Search

Your search keyword '"Sharma, Divya"' showing total 1,776 results
Start Over Author "Sharma, Divya"
1,776 results on '"Sharma, Divya"'

19. Size-induced Exchange Bias in Single-phase CoO Nanoparticles

Author

Sharma, Vikash, Pal, Sudip, Sharma, Divya, Shukla, Dinesh Kumar, Chaudhary, Ram Janay, and Okram, Gunadhor Singh

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We report exchange bias (EB) in single-phase CoO nanoparticles, where two magnetic phases naturally emerge as the crystallite size decreases from 34.6 to 10.8 nm. The N\'eel temperature (TN) associated with antiferromagnetic ordering decreases monotonically with the reduction in crystallite size, highlighting the significant influence of size effects. The 34.6 nm nanoparticles exhibit magnetization irreversibility between zero field cooled (ZFC) and field-cooled (FC) states below TN. This irreversibility appears well above TN with further reduction in size, resulting in the absence of true paramagnetic regime which indicates the occurrence of an additional magnetic phase. The frequency-dependent ac-susceptibility in 10.8 nm nanoparticles suggests slow dynamics of disordered surface spins above TN, coinciding with the establishment of long-range order in the core. The thermoremanent magnetization (TRM) and isothermoremanent magnetization (IRM) curves suggest a core-shell structure: the core is antiferromagnetic, and the shell consists of disordered surface spins causing ferromagnetic interaction. Hence, the exchange bias in these CoO nanoparticles results from the exchange coupling between an antiferromagnetic core and a disordered shell that exhibits unconventional surface spin characteristics., Comment: 33 Pages, 9 figures

Published
2024

22. Virtual Learning Decreases the Carbon Footprint of Medical Education.

Author

Sharma, Divya, Rizzo, Julianne, Nong, Yvonne, Murase, Lilia, Fong, Sydney, Lo, Kenny, Rosenbach, Misha, Murase, Jenny, and Sivamani, Raja

Subjects
Carbon dioxide emissions, Conference, Emissions, Greenhouse gas, In-person conferences, Travel, Virtual learning
Abstract

INTRODUCTION: The environmental impact of holding in-person academic conferences and continuing medical education (CME) programs can be significant. In-person conferences provide a unique social and professional platform to engage in networking and foster professional development; however, there is an opportunity for hybrid and virtual platforms to provide CME for broader audiences looking to improve their clinical skills and strengthen their knowledge base. This study seeks to describe the reduction in carbon emissions associated with a webinar hosted by an online dermatology-focused medical education platform. METHODS: This cross-sectional study used the location of deidentified virtual attendees of a webinar to predict the carbon emissions produced if attendees had instead traveled to the location of the most recent Integrative Dermatology Symposium (Sacramento, CA). Following collection of each virtual attendees location, the mode of transportation was predicted on the basis of each participants distance to the conference. RESULTS: The estimated carbon emissions were calculated for 576 participants. The total estimated, unadjusted carbon emissions for both attendees predicted to fly or drive was 370,100 kg CO2. The emissions produced per participant from those expected to fly to an in-person CME after adjusting for all additional passengers on every flight were 4.5 kg CO2. The emissions produced per participant from those expected to drive were 42.7 kg CO2. CONCLUSION: The use of a virtual CME webinar led to a significant reduction in travel-related carbon dioxide emissions when compared to running the same program in-person event. When accounting for all passengers traveling via plane on any flight, driving to an event produced more emissions per participant than flying.

Published
2024

35. Crystal-GFN: sampling crystals with desirable properties and constraints

Author

AI4Science, Mila, Hernandez-Garcia, Alex, Duval, Alexandre, Volokhova, Alexandra, Bengio, Yoshua, Sharma, Divya, Carrier, Pierre Luc, Benabed, Yasmine, Koziarski, Michał, and Schmidt, Victor

Subjects
Computer Science - Machine Learning
Abstract

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy., Comment: Main paper (10 pages) + references + appendix

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results