We demonstrate that the dissolution of 10 nm metal nanoparticles localized in the subsurface layer of silicate glass by static electric field can be employed to alter the optical density and surface profile of the glass-metal composite with spatial resolution of 200 nm. The developed technique, which can be referred to as electric field imprinting (EFI), offers an attractive alternative to conventional micro- and nano-pattering techniques.

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In this contribution we present a method for solving the inverse problem in electric impedance tomography with neural networks.

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In process tomography the aim is to obtain information typically from the interior of the process vessels based on the measurements made on the surface of the vessel. Electrical impedance tomography (EIT) is an imaging modality in which the internal resistivity distribution is reconstructed based on the known injected currents and measured voltages on the surface. Since the reconstructed image represents the resistivity distribution of the interior, certain internal structures such as highly conductive mixing paddles in a stirred vessel may entail difticulties in the image reconstruction. This is because EIT is a diffuse, nonlinear imaging modality which makes it difficult to reconstruct high contrasts in the internal resistivity distribution. In this paper the effects of internal conductive structures on the reconstructed images in two-dimensional cases are considered and possible improvements in the reconstructions by taking into account the locations and resistivities of these structures are studied. It is shown that in some internal conductive structure geometries the prior information of the structure may improve the reconstructions substantially. However, it is also shown that in a case in which the conductive structure encloses the region of interest by creating a low-resistivity path for current to flow, the proper reconstruction is impossible even with prior infomation of the structure.