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Giant thermal conductivity and strain thermal response of nitrogen substituted diamane: a machine-learning-based prediction.

Biao WangZhenqiao HuangXingchun XuSaifei FanKunlong ZhaoJiaqi Zhu
Published in: Nanoscale (2024)
With the ongoing trend of seeking miniaturization and enhanced performance for electronic devices, effective thermal management has emerged as a critical concern. The discovery and investigation of high thermal conductivity ( κ ) materials have proved to be pivotal in addressing this challenge. This study aims to explore the distinctive properties and potential applications of nitrogen substituted diamane (NCCN), a two-dimensional material with a diamond-like structure composed of carbon and nitrogen atoms. This work systematically delves into NCCN's thermal, mechanical, and electrical properties. It is predicted that NCCN exhibits an exceptional κ , ∼2288 W m -1 K -1 , at room temperature (300 K) by combining the machine-learning interatomic potential method and the phonon Boltzmann transport equation, surpassing that of H-diamane and rivaling that of diamond, and an impressive electronic band gap of ∼4.47 eV (PBE). For mechanical properties, the stress-strain relationship reveals that NCCN exhibits isotropic elastic properties and anisotropic tensile strengths. Additionally, the variations in NCCN's κ and electronic energy band structure under different strains underscore its substantial potential in the field of thermoelectric applications.
Keyphrases
  • machine learning
  • room temperature
  • molecular docking
  • small molecule
  • artificial intelligence
  • escherichia coli
  • high throughput
  • single cell
  • heat stress