Transmission-mode imaging in the environmental scanning electron microscope (ESEM)

Electron microscopy was first conducted in the 1930s with the advent of the TEM and later the STEM. In 1969, the first commercial SEM was released, with the possibility of retrofitting it to behave like a STEM following soon afterwards. In 1979, Danilatos and Robinson advanced electron microscopy by creating a new type of SEM which allowed a controlled quantity of gas into the sample chamber, termed ESEM. The most recent evolution in this line was the combination of ESEM and STEM in 2005, a procedure termed Wet STEM. The focus of this work is on investigating applications of this new technique, along with the contrast mechanisms involved in forming an image. To that end, a wide variety of samples will be imaged. Clay and paint suspensions (colloids) are used to test Wet STEM’s capacity to image submerged objects, as well as thin objects which are stacked together. Diblock copolymer films are used to test Wet STEM’s ability to distinguish chemically similar materials without staining, the physical effects of heavy metal staining and to demonstrate the necessity of gas for the purpose of charge neutralisation. Single cell biological samples are also investigated. Internal contrast in mammalian cells is visible without recourse to staining, but chemical fixation is required despite maintaining a high relative humidity. Bacteria are more resilient and as such are easier to image than animal cells, requiring no prior treatment. When exposed to low relative humidity, bacteria are found to collapse. The collapse pattern is observed to differ between wild-type and cytoskeletal-deficient bacteria of the same species and strain, so it is likely that dehydration-induced collapse offers information about the position and shape of the bacterial cytoskeleton.This work was funded by the EPSRC [grant number EP/P50385X/1] and by a CASE studentship from FEI Company

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

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.L.S. and P.A.M thank Michelin for funding. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 291522-3DIMAGE.This is the final published version. It first appeared at http://www.nature.com/srep/2014/141209/srep07389/full/srep07389.html

Machine learning as a tool for classifying electron tomographic reconstructions

AbstractElectron 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.</jats:p