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Steven R. Spurgeon

Publications and source records attributed to Steven R. Spurgeon.

At least 19 recordsLinked to original sources

Revealing the Atomic Structure of NiO/Ga$_{2}$O$_{3}$ Interfaces

NiO/Ga$_{2}$O$_{3}$ heterojunctions have garnered significant attention for use in power electronics due to the ultrawide bandgap and wafer-scale availability of Ga$_{2}$O$_{3}$ and the controllable p-type doping of NiO. However, the structure of NiO/Ga$_{2}$O$_{3}$ interfaces remains underexplored, largely due to the complexity of the junction between their dissimilar cubic and monoclinic crystal structures. Here we investigate the atomistic structure of the NiO/Ga$_{2}$O$_{3}$ interface for (100), (-201), and (001) oriented Ga$_{2}$O$_{3}$ substrates using aberration-corrected scanning transmission electron microscopy (STEM) in combination with interface modeling and image simulations. We evaluate the abruptness and consistency of the interfaces and compare them to calculated interface models, proposing precise atomic structures and assessing potential structural variation arising from complexity of the monoclinic Ga$_{2}$O$_{3}$ crystal structure. Our interface analysis supports increased focus on (100) oriented Ga$_{2}$O$_{3}$ as a candidate for fabricating high quality, low defect density NiO/Ga$_{2}$O$_{3}$ heterojunction devices. Importantly, we consider the effects of specimen thickness and 3D-to-2D projection during the STEM imaging process to differentiate such effects from real crystal variations. This work provides insight into the effect of substrate orientation on NiO film and interface quality, creating a pathway to improving heterojunction properties. It further highlights important considerations for interpretation of stability and interlayer phase formation in these interfaces, which is crucial for their integration into reliable and robust power electronic devices.

cond-mat.mtrl-sci

Atomic-Scale Characterization of Oxide Interfaces and Superlattices Using Scanning Transmission Electron Microscopy

Scanning transmission electron microscopy (STEM) is a cornerstone of our understanding of oxide interfaces and superlattices. No other technique provides the same level of insight into structure, chemistry, composition, and dynamics across as wide a variety of material systems. STEM imaging and diffraction, coupled with electron energy loss (EELS) and energy-dispersive X-ray (EDS) spectroscopies, offer unparalleled, high-resolution analysis of structure--property relationships. In this chapter we highlight investigations into key phenomena, including interfacial conductivity in oxide superlattices, charge screening effects in magnetoelectric heterostructures, interface engineering in iron oxides, and the complex physics governing atomic-scale chemical mapping. We also discuss emerging plasma preparation techniques and artificial intelligence-guided approaches to both ex situ and in situ microscopy. These studies illustrate how unique insights from STEM characterization can be integrated with other techniques and theory calculations to develop more predictive models for the behavior of functional oxides.

cond-mat.mtrl-sci

Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

cond-mat.mtrl-sci

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti$_3$C$_2$T$_X$ MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures--from isolated vacancies to nanopores--revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

cond-mat.mtrl-sci

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci

Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization

What does materials science look like in the "Age of Artificial Intelligence?" Each materials domain-synthesis, characterization, and modeling-has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.

cond-mat.mtrl-sci

Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

cond-mat.mtrl-sci

Isolation of Single Donors in ZnO

The shallow donor in zinc oxide (ZnO) is a promising semiconductor spin qubit with optical access. Single indium donors are isolated in a commercial ZnO substrate using plasma focused ion beam (PFIB) milling. Quantum emitters are identified optically by spatial and frequency filtering. The indium donor assignment is based on the optical bound exciton transition energy and magnetic dependence. The single donor emission is intensity and frequency stable with a transition linewidth less than twice the lifetime limit. The isolation of optically stable single donors post-FIB fabrication is promising for optical device integration required for scalable quantum technologies based on single donors in direct band gap semiconductors.

cond-mat.mes-hall

Silicon-lattice-matched boron-doped gallium phosphide: A scalable acousto-optic platform

