Amorphous silicon is synthesized by treating the tetrahalosilanes SiX4 (X=Cl, F) with molten sodium in high boiling polar and non-polar solvents such as diglyme or nonane to give a brown or a black solid showing different reactivities towards suitable reagents. With regards to their technical relevance, their stability towards oxygen, air, moisture, chlorine-containing reaction partners RCl (R=H, Cl, Me) and alcohols is investigated. In particular, reactions with methanol are a versatile tool to deliver important products. Besides tetramethoxysilane formation, methanolysis of silicon releases hydrogen gas under ambient conditions and is thus suitable for a decentralized hydrogen production; competitive insertion into the MeO-H versus the Me-OH bond either yields H- and/or methyl-substituted methoxy functional silanes. Moreover, compounds, such as Men Si(OMe)4-n (n=0-3) are simply accessible in more than 75 % yield from thermolysis of, for example, tetramethoxysilane over molten sodium. Based on our systematic investigations we identified reaction conditions to produce the methoxysilanes Men Si(OMe)4-n in excellent (n=0:100 %) to acceptable yields (n=1:51 %; n=2:27 %); the yield of HSi(OMe)3 is about 85 %. Thus, the methoxysilanes formed might possibly open the door for future routes to silicon-based products.
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http://dx.doi.org/10.1002/chem.201404966 | DOI Listing |
ACS Phys Chem Au
January 2025
Department of Chemistry, McGill University, Montréal, Québec H3A 0B8, Canada.
Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.
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Department of Pharmaceutical Sciences, Faculty of Pharmacy, Chiang Mai University, Chiang Mai 50200, Thailand; Research Center for Development of Local Lanna Rice and Rice Products, Chiang Mai University, Chiang Mai 50200, Thailand. Electronic address:
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Anhui Huasun Energy Company, Limited, Xuancheng 242000, China.
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State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China.
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