report a consistent softening of glassy polymers films at thicknesses less than 100 nm.[1] This validation in hand, we turn to elastomers, which are more complex due to the fact that adhesion plays a significant role in nanomechanical measurements. Furthermore, elastomers have been reported to stiffen by more than 100 fold with size effects beginning at thicknesses substantially thicker than a micrometer. By leveraging the same FEM-based correction factor, we find that indeed elastomers become stiffer when confined to thin films, but only at thicknesses of a few hundred nanometers and the increase in moduli is substantially more modest. We find this stiffening to be consistent with a purely entropic model in which additional crosslinks at interface result in the modulus depending on depth.[2] These results provide new basic information about polymers at the nanoscale as well as the technological tools needed to study how the properties of these polymers are affected by confinement. While these results provide new insight, the available compositions and processing conditions of polymers are far too numerous to study via this process, so new methods are needed to explore the vast landscape of polymer nanomechanics. We hypothesize that automated researchers guided by machine learning can provide a new experimental paradigm and discuss our recent work using these concepts to provide insight into polymer nanomechanics. [1] L. Li, L. M. Encarnacao, K. A. Brown, "Polymer nanomechanics: Separating the size effect from the substrate effect in nanoindentation" Appl. Phys. Lett. 2017, 110, 043105. [2] L. Li, N. Alsharif, K. A. Brown, "Confinement-Induced Stiffening of Elastomer Thin Films" J. Phys. Chem. B 2018, 122, 10767.