The compact size, scalability, and strongly confined fields in integrated photonic devices enable new functionalities in photonic networking and information processing, both classical and quantum. Gallium phosphide (GaP) is a promising material for active integrated photonics due to its high refractive index, wide band gap, strong nonlinear properties, and large acousto-optic figure of merit. In this work we demonstrate that silicon-lattice-matched boron-doped GaP (BGaP), grown at the 12-inch wafer scale, provides similar functionalities as GaP. BGaP optical resonators exhibit intrinsic quality factors exceeding 25,000 and 200,000 at visible and telecom wavelengths respectively. We further demonstrate the electromechanical generation of low-loss acoustic waves and an integrated acousto-optic (AO) modulator. High-resolution spatial and compositional mapping, combined with ab initio calculations indicate two candidates for the excess optical loss in the visible band: the silicon-GaP interface and boron dimers. These results demonstrate the promise of the BGaP material platform for the development of scalable AO technologies at telecom and provide potential pathways toward higher performance at shorter wavelengths.

physics.optics

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

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-centered 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 the 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 and 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.

cond-mat.mtrl-sci

Evaluating Stage Motion for Automated Electron Microscopy

Precise control is an essential and elusive quality of emerging self-driving microscopes. It is widely understood these instruments must be capable of performing rapid, high-volume, and arbitrary movements for practical self-driving operation. However, stage movements are difficult to automate at scale, owing to mechanical instability, hysteresis, and thermal drift. Such difficulties pose major barriers to intelligent microscope designs that require repeatable, precise movements. To guide design of emerging instruments, it is necessary to understand the behavior of existing designs to identify rate limiting steps for full autonomy. Here we describe a general framework to evaluate stage motion in any electron microscope. We define metrics to evaluate stage degrees of freedom, propose solutions to improve performance, and comment on fundamental limits to automated experimentation using present hardware.

physics.app-ph

Jahn-Teller-driven Phase Segregation in Mn$_{x}$Co$_{3-x}$O$_{4}$ Spinel Thin Films

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 of 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, Mn$_{x}$Co$_{3-x}$O$_{4}$ 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 Mn$^{3+}$ and Mn$^{4+}$ character with evidence of phase segregation tendencies with increasing Mn content and increasing Mn$^{3+}$ formal charge. Phase segregation may occur due to structural incompatibility between cubic and tetragonal crystal structures associated with Mn$^{4+}$ and Jahn-Teller active Mn$^{3+}$ octahedra, respectively. Our results help to explain the reported differences across samples in these promising materials for renewable energy technologies.

cond-mat.mtrl-sci

Resolving diverse oxygen transport pathways across Sr-doped lanthanum ferrite and metal-perovskite heterostructures

Perovskite structured transition metal oxides are important technological materials for catalysis and solid oxide fuel cell applications. Their functionality often depends on oxygen diffusivity and mobility through complex oxide heterostructures, which can be significantly impacted by structural and chemical modifications, such as doping. Further, when utilized within electrochemical cells, interfacial reactions with other components (e.g. Ni- and Cr-based alloy electrodes and interconnects) can influence the perovskite's reactivity and ion transport, leading to complex dependencies that are difficult to control in real-world environments. Here we use isotopic tracers and atom probe tomography to directly visualize oxygen diffusion and transport pathways across perovskite and metal-perovskite heterostructures, i.e. (Ni-Cr coated) Sr-doped lanthanum ferrite (LSFO). Annealing in 18O2(g) results in elemental and isotopic redistributions through oxygen exchange (OE) in the LSFO while Ni-Cr undergoes oxidation via multiple mechanisms and transport pathways. Complementary density functional theory (DFT) calculations at experimental conditions provide rationale for OE reaction mechanisms and reveal a complex interplay of different thermodynamic and kinetic drivers. Our results shed light on the fundamental coupling of defects and oxygen transport in an important class of catalytic materials.

cond-mat.mtrl-sci

Free-Standing Epitaxial SrTiO$_3$ Nanomembranes via Remote Epitaxy using Hybrid Molecular Beam Epitaxy

