Atomically Resolved Transition Pathways of Iron Redox.

The redox transition between iron and its oxides is of the utmost importance in heterogeneous catalysis, biological metabolism, and geological evolution. The structural characteristics of this reaction may vary based on surrounding environmental conditions, giving rise to diverse physical scenarios. In this study, we explore the atomic-scale transformation of nanosized Fe3O4 under ambient-pressure H2 gas using in-situ environmental transmission electron microscopy. Our results reveal that the internal solid-state reactions dominated by iron diffusion are coupled with the surface reactions involving gaseous O or H species. During reduction, we observe two competitive reduction pathways, namely Fe3O4 → FeO → Fe and Fe3O4 → Fe. An intermediate phase with vacancy ordering is observed during the disproportionation reaction of Fe2+ → Fe0 + Fe3+, which potentially alleviates stress and facilitates ion migration. As the temperature decreases, an oxidation process occurs in the presence of environmental H2O and trace amounts of O2. A direct oxidation of Fe to Fe3O4 occurs in the absence of the FeO phase, likely corresponding to a change in the water vapor content in the atmosphere. This work elucidates a full dynamical scenario of iron redox under realistic conditions, which is critical for unraveling the intricate mechanisms governing the solid-solid and solid-gas reactions.

Automatic center identification of electron diffraction with multi-scale transformer networks.

Selected area electron diffraction (SAED) is a widely used technique for characterizing the structure and measuring lattice parameters of materials. An autonomous analytic method has become an urgent demand for the large-scale SAED data produced from in-situ experiments. In this work, we realize the automatic processing for center identification with a proposed deep segmentation model named the multi-scale Transformer (MS-Trans) network. This algorithm enables robust segmentation of the central spots by combining a novel gated axial-attention module and multi-scale feature fusion. The proposed MS-Trans model shows high precision and robustness, enabling autonomous processing of SAED patterns without any prior knowledge. The application on in-situ SAED data of the oxidation process of FeNi alloy demonstrates its capability of implementing autonomous quantitative processing. © 2017 Elsevier Inc. All rights reserved.