Metastable states in high carbon C–(Fe,Ni, Co) films obtained by three-electrode ion-plasma sputtering

Abstract

The effect of ion-plasma deposition on the structure of high-carbon films (at. %) Fe–(20–84) % С, Co–(5–52) % С, Ni–(7–61) % С was investigated. The lattice periods and crystallite sizes of nonequilibrium phases in the as-deposited state and after heating are determined. The temperatures of the beginning and end of the decay of metastable phases during heating at a constant speed are established. The transition from an amorphous to an equilibrium crystalline state in Fe–C films passes through the stage of formation and subsequent decomposition of an intermediate, metastable hcp phase of variable composition. The electrical and hysteretic magnetic properties of the films were measured in the as-deposited state and after heat treatment. The compositions and conditions for producing films with low values of the temperature coefficient of electrical resistance and high coercive force are established. Thus, high-carbon films of Ni–61% C in the as-depoteted state and Fe–69% C films after heating to 900?K are characterized by small TCR values (± 10^?6 К^?1) over a wide temperature range.     

Hierarchical structure of SERS substrates possessing the silver ring morphology

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ChemInform Abstract: Explaining Stability of Transition Metal Carbides — and Why TcC Does Not Exist.

The formation of transition metal (M) carbides M_xC_y and trends of their stability are systematically investigated using the USPEX code within the DFT.     

On the Statistical Calibration of Physical Models

We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.