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Columnar Niobium Oxide Nanostructures: Mechanism of Formation, Microstructure, and Electrophysical Properties of formation and growth is proposed and the phase composition and electrophysical properties of columnar

Electrophysical properties of transition metals chalcogenides structures used as structural elements of the nanoelectronics devicesElectrophysical properties of transition metals chalcogenides structures used as structural

Technological approaches for modified multilayer coatings formation based on nanostructured porous anodic Al2O3 and deposited dielectric films with closed porosity have been obtained. The improvement of thermophysical and electrophysical properties

The electrophysical properties of polytolans and polydiphenylbutadiyne and the relationship between these and supermolecular structureThe electrophysical properties of polytolans and polydiphenylbutadiyne and the relationship between

Electro-Physical Properties of Niobia Columnlike Nanostructures via the Anodizing of Al/Nb Layers, and chemical etching of sputter-deposited Al/Nb metal layers. The morphological properties of synthesized

Influence of interparticle correlations on electrophysical properties of solid electrolyteInfluence of interparticle correlations on electrophysical properties of solid electrolyte

Electrophysical properties of composite films based on graphene nanoplatelets covered by metal nanoparticlesElectrophysical properties of composite films based on graphene nanoplatelets covered by metal

Changes in the electro-physical properties of MCT epitaxial films affected by a plasma volume discharge induced by an avalanche beam in atmospheric-pressure air leads to changes in the electrophysical properties of MCT epitaxial films due to formation of a near

Influence of fractional structure of the granulated nanofibrous carbon filler on electrophysical properties of epoxy composites properties of epoxy resin composites has been investigated in this article. The conductivity and permittivity

Мachine learning methods for predict electrophysical properties of semiconductor materials for optoelectronic and energy storage devicesThere were several notable attempts at utilizing Machine Learning to predict physical properties

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