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Silicon nitride films are widely used as the charge storage layer of charge trap flash (CTF) devices due to their high charge trap densities. The nature of the charge trapping sites in these materials responsible for the memory effect in CTF devices is still unclear. Most prominently, the Si dangling bond or K-center has been identified as an amphoteric trap center. Nevertheless, experiments have shown that these dangling bonds only make up a small portion of the total density of electrical active defects, motivating the search for other charge trapping sites. Here, we use a machine-learned force field to create model structures of amorphous Si3N4 by simulating a melt-and-quench procedure with a molecular dynamics algorithm. Subsequently, we employ density functional theory in conjunction with a hybrid functional to investigate the structural properties and electronic states of our model structures. We show that electrons and holes can localize near over- and under-coordinated atoms, thereby introducing defect states in the band gap after structural relaxation. We analyze these trapping sites within a nonradiative multi-phonon model by calculating relaxation energies and thermodynamic charge transition levels. The resulting defect parameters are used to model the potential energy curves of the defect systems in different charge states and to extract the classical energy barrier for charge transfer. The high energy barriers for charge emission compared to the vanishing barriers for charge capture at the defect sites show that intrinsic electron traps can contribute to the memory effect in charge trap flash devices.

The elemental composition has been extensively used to characterize wine and to find correlations with environmental and winemaking factors. Although X-ray fluorescence (XRF) techniques offer many advantages, they have been rarely used for wine analysis. Here, we show the comparison of wine elemental composition results obtained by total reflection X-ray fluorescence (TXRF) and energy dispersive X-ray fluorescence (EDXRF) for elements K, Ca, Mn, Fe, Cu, Zn, Br, Rb, and Sr. The results obtained by TXRF and EDXRF have been additionally verified by inductively coupled plasma–mass spectrometry. The important analytical features of XRF techniques in wine science have been described, the preservation of volatile elements (e.g., Br) being one of their main advantages. In addition, we have shown that XRF techniques offer an optimal analytical approach for building large data sets containing highly reliable and reproducible results of elemental abundances in wines, corresponding soils, and grape juice. Such data sets are especially important for the geographic authentication of wine. This has been shown for 37 Austrian and Croatian wines collected together with respective soils from selected wine regions. The element abundances in soil reflect in a large portion in grape juice and finished wine suggesting that the contribution of the soil, that is, the plant uptake capacity expressed as c i(wine)/c i(soil) concentration factors, can be a highly discriminating factor for wine fingerprinting. This indeed has been proved in the present study in comparison to discrimination based only on wine element abundances. We have identified Fe, Zn, Br, Rb, and Sr as the best discriminator elements for the geographical authentication of wine. The study opens a new perspective in extending the application of XRF techniques as a cost-effective analytical tool for creating large databases of soil, grape juice, and wine element abundances for the evaluation of soil characteristics and other environmental parameters on wine composition.

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