Data-Driven Investigation of Monosilane and Ammonia Co-Pyrolysis to Silicon-Nitride-Based Ceramic Nanomaterials.
Yeseul ChoiThomas J PrestonAndrew J AdamczykPublished in: Chemphyschem : a European journal of chemical physics and physical chemistry (2020)
With its high strength, high thermal stability, low density, and high electrical resistance, silicon-nitride-based ceramics have been widely used as gate insulating layers, oxidation masks, and passivation layers. Employing SiN nanomaterials in anode applications also improves rate performances and cycling stability of the lithium-ion batteries. However, a fundamental understanding of the SiN synthetic process remains elusive. SiN gas-phase synthesis can be tailored with a comprehensive understanding of the underlying thermodynamics. In comparison to the characterization data available for solid-state SiN materials, high-level theoretical studies on gas-phase materials possessing Si-N bonds and comprehensive investigation of the SiN chemistry, particularly for nanoclusters, are very uncommon. Thus, we performed a theoretical study of Si and SiN alloy acyclic hydrides and polycyclic clusters to predict electronic structures and thermochemistry using quantum chemical calculation and statistical thermodynamics. Electronic properties by way of highest and lowest occupied molecular orbital energy gap and natural bonding orbitals analysis were calculated to explore the influence of elemental composition and geometry on the stability. Our studies provide characteristic data of SiN species for a data-driven approach to map the design space for discovery of novel silicon-nitride-based ceramic materials for advanced electronic and coating applications.
Keyphrases
- solid state
- quantum dots
- reduced graphene oxide
- electronic health record
- room temperature
- big data
- high throughput
- sensitive detection
- high resolution
- machine learning
- solar cells
- density functional theory
- monte carlo
- energy transfer
- smoking cessation
- single cell
- sewage sludge
- genetic diversity
- ionic liquid
- deep learning
- anaerobic digestion