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.

Environmental scanning electron microscopy (ESEM) (1) is an imaging technique which allows hydrated, insulating samples to be imaged under an electron beam. The resolution afforded by this technique is higher than conventional optical microscopy but lower than conventional scanning electron microscopy (CSEM). The major advantage of the technique is the minimal sample preparation needed, making ESEM quick to use and the images less susceptible to the artifacts that the extensive sample preparation usually required for CSEM may introduce. Careful manipulation of both the humidity in the microscope chamber and the beam energy are nevertheless essential to prevent dehydration and beam damage artifacts. In some circumstances it is possible to image live cells in the ESEM (2).In the following sections we introduce the fundamental principles of ESEM imaging before presenting imaging protocols for plant epidermis, mammalian cells, and bacteria. In the first two cases samples are imaged using the secondary electron (topographic) signal, whereas a transmission technique is employed to image bacteria.

Electron tomographic reconstructions often contain artefacts from sources such as noise in the projections and a "missing wedge" of projection angles which can hamper quantitative analysis. We present a machine-learning approach using freely available software for analysing imperfect reconstructions to be used in place of the more traditional thresholding based on grey-level technique and show that a properly trained image classifier can achieve manual levels of accuracy even on heavily artefacted data, though if multiple reconstructions are being processed, a separate classifier will need to be trained on each reconstruction for maximum accuracy.

Wet scanning-transmission electron microscopy (STEM) is a technique that allows high-resolution transmission imaging of biological samples in a hydrated state, with minimal sample preparation. However, it has barely been used for the study of bacterial cells. In this study, we present an analysis of the advantages and disadvantages of wet STEM compared with standard transmission electron microscopy (TEM). To investigate the potential applications of wet STEM, we studied the growth of polyhydroxyalkanoate and triacylglycerol carbon storage inclusions. These were easily visible inside cells, even in the early stages of accumulation. Although TEM produces higher resolution images, wet STEM is useful when preservation of the sample is important or when studying the relative sizes of different features, since samples do not need to be sectioned. Furthermore, under carefully selected conditions, it may be possible to maintain cell viability, enabling new types of experiments to be carried out. To our knowledge, internal features of bacterial cells have not been imaged previously by this technique.

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