High-resolution electron microscopy is achievable only when a high electron dose is employed, a practice that may cause damage to the specimen and, in general, affects the observation. This drawback sets some limitations on the range of applications of high-resolution electron microscopy. Our work proposes a strategy, based on machine learning, which enables a significant improvement in the quality of Scanning Transmission Electron Microscope images generated at low electron dose, strongly affected by Poisson noise. In particular, we develop an autoencoder, trained on a large database of images, which is thoroughly tested on both synthetic and actual microscopy data. The algorithm is demonstrated to drastically reduce the noise level and approach ground-truth precision over a broad range of electron beam intensities. Importantly, it does not require human data pre-processing or the explicit knowledge of the dose level employed and can run at a speed compatible with live data acquisition. Furthermore, a quantitative unbiased benchmarking protocol is proposed to compare different denoising workflows.
Toroidal magnetic configurations are widely exploited in industry and scientific research, involving a vast spectrum of applications, such as thermonuclear fusion, particle detectors, SMES systems and medical devices. To properly design and analyse these systems, it is crucial to determine the magnetic field generated by different configurations. The multipole expansion theory can be applied to the analysis of toroidal configurations, by solving the Laplace equation for the magnetic scalar potential in toroidal coordinates. Contrarily to the case of accelerator magnets with straight axis, in this case the correlation between the current distribution and the field harmonics cannot easily be identified. This paper proposes a methodology for the computation of field harmonics in toroidal coordinates, which is validated by comparison with the results obtained through the Biot-Savart law. This work was carried out in the frame of the GaToroid project ongoing at CERN.
Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise FIB-SEM nanotomography to quantitatively characterise the morphology of nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets, as well as networks of silver nanowires, is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within vertical printed heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.
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