Vacuum Evaporation.

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Исследование закономерностей развития структурно-химической неоднородности в разнородном сварном соединении трубопроводного переходника

Данная работа посвящена исследованию структурно-химической неоднородности (СХН) трубопроводного переходника из разнородных сталей, выполненного аргонодуговой сваркой (АрДС). При помощи оптического микроскопа получены фотографии СХН и ее размеры. СХН выражается в появлении прослоек (карбидной и ферритной) с разным химическим составом. Приведены графики роста ширин прослоек от времени выдержки и температуры.This paper is devoted to the comparison of the structural and chemical inhomogeneity (SCI) of adapters made of dissimilar steels, made by tungsten insert gas (TIG). By means of optical microscope, photographs of the SCI and its dimensions were obtained. SCI is expressed in the appearance of interlayers (carbide and ferritic) with different chemical composition. Graphs of the growth of the widths of the interlayers from the time of exposure and temperature are given

Size Dependence of a Temperature-Induced Solid–Solid Phase Transition in Copper(I) Sulfide

Determination of the phase diagrams for the nanocrystalline forms of materials is crucial for our understanding of nanostructures and the design of functional materials using nanoscale building blocks. The ability to study such transformations in nanomaterials with controlled shape offers further insight into transition mechanisms and the influence of particular facets. Here we present an investigation of the size-dependent, temperature-induced solid-solid phase transition in copper sulfide nanorods from low- to high-chalcocite. We find the transition temperature to be substantially reduced, with the high chalcocite phase appearing in the smallest nanocrystals at temperatures so low that they are typical of photovoltaic operation. Size dependence in phase trans- formations suggests the possibility of accessing morphologies that are not found in bulk solids at ambient conditions. These other- wise-inaccessible crystal phases could enable higher-performing materials in a range of applications, including sensing, switching, lighting, and photovoltaics

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics