Engineering topics

Nepal, K.

Publications and source records attributed to Nepal, K..

3 records

DOE OSTI2024

Physical origin of enhanced electrical conduction in aluminum-graphene composites

In this study, the electronic and transport properties of aluminum-graphene composite materials were investigated using the ab initio plane wave density functional theory. The interfacial structure is reported for several configurations. In some cases, the face-centered aluminum (111) surface relaxes in a nearly ideal registry with graphene, resulting in a remarkably continuous interface structure. The Kubo–Greenwood formula and space-projected conductivity were employed to study electronic conduction in aluminum single- and double-layer graphene-aluminum composite models. The electronic density of states at the Fermi level is enhanced by the graphene for certain aluminum–graphene interfaces, thus improving electronic conductivity. In double-layer graphene composites, conductivity varies non-monotonically with temperature, showing an increase between 300 and 400 K at short aluminum-graphene distances, unlike the consistent decrease in single-layer composites.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Atomistic nature of amorphous graphite

This paper focuses on the structural, electronic, and vibrational features of amorphous graphite [R. Thapa et al, Phys. Rev. Lett., 2022, 128, 236402]. The structural order in amorphous graphite is discussed and compared with graphite and amorphous carbon. The electronic density of states and localization in these phases were analyzed. Spatial projection of charge densities in the π bands showed a high charge concentration on participating atoms in connecting hexagons. A vibrational density of states was computed and is potentially an experimentally testable fingerprint of the material. An analysis of the vibrational modes was carried out using the phase quotient, and the mode stretching character. The average thermal conductivity calculated for aG was 0·85 and 0·96 W/cmK at room temperature and 1000 K, respectively.

Chemistry↗

DOE OSTI2022

Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential

Multi-shell fullerenes ”buckyonions ” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 ~ 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA , revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjugate gradient energy convergence of the models was achieved with both methods.

74 ATOMIC AND MOLECULAR PHYSICS↗