arXiv subjects

Bruce Ravel

Publications and source records attributed to Bruce Ravel.

9 recordsLinked to original sources

arXiv2026

Synthesis and Characterization of Compositionally Complex (Gd/Ho/Er/Dy)2Zr2O7 Thin Film Combinatorial Library

High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of next-generation thermal barrier materials. A combinatorial thin film material library of (GdDyHoEr)2Zr2O7 were created via combinatorial magnetron reactive sputtering with rare-earth/zirconium alloy targets. Structural, chemical, and thermal property characterization mapping across the four component composition space was performed and correlated with thermal transport measurements. Steady state thermoreflectance mapping identifies a pronounced minimum in thermal conductivity within the Dy/Gd-rich quadrant. This minimum does not coincide with either the equiatomic composition or the region predicted to exhibit maximum cation size disorder. Instead, it corresponds to the largest experimentally observed lattice parameter, despite deviating from Vegard