In this work, we report the influence of single-ionized oxygen vacancies ($$V^{\prime}_{{\text{O}}}$$ V O ′ ) as a spin ½ system in the ferromagnetic response of undoped and Cr-doped SnO2 nanowires. For this study, undoped and Cr-doped SnO2 nanowires were synthesized by a thermal evaporation method. Raman, Auger, and X-ray photoelectron spectroscopies confirmed the incorporation of Cr3+ ions in the SnO2 lattice. Electron paramagnetic resonance measurements demonstrated the presence of single-ionized oxygen vacancies ($$V^{\prime}_{{\text{O}}}$$ V O ′ ) in undoped and Cr-doped nanowires. Complementarily, cathodoluminescence measurements confirmed the presence of VO defects in the samples. Magnetic measurements revealed FM behavior from the undoped SnO2 and Cr-doped SnO2 nanowires, showing magnetization saturation values (MS) of ± 1 × 10–3 and ± 1.6 × 10–3 emu/g, respectively, and magnetic coercivity values (HC) of 180 and 200 Oe. We assign the FM response of nanowires to the presence of single ionized $$V^{\prime}_{{\text{O}}}$$ V O ′ acting as a spin ½ system and to the alignment of magnetic moments of Cr3+ ions, finding that $$V^{\prime}_{{\text{O}}}$$ V O ′ defects dominate in the FM generation.

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If there were any rules about teaching physics, “Don’t assign problems to the students that you, yourself, cannot solve” would probably top the list. And yet this bias is an unfortunate one: a closer examination of some of these problems can lead to new and valuable understandings.

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning techniques, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data modalities and domains of application. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool has been tested on both synthetic and real datasets, and its code is publicly available on https://github.com/vrodriguezf/deepvats

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