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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Synthesis of Ultra-incompressible sp 3 -Hybridized Carbon Nitride with 1:1 Stoichiometry

Search of materials with C-N composition hold a great promise in creating materials which would rival diamond hardness due to the very strong and relatively low-ionic C-N bond. Early experimental and theoretical works on C-N compounds were based on structural similarity with binary A 3 B 4 structural types; however, the synthesis of C 3 N 4 remains elusive. Here we explored an unbiased synthesis from the elemental materials at high pressures and temperatures. Using in situ synchrotron X-ray diffraction and Raman spectroscopy we demonstrate the synthesis of highly incompressible Pnnm CN compound with sp 3 hybridized carbon above 55 GPa and 7000 K. This result is supported by first principles evolutionary search, which finds that Pnnm CN is the most stable compound above 10.9 GPa. On pressure release below 6 GPa the synthesized CN compound amorphizes reattaining its 1:1 stoichiometry as confirmed by Energy-Dispersive X-ray Spectroscopy. Here, this work underscores the importance of understanding of novel high-pressure chemistry rules and it opens a new route for synthesis of superhard materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Isotopic Heft on the $B_{1l}$ Silent Mode in Ultra-Narrow Gallium Nitride Nanowires

Wurtzite semiconductor compounds have two silent modes, $B_{1l}$ and $B_{lh}$. A silent mode is a vibrational mode that carries neither a dipole moment nor Raman polarizability. Thus, they are forbidden in both infrared reflectivity and Raman spectroscopy. Astonishingly, we detected the $B_{1l}$ mode in high-quality, ultra-narrow GaN nanowires using resonant Raman scattering, although the $B_{lh}$ was not observed, and there is no immediate explanation for this asymmetric finding. In this work, the Raman experiments were performed using several laser lines from 647 to 325 nm; the latter is a wavelength in which Raman becomes resonant. Actually, we observed the $B_{1l}$ mode only in resonance, indicating that the appearance of this mode is related to Frohlich electron-phonon interactions; i.e., a dipole moment emerging in the $B_{1l}$ silent mode may not be present in the $B_{lh}$ mode. To shed light onto the physical origin of these observations, we performed density functional theory calculations of the lattice dynamics in GaN. We performed a careful analysis of the different physical mechanisms that allow the forbidden mode to appear to explain the physics underlying the nonzero dipole moment in the $B_{1l}$ mode, and the reason why this dipole moment is not present in the $B_{1h}$ mode.

crystalline perfection↗
DOE OSTI2019

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

Abstract It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R 2 of >90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.

Dong, Yuan↗