Data-driven semiconducting nanomaterials design for energy applications

Abstract

Silicon nitrides and hydrogenated silicon nitrides attract widespread scientific interest across multiple application fields due to their superior combination of optical, mechanical, thermal and optoelectronic properties. The wide range of possible applications of silicon nitrides are structural, cutting tools, passivation layers in solar cells, permeation barriers and encapsulation layers in light-emitting device (LED). The wide bandgap (~5.2 eV) of thin films allows for its optoelectronic application, while the silicon nitrides could act as a host matrix for silicon nano-inclusions (Si-ni) for solar cell devices and lithium ion battery anodes. In order to produce silicon-based nanoparticles1, 3-6, there have been various methods such as pyrolysis7, chemical vapor deposition8-12, atomic layer deposition13 and sputtering. Among those methods, a basal protocol to create silicon-alloyed nanoparticles and understand the synthetic mechanism is pyrolysis. Recent comprehensive reviews of silicon nitrides in both monolithic and thin films mostly focus on the current film deposition techniques, silicon nitrides’ physical, electronic, optoelectronic properties, and their applications. Synthesizing nanomaterial thorough pyrolysis is of specific interest regarding simplicity, flexibility, and scalability. Because any mixtures of precursor gases can be built into multi-functional nanoparticles that can be directly used for specific applications instead of focusing on modification of nanostructures after they have been formed. Co-pyrolysis of SiH4 and NH3 is one protocol to create polycrystalline or amorphous silicon nitrides nanoparticles in the gas phase or controlled growth of silicon wafers at a gas-solid interface to form semiconductor-grade materials through chemical vapor deposition (CVD) methods. Polymerization of silicon-alloyed in the gas phase causes deposits on a growing semiconductor surface forming point defects. A detailed understanding of the microkinetics for the gas-phase formation of silicon-based nanoparticles will allow for the improvement of applications in which silicon nanoparticles are desired or side products. Unfortunately, the fundamental explanation of the synthesis is still vague. In order to understand the fundamental of a reaction system, the first step is considered to understand the properties of materials, which can be used as reactants, intermediate structures, and products. The second step is understanding what kind of reaction occurs in the system. The third step is to be able to present an integrated reaction mechanism in the basic condition. In the final step, we are asked to predict all the possible reactions in a specific reaction condition. While a limited number of computational studies of silicon nitride nanoparticle formation have been carried out to address these concerns at the elementary step level, augmentation of these models to address multifunctionality, more accurate treatment of kinetics, and the complex, polycyclic nature of silicon nitrides is warranted. Using quantum chemical calculations, statistical thermodynamics, conventional and variational transition state theory, accurate rate coefficients were calculated for over 130 reactions involving 1,2-hydrogen shift, H2 addition-elimination, substituted silylene addition-elimination, and cyclization-ring opening. Silane and Ammonia co-pyrolysis has been employed for synthesizing, yet rate coefficients of cannot be measured directly for all possible reactions of silicon nitrides of relevant sizes and substituents. Thus, silicon nitrides containing up to 6 silicon atoms, a variety of acyclic and cyclic substituents about the reactive center, and polycyclic nature were explored. The Evans-Polanyi correlation was revised for multifunctional kinetics, and representative pre-exponential factors were calculated. Additionally, thermochemical properties for 113 silicon-alloyed clusters containing up to 6 heavy atoms (Si and Ge, Si and N) were calculated to analyze the polycyclic and multifunctional nature of complex species. This research serves the understanding of silicon nitrides nanoparticle formation at the molecular level and provides the practical value of the kinetic correlations governing silicon nitrides nanoparticle formation to engineers designing new nanomaterials and reactor systems for semiconductors or tailored nanoparticles.