Isaac G. Juma
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Isaac G. Juma's research focuses on the properties and applications of advanced materials, particularly in the realm of two-dimensional (2D) materials and their heterostructures. His work has involved the direct growth of hexagonal boron nitride (h-BN) on non-metallic substrates, a key step for integrating this material with other 2D systems like graphene. He has also investigated the behavior of phonons in transition metal dichalcogenides (TMDs), such as WS2. This investigation utilized Raman spectroscopy, a technique sensitive to lattice vibrations, and employed machine learning to analyze the complex phonon anharmonicity. Juma collaborates with Mansour Mortazavi at the University of Arkansas at Pine Bluff, with whom he has co-authored publications. His scholarly output includes a h-index of 2 and 55 citations across 2 publications.
Metrics
- h-index: 2
- Publications: 2
- Citations: 56
Selected Publications
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Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning (2023)
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Direct growth of hexagonal boron nitride on non-metallic substrates and its heterostructures with graphene (2021)
Collaboration Network
Top Collaborators
- Direct growth of hexagonal boron nitride on non-metallic substrates and its heterostructures with graphene
- Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning
- Direct growth of hexagonal boron nitride on non-metallic substrates and its heterostructures with graphene
- Direct growth of hexagonal boron nitride on non-metallic substrates and its heterostructures with graphene
- Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning
- Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning
- Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning
- Probing anharmonic phonons in WS2 van der Waals crystal by Raman spectroscopy and machine learning
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