Electron tomography provides a direct link between the Payne effect and the inter-particle spacing of rubber composites

Abstract

Rubber-filler composites are a key component in the manufacture of tyres. The filler provides mechanical reinforcement and additional wear resistance to the rubber, but it in turn introduces non-linear mechanical behaviour to the material which most likely arises from interactions between the filler particles, mediated by the rubber matrix. While various studies have been made on the bulk mechanical properties and of the filler network structure (both imaging and by simulations), there presently does not exist any work directly linking filler particle spacing and mechanical properties. Here we show that using STEM tomography, aided by a machine learning image analysis procedure, to measure silica particle spacings provides a direct link between the inter-particle spacing and the reduction in shear modulus as a function of strain (the Payne effect), measured using dynamic mechanical analysis. Simulations of filler network formation using attractive, repulsive and non-interacting potentials were processed using the same method and compared with the experimental data, with the net result being that an attractive inter-particle potential is the most accurate way of modelling styrene-butadiene rubber-silica composite formation.

Frozen non-equilibrium structure for anisotropically deformed natural rubber with nanomatrix structure observed by 3D FIB-SEM and electron tomography

“…In the previous works, various heterogeneous structures have been observed by FIB-SEM and electron tomography: that is, island-sea, cylinder, gyroid and lamellar structures for block and graft copolymers [1,2] as well as dispersion of primary nanoparticles, structure of secondary aggregates, and 3D filler-network structure for filled polymers [1,2,[6][7][8]. Some important findings were achieved by 3D observations through FIB-SEM and electron tomography.…”

Towards the understanding of the gold interaction with AIII-BV semiconductors at the atomic level

“…The Random Forest method was used as implemented in Trainable Weka Segmentation 37 to statistically distinguish between these phases, as successfully used in HAADF STEM Tomography 72 . Here, we…”

Machine learning as a tool for classifying electron tomographic reconstructions

“…The sample used here is a composite of silica nanoparticle aggregates inside a rubber matrix, the bulk sample being 25 % silica by volume (investigated previously [7] by the authors using the technique demonstrated in this work). Figure 6 shows one slice from the experimental data.…”

“…A 150-nm diameter, 2-μm long pillar of rubber-silica composite [7] was fabricated using a focussed ion beam instrument and then imaged using an FEI Tecnai F20 at 200 kV under HAADF-STEM conditions (collection angle = 23.7-118 mrad). Images were taken from −76°t o +70°at 2°increments, resulting in a missing wedge of 34°.…”

“…Tomography is carried out when three-dimensional information is explicitly necessary-for instance, when visualising the distribution of nanoparticles inside a polymer matrix [6,7]. Quantitative 3D information (such as nearest-neighbour distances) can also be extracted from tomograms-something impossible with a twodimensional projection of an irregular structure such as a zeolite or mesoporous catalyst [8][9][10].…”