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Ganesh Kumar Nayak

Publications and source records attributed to Ganesh Kumar Nayak.

3 recordsLinked to original sources

Impact of O concentration on the thermal stability and decomposition mechanism of (Cr,Al)N compared to (Ti,Al)N thin films

The composition-dependent thermal stability of (Cr$_{0.47 \mp 0.03}$Al$_{0.53 \mp 0.03}$)$_{z}$(O$_{y}$N$_{1-y}$)$_{1-z}$ thin films with O concentrations of y = 0, 0.15, and 0.40 is investigated up to 1200 °C and then compared to (Ti$_{0.56}$Al$_{0.44}$)$_{z}$(O$_{y}$N$_{1-y}$)$_{1-z}$. X-ray diffraction reveals a thermal stability limit of 1150 °C independent of the O concentration, as witnessed by the formation of decomposition products, namely h-Cr$_{2}$N for (Cr$_{0.50}$Al$_{0.50}$)$_{0.49}$N$_{0.51}$ and c-Cr for both (Cr$_{0.48}$Al$_{0.52}$)$_{0.48}$(O$_{0.15}$N$_{0.85}$)$_{0.52}$ and (Cr$_{0.44}$Al$_{0.56}$)$_{0.46}$(O$_{0.40}$N$_{0.60}$)$_{0.54}$. Based on TEM and ERDA data, the thermal stability limit is extended to 1100 - 1150 °C. DFT calculations indicate that bond breaking limits the thermal stability. In (Cr,Al)N, N has the lowest activation energy for migration. Furthermore, the O vacancy formation energy is highest in (Cr,Al)(O,N). It has to be overcome to enable diffusion on the non-metal sublattice, which is necessary for forming decomposition products like w-AlN or c-Cr. However, once Cr-N bonds break, decomposition into h-Cr$_{2}$N and subsequent c-Cr together with N$_{2}$ is triggered. This results in N evaporation, generating sufficient non-metal vacancies that greatly enhance diffusion and render the extensive vacancy formation energies for non-metals irrelevant. This reduction of the activation energy for mass transport on the non-metal sublattice to the migration barrier causes the similar thermal stability in (Cr$_{0.47 \mp 0.03}$Al$_{0.53 \mp 0.03}$)$_{z}$(O$_{y}$N$_{1-y}$)$_{1-z}$. In contrast, Al bonds break first without creating non-metal vacancies in (Ti,Al)(O,N). Thus, the high O vacancy formation energy in (Ti,Al)(O,N) significantly increases the thermal stability compared to (Ti,Al)N as well as the here investigated films.

cond-mat.mtrl-sci↗

Vacancy-concentration-dependent thermal stability of fcc-(Ti,Al)Nx predicted via chemical-environment-sensitive diffusion activation energies

Thermal decomposition of metastable fcc-(Ti,Al)Nx limits the lifetime of coated components. While energetic decomposition aspects can be modelled reliably, the inherent variability of chemical environment-dependent diffusion activation energies remains systematically unexplored. Here, we predict an activation energy range (envelope) for mass transport in varying chemical environments, reflecting the vacancy concentration range fcc-(Ti0.5Al0.5)1-xNx with x = 0.47, 0.5, 0.53. The stoichiometric compound shows maximum thermal stability, consistent with experimental data. Metal vacancies decrease the average migration energy, while metal and nitrogen vacancies reduce barriers via lattice strain relaxation, enhancing mobility. The strong chemical environment dependence challenges conclusions from single-point activation energy data.

cond-mat.mtrl-sci↗

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride

Amorphous silicon nitride (a-SiN) is a material which has found wide application due to its excellent mechanical and electrical properties. Despite the significant effort devoted in understanding how the microscopic structure influences the material performance, many aspects still remain elusive. If on the one hand \textit{ab initio} calculations respresent the technique of election to study such a system, they present severe limitations in terms of the size of the system that can be simulated. Such an aspect plays a determinant role, particularly when amorphous structure are to be investigated, as often results depend dramatically on the size of the system. Here, we overcome this limitation by training a machine-learning (ML) interatomic model to \textit{ab initio} data. We show that molecular dynamics simulations using the ML model on much larger systems can reproduce experimental measurements of elastic properties, including elastic isotropy. Our study demonstrates the broader impact of machine-learning potentials for predicting structural and mechanical properties, even for complex amorphous structures.

cond-mat.mtrl-sci↗