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Rahul Rao

Researcher at Air Force Research Laboratory

Publications -  125
Citations -  4593

Rahul Rao is an academic researcher from Air Force Research Laboratory. The author has contributed to research in topics: Raman spectroscopy & Carbon nanotube. The author has an hindex of 27, co-authored 106 publications receiving 3212 citations. Previous affiliations of Rahul Rao include Clemson University & Honda.

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Defect engineering of two-dimensional transition metal dichalcogenides

TL;DR: In this article, structural defects in two-dimensional transition metal dichalcogenides (TMDs) have been studied and the authors provide a comprehensive understanding of structural defects and the pathways to generating structural defects during and after synthesis.
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Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications

TL;DR: While the primary focus of this review is on the science framework of SWCNT growth, connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene are drawn.
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Strain engineering and epitaxial stabilization of halide perovskites.

TL;DR: Strain engineering of α-formamidinium lead iodide (α-FAPbI 3) is investigated using both experimental techniques and theoretical calculations, and it is demonstrated that a compressive strain effectively changes the crystal structure, reduces the bandgap and increases the hole mobility of α -FAPBI 3.
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Emerging Applications of Elemental 2D Materials

TL;DR: A complete review of emerging applications in electronics, sensing, spintronics, plasmonics, photodetectors, ultrafast lasers, batteries, supercapacitors, and thermoelectrics is presented by application type, including detailed descriptions of how the material properties may be tailored toward each specific application.
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Autonomy in materials research: a case study in carbon nanotube growth

TL;DR: An Autonomous Research System (ARES), a robot guided by artificial intelligence in an iterative learning loop that is capable of designing, executing and analyzing its own experiments orders of magnitude faster than current research methods, is built.