Tuning thermal transport in Si nanowires by isotope engineering

Literature Information

Publication Date 2016-08-30
DOI 10.1039/C6CP04581B
Impact Factor 3.676
Authors

Miquel Royo, Riccardo Rurali


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Abstract

We study thermal transport in isotopically disordered Si nanowires, discussing the feasibility of phonon engineering for thermoelectric applications within these systems. To this purpose, we carry out atomistic molecular dynamics and nonequilibrium Green's function calculations to characterize the dependence of the thermal conductance as a function of the isotope concentration, isotope radial distribution and temperature. We show that a reduction of the conductivity of up to 20% can be achieved with suitable isotope blends at room temperature and approximately 50% at low temperature. Interestingly, precise control of the isotope composition or radial distribution is not needed. An isotope disordered nanowire roughly behaves like a low-pass filter, as isotope impurities are transparent for long wave-length acoustic phonons, while only mid- and high-frequency optical phonons undergo significant scattering.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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