Ashivni Shekhawat
Research Subjects
Ashivni Shekhawat has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Colin Ophus
Stanford University
2 shared publications - Alex Zettl
Department of Physics, University of California, Berkeley, CA, USA
1 shared publication Haider Rasool
Department of Physics, University of California at Berkeley, Berkeley, California 94720, USA and Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
1 shared publicationRobert O. Ritchie
Department of Materials Science and Engineering
1 shared publication- Robert O. Ritchie
Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, USA
1 shared publication
Latest Publications
- 29 April 2016
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 6 (50) , 44489-44497
A generalized Read–Shockley model and large scale simulations for the energy and structure of graphene grain boundaries
The grain boundary (GB) energy is a quantity of fundamental importance for understanding several key properties of graphene.
- 29 April 2016
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 6 (50) , 44489-44497
A generalized Read–Shockley model and large scale simulations for the energy and structure of graphene grain boundaries
The grain boundary (GB) energy is a quantity of fundamental importance for understanding several key properties of graphene.
- Open Access
- 28 January 2016
- Published by Springer Nature in Nature Communications
- Vol. 7 (1) , 10546
Toughness and strength of nanocrystalline graphene
Pristine monocrystalline graphene is claimed to be the strongest material known with remarkable mechanical and electrical properties. However, graphene made with scalable fabrication techniques is polycrystalline and contains inherent nanoscale line and point defects—grain boundaries and grain-boundary triple junctions—that lead to significant statistical fluctuations in toughness and strength. These fluctuations become particularly pronounced for nanocrystalline graphene where the density of defects is high. Here we use large-scale simulation and continuum modelling to show that the statistical variation in toughness and strength can be understood with ‘weakest-link’ statistics. We develop the first statistical theory of toughness in polycrystalline graphene, and elucidate the nanoscale origins of the grain-size dependence of its strength and toughness. Our results should lead to more reliable graphene device design, and provide a framework to interpret experimental results in a broad class of two-dimensional materials.
- 28 January 2016
- Published by Springer Nature in Nature Communications
- Vol. 7 (1) , 10546
Open AccessToughness and strength of nanocrystalline graphene
Pristine monocrystalline graphene is claimed to be the strongest material known with remarkable mechanical and electrical properties. However, graphene made with scalable fabrication techniques is polycrystalline and contains inherent nanoscale line and point defects—grain boundaries and grain-boundary triple junctions—that lead to significant statistical fluctuations in toughness and strength. These fluctuations become particularly pronounced for nanocrystalline graphene where the density of defects is high. Here we use large-scale simulation and continuum modelling to show that the statistical variation in toughness and strength can be understood with ‘weakest-link’ statistics. We develop the first statistical theory of toughness in polycrystalline graphene, and elucidate the nanoscale origins of the grain-size dependence of its strength and toughness. Our results should lead to more reliable graphene device design, and provide a framework to interpret experimental results in a broad class of two-dimensional materials.
- Open Access
- 3 November 2015
- Published by American Physical Society (APS) in Physical Review B
- Vol. 92 (20) , 205402
Large-scale experimental and theoretical study of graphene grain boundary structures
We have characterized the structure of 176 different single-layer graphene grain boundaries grown with chemical vapor deposition using experimental high-resolution transmission electron microscopy images using a semiautomated structure processing routine. We introduce an algorithm for generating grain boundary structures for a class of hexagonal two-dimensional materials and use this algorithm and molecular dynamics to simulate the structure of linear graphene grain boundaries covering 4122 unique orientations distributed over the entire parameter space. The dislocation content and structural properties are extracted from all experimental and simulated boundaries, and various trends are explored. We find excellent agreement between the simulated and experimentally observed grain boundaries. Our analysis demonstrates the power of a statistically significant number of measurements as opposed to a small number of observations in atomic science.- 3 November 2015
- Published by American Physical Society (APS) in Physical Review B
- Vol. 92 (20) , 205402
Open AccessLarge-scale experimental and theoretical study of graphene grain boundary structures
We have characterized the structure of 176 different single-layer graphene grain boundaries grown with chemical vapor deposition using experimental high-resolution transmission electron microscopy images using a semiautomated structure processing routine. We introduce an algorithm for generating grain boundary structures for a class of hexagonal two-dimensional materials and use this algorithm and molecular dynamics to simulate the structure of linear graphene grain boundaries covering 4122 unique orientations distributed over the entire parameter space. The dislocation content and structural properties are extracted from all experimental and simulated boundaries, and various trends are explored. We find excellent agreement between the simulated and experimentally observed grain boundaries. Our analysis demonstrates the power of a statistically significant number of measurements as opposed to a small number of observations in atomic science.