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Interfacial negative charges in PECVD-SiO2/Si films: Correlating Si 2p XPS with electrical properties to construct a physically consistent model
No full textThe correlation between electrical properties and atomic features was investigated in tetraethyl orthosilicate (TEOS)-based plasma-enhanced chemical vapor deposition SiO films on Si(001) using capacitance–voltage analysis and x-ray photoelectron spectroscopy within a datadriven physics framework. The study aimed to clarify the microscopic origins of the electrical property variations. The Si 2p binding energy in the XPS spectra exhibits thickness-dependent behavior distinct from that of thermally grown SiO and shows a clear correlation with both the interface trap density (D) and the effective positive charge (Q). Electrostatic analysis of the Si 2p energy shifts suggests negative charge accumulation near the interface in the thickness range of 4 to over 8 nm, with a positively charged layer formed on top. The amount of this negative charge can reach 1 - 2 x 10 cm, comparable to the positive Q of the order of 2 x 10 cm. These results demonstrate the presence of charge polarity switching in TEOS-based PECVD-SiO, which can be interpreted as arising from a double-layer structure consisting of a plasma-oxidation-dominant layer and a CVD-dominant layer, with the negative charges attributed to excess oxygen introduced during plasma oxidation. The thickness of the plasma-oxidized layer appears to be governed by the CVD growth rate, whereas the variations in Dit are linked to plasma oxidation conditions, such as the flux of oxygen species arriving at the interface
Colloid influence on the Radionuclide Migration from a Nuclear Waste Repository: Brief overview of on-going activities at KIT-INE following the way opened by Professor Jae-Il KIM
No full textSelf-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials
No full textHigh-throughput toxicity testing offers a fast and cost-effective way to test large amounts of compounds. A key component for such systems is the automated evaluation via machine learning models. In this paper, we address critical challenges in this domain and demonstrate how representations learned via self-supervised learning can effectively identify toxicant-induced changes. We provide a proof-of-concept that utilizes the publicly available EmbryoNet dataset, which contains ten zebrafish embryo phenotypes elicited by various chemical compounds targeting different processes in early embryonic development. Our analysis shows that the learned representations using self-supervised learning are suitable for effectively distinguishing between the modes-of-action of different compounds. Finally, we discuss the integration of machine learning models in a physical toxicity testing device in the context of the TOXBOX project
Two-loop light-quark Electroweak corrections to Higgs boson pair production in gluon fusion
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