A model for predicting the thermal conductivity of SiO2–Ge nanoparticle composites
文献情報
Vasyl Kuryliuk, Andriy Nadtochiy, Oleg Korotchenkov, Chin-Chi Wang
We present a simple theoretical model that predicts the thermal conductivity of SiO2 layers with embedded Ge quantum dots (QDs). Overall, the resulting nanoscale architecture comprising the structural relaxation in the SiO2 matrix, deviation in mass density of the QDs compared to the surrounding matrix and local strains associated with the dots are all likely to enhance phonon scattering and thus reduce the thermal conductivity in these systems. We have found that the conductivity reduction can be predicted by the dot-induced local elastic perturbations in SiO2. Our model is able to explain not only this large reduction but also the magnitude and temperature variation of the thermal conductivity with size and density of the dots. Within the error range, the theoretical calculations of the temperature-dependent thermal conductivity in different samples are in close agreement with the experimental measurements. Including the details of the strain fields in oxidized Si nanostructured layers is therefore essential for a better prediction of the heat pathways in on-chip thermoelectric devices and circuits.
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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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