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Transition metal spinel oxides comprised of earth-abundant Mn and Co have long been explored for their use in catalytic reactions and energy storage. However, understanding functional properties can be challenging due to differences in sample preparation and the ultimate structural properties of the materials. Epitaxial thin film synthesis provides a novel means of producing precisely controlled materials to explore the variations reported in the literature. In this work, MnxCo3−xO4 samples from x = 0 to x = 1.28 were synthesized through molecular beam epitaxy and characterized to develop a material properties map as a function of stoichiometry. Films were characterized via in situ x-ray photoelectron spectroscopy, x-ray diffraction, scanning transmission electron microscopy, and polarized K-edge x-ray absorption spectroscopy. Mn cations within this range were found to be octahedrally coordinated, in line with an inverse spinel structure. Samples largely show mixed Mn3+ and Mn4+ character with evidence of phase segregation tendencies with the increasing Mn content and increasing Mn3+ formal charge. Phase segregation may occur due to structural incompatibility between cubic and tetragonal crystal structures associated with Mn4+ and Jahn–Teller active Mn3+ octahedra, respectively. Our results help in explaining the reported differences across samples in these promising materials for renewable energy technologies. 

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

 

Abstract Germanium-based oxides such as rutile GeO 2 are garnering attention owing to their wide band gaps and the prospects of ambipolar doping for application in high-power devices. Here, we present the use of germanium tetraisopropoxide (GTIP), a metal-organic chemical precursor, as a source of germanium for the demonstration of hybrid molecular beam epitaxy for germanium-containing compounds. We use Sn 1- x Ge x O 2 and SrSn 1- x Ge x O 3 as model systems to demonstrate our synthesis method. A combination of high-resolution X-ray diffraction, scanning transmission electron microscopy, and X-ray photoelectron spectroscopy confirms the successful growth of epitaxial rutile Sn 1- x Ge x O 2 on TiO 2 (001) substrates up to x  = 0.54 and coherent perovskite SrSn 1- x Ge x O 3 on GdScO 3 (110) substrates up to x  = 0.16. Characterization and first-principles calculations corroborate that germanium occupies the tin site, as opposed to the strontium site. These findings confirm the viability of the GTIP precursor for the growth of germanium-containing oxides by hybrid molecular beam epitaxy, thus providing a promising route to high-quality perovskite germanate films. 

Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

 

Hybrid MBE produces epitaxial SrTiO 3 free-standing nanomembranes using remote epitaxy in an adsorption-controlled manner. 

4d transition metal oxides have emerged as promising materials for numerous applications including high mobility electronics. SrNbO3 is one such candidate material, serving as a good donor material in interfacial oxide systems and exhibiting high electron mobility in ultrathin films. However, its synthesis is challenging due to the metastable nature of the d1 Nb4+ cation and the limitations in the delivery of refractory Nb. To date, films have been grown primarily by pulsed laser deposition (PLD), but development of a means to grow and stabilize the material via molecular beam epitaxy (MBE) would enable studies of interfacial phenomena and multilayer structures that may be challenging by PLD. To that end, SrNbO3 thin films were grown using hybrid MBE for the first time using a tris(diethylamido)(tert-butylimido) niobium precursor for Nb and an elemental Sr source on GdScO3 substrates. Varying thicknesses of insulating SrHfO3 capping layers were deposited using a hafnium tert-butoxide precursor for Hf on top of SrNbO3 films to preserve the metastable surface. Grown films were transferred in vacuo for x-ray photoelectron spectroscopy to quantify elemental composition, density of states at the Fermi energy, and Nb oxidation state. Ex situ studies by x-ray absorption near edge spectroscopy and scanning transmission electron microscopy illustrate that the SrHfO3 capping plays an important role in preserving the crystalline quality of the material and the Nb 4d1 metastable charge state under atmospheric conditions.

 

Nanocrystalline MnFe2O4 has shown promise as a catalyst for the oxygen reduction reaction (ORR) in alkaline solutions, but the material has been sparingly studied as highly ordered thin-film catalysts. To examine the role of surface termination and Mn and Fe site occupancy, epitaxial MnFe2O4 and Fe3O4 spinel oxide films were grown on (001)- and (111)-oriented Nb:SrTiO3 perovskite substrates using molecular beam epitaxy and studied as electrocatalysts for the oxygen reduction reaction (ORR). High-resolution X-ray diffraction (HRXRD) and X-ray photoelectron spectroscopy (XPS) show the synthesis of pure phase materials, while scanning transmission electron microscopy (STEM) and reflection high-energy electron diffraction (RHEED) analysis demonstrate island-like growth of (111) surface-terminated pyramids on both (001)- and (111)-oriented substrates, consistent with the literature and attributed to the lattice mismatch between the spinel films and the perovskite substrate. Cyclic voltammograms under a N2 atmosphere revealed distinct redox features for Mn and Fe surface termination based on comparison of MnFe2O4 and Fe3O4. Under an O2 atmosphere, electrocatalytic reduction of oxygen was observed at both Mn and Fe redox features; however, a diffusion-limited current was only achieved at potentials consistent with Fe reduction. This result contrasts with that of nanocrystalline MnFe2O4 reported in the literature where the diffusion-limited current is achieved with Mn-based catalysis. This difference is attributed to a low density of Mn surface termination, as determined by the integration of current from CVs collected under N2, in addition to low conductivity through the MnFe2O4 film due to the degree of inversion. Such low densities are attributed to the synthetic method and island-like growth pattern and highlight challenges in studying ORR catalysis with single-crystal spinel materials. 

Transition metal oxides have long been an area of interest for water electrocatalysis through the oxygen evolution and oxygen reduction reactions. Iron oxides, such as LaFeO 3 , are particularly promising due to the favorable energy alignment of the valence and conduction bands comprised of Fe 3+ cations and the visible light band gap of such materials. In this work, we examine the role of band alignment on the electrocatalytic oxygen evolution reaction (OER) in the intrinsic semiconductor LaFeO 3 by growing epitaxial films of varying thicknesses on Nb-doped SrTiO 3 . Using cyclic voltammetry, we find that there is a strong thickness dependence on the efficiency of electrocatalysis for OER. These measurements are understood based on interfacial band alignment in the system as well as catalytically active surface defect states as confirmed by layer-resolved electron energy loss spectroscopy, electrochemical impedance spectroscopy, and Mott–Schottky measurements. Our results demonstrate the importance of band engineering for the rational design of thin film electrocatalysts for renewable energy sources.