A revised model of silicon oxidation during the dissolution of silicon in HF/HNO3 mixtures
文献信息
Anja Rietig, Thomas Langner, Jörg Acker
The stoichiometry of wet chemical etching of silicon in concentrated HF/HNO3 mixtures was investigated. The formation of nitrogen species enriched in the etching mixture and their reactivity during the etching process was studied. The main focus of the investigations was the comprehensive quantification of the gaseous reaction products using mass spectrometry. Whereas previously it could only be speculated that nitrogen was a product, its formation was detected for the first time. The formation of hydrogen, N2, N2O and NH4+ showed a dependence on the etching bath volume used, which indicates the formation of nitrogen compounds by side reactions. Simultaneously, the ratio of the nitrogen oxides, NO and NO2, formed decreases with increasing etching bath volume, while nitric acid consumption increases, so that the formation of NO2 could also be identified as a side reaction. Based on the stoichiometries obtained, a new reaction scheme for the reduction of nitric acid during etching in HF/HNO3 mixtures and an electron balance for the oxidation of silicon is presented.
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Physical Chemistry Chemical Physics

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.














