The current state-of-the-art in the growth, structure, and physicochemical properties of iron nitride thin films is presented. First, different iron nitride phases are introduced based on their crystallographic structure and the Fe-N phase diagram. Second, preparation methods for thin iron nitride films are described. Next, the structure, electronic, and magnetic properties of the films are discussed. Finally, potential applications of iron nitride films, as well as the challenges to be faced in the field, are highlighted. This Review constitutes a starting point for anyone who would like to conduct research on these fascinating materials, the scientific and technological potential of which has not been fully explored to date.

Iron oxide films epitaxially grown on close-packed metal single crystal substrates exhibit nearly-perfect structural order, high catalytic activity (FeO) and room-temperature magnetism (Fe₃O₄). However, the morphology of the films, especially in the ultrathin regime, can be significantly influenced by the crystalline structure of the used support. This work reports an ultra-high vacuum (UHV) low energy electron/synchrotron light-based X-ray photoemission electron microscopy (LEEM/XPEEM) and electron diffraction (µLEED) study of the growth of FeO and Fe₃O₄ on two closed-packed metal single crystal surfaces: Pt(111) and Ru(0001). The results reveal the influence of the mutual orientation of adjacent substrate terraces on the morphology of iron oxide films epitaxially grown on top of them. On fcc Pt(111), which has the same mutual orientation of adjacent monoatomic terraces, FeO(111) grows with the same in-plane orientation on all substrate terraces. For Fe₃O₄(111), one or two orientations are observed depending on the growth conditions. On hcp Ru(0001), the adjacent terraces of which are ‘rotated’ by 180° with respect to each other, the in-plane orientation of initial FeO(111) and Fe₃O₄(111) crystallites is determined by the orientation of the substrate terrace on which they nucleated. The adaptation of three-fold symmetric iron oxides to three-fold symmetric substrate terraces leads to natural structuring of iron oxide films, i.e., the formation of patch-like magnetite layers on Pt(111) and stripe-like FeO and Fe₃O₄ structures on Ru(0001).

Single-crystal magnetic nanostructures with well-defined shapes attract lots of interest due to their potential applications in magnetic and spintronic devices. However, development of methods allowing controlling their mutual crystallographic and geometric orientation constitutes a significant scientific challenge. One of the routes for obtaining such structures is to grow the materials epitaxially on naturally-structured supports, such as vicinal surfaces of single-crystal substrates. Iron oxides are among the most well-known magnetic materials which, depending on the phase, may exhibit ferro/ferri- or antiferromagnetic ordering. We have grown iron oxide nanowires on a Cu(410) single-crystal substrate faceted with molecular oxygen. Scanning tunneling microscopy and low energy electron diffraction revealed that the oxide grows in the [111] direction, along the step edges of the substrate and rotated by ±15° with respect to the [010] direction of copper atomic terraces (so that the the growing elongated structures are orientated parallel to each other). Notably, x-ray photoelectron spectroscopy confirmed that the nanowires represent the ferrimagnetic γ-Fe₂O₃ (maghemite) iron oxide phase, while micromagnetic simulations indicated that the wires are single-domain, with the easy magnetization axis orientated in-plane and along the long axis of the wire.

It is crucial to control and comprehend the interaction between elemental adsorbates and two-dimensional materials to drive future generations of electronic, sensing, and energy applications. One such material, particularly interesting from the perspective of tunability, is silicene—the silicon-based cousin of graphene. In this work, we investigate nearly 2000 atomic adsorption models on silicene via a combination of density functional theory (DFT) and machine learning (ML). Different systems with varied adsorption geometries, element identities, and surface coverages were optimized using spin-polarized DFT, and the most stable configurations were selected based on adsorption energy. This information was used to train various ML models, including tree-based models and artificial neural networks, to predict adsorption geometry (classification) and adsorption energy (regression). The current hybrid DFT + ML approach provides a transferable framework for high-throughput screening of element-functionalized silicene and other 2D surfaces, which is of immense importance in directing surface modification strategies in electronic and catalytic device engineering.