The epitaxial growth of functional materials using a substrate with a graphene layer is a highly desirable method for improving structural quality and obtaining free-standing epitaxial nano-membranes for scientific study, applications, and economical reuse of substrates. However, the aggressive oxidizing conditions typically employed to grow epitaxial perovskite oxides can damage graphene. Here, we demonstrate a technique based on hybrid molecular beam epitaxy that does not require an independent oxygen source to achieve epitaxial growth of complex oxides without damaging the underlying graphene. The technique produces films with self-regulating cation stoichiometry control and epitaxial orientation to the oxide substrate. Furthermore, the films can be exfoliated and transferred to foreign substrates while leaving the graphene on the original substrate. These results open the door to future studies of previously unattainable free-standing nano-membranes grown in an adsorption-controlled manner by hybrid molecular beam epitaxy, and has potentially important implications for the commercial application of perovskite oxides in flexible electronics.

cond-mat.mtrl-sci

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This unique capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition using in-situ environmental TEM data, informing models of morphological evolution and catalytic properties. The model performance and achieved accuracy of predictions are desirable based on, for scientific data characteristic, based on limited size of training data sets. The model convergence and values for the loss function mean square error show dependence on the training strategy, and structural similarity measure between predicted structure images and ground truth reaches the value of about 0.7. This computed structural similarity is smaller than values obtained when the deep learning architecture is trained using much larger benchmark data sets, it is sufficient to show the structural transition of Au nanoparticles. While performance parameters of our model applied to scientific data fall short of those achieved for the non-scientific big data sets, we demonstrate model ability to predict the evolution, even including the particle structural phase transformation, of Au nano particles as catalyst for CO oxidation under the chemical reaction conditions. Using this approach, it may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms.

cond-mat.mtrl-sci

Hybrid Molecular Beam Epitaxy of Ge-based Oxides

Germanium-based oxides such as rutile GeO2 are garnering attention owing to their wide band gaps and the prospects for ambipolar doping for application in high-power devices. Here, we present the use of germanium tetraisopropoxide (GTIP) (an organometallic chemical precursor) as a source of Ge for the demonstration of hybrid molecular beam epitaxy (MBE) for Ge-containing compounds. We use Sn1-xGexO2 and SrSn1-xGexO3 as model systems to demonstrate this new 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 Sn1-xGexO2 on TiO2(001) substrates up to x = 0.54 and coherent perovskite SrSn1-xGexO3 on GdScO3(110) substrates up to x = 0.16. Characterization and first-principles calculations corroborate that Ge preferentially occupies the Sn site, as opposed to the Sr site. These findings confirm the viability of the GTIP precursor for the growth of germanium-containing oxides by hybrid MBE, and thus open the door to high-quality perovskite germanate films.

cond-mat.mtrl-sci

Oxygen Reduction Electrocatalysis with Epitaxially Grown Spinel MnFe$_{2}$O$_{4}$ and Fe$_{3}$O$_{4}$

Nanocrystalline MnFe$_{2}$O$_{4}$ has shown promise as a catalyst for the oxygen reduction reaction (ORR) in alkaline solutions, but the material has been lightly studied as highly ordered thin film catalysts. To examine the role of surface termination and Mn and Fe site occupancy, epitaxial MnFe$_{2}$O$_{4}$ and Fe$_{3}$O$_{4}$ spinel oxide films were grown on (001) and (111) oriented Nb:SrTiO$_{3}$ perovskite substrates using molecular beam epitaxy and studied as electrocatalysts for the oxygen reduction reaction (ORR). HRXRD and XPS show synthesis of pure phase materials while STEM and 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 lattice mismatch between the spinel films and perovskite substrate. Cyclic voltammograms under an N$_{2}$ atmosphere revealed distinct redox features for Mn and Fe surface termination based on comparison of MnFe$_{2}$O$_{4}$ and Fe$_{3}$O$_{4}$. Under O$_{2}$ atmosphere, electrocatalytic reduction of oxygen was observed at both Mn and Fe redox features; however, diffusion limited current was only achieved at potentials consistent with Fe reduction. This result contrasts with that of nanocrystalline MnFe$_{2}$O$_{4}$ reported in the literature where 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 N$_{2}$, in addition to low conductivity through the MnFe$_{2}$O$_{4}$ 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.

cond-mat.mtrl-sci

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We demonstrate how a centralized controller, informed by machine learning combining limited $a$ $priori$ knowledge and task-based discrimination, can drive on-the-fly experimental decision-making. This platform unlocks practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

cond-mat.mtrl-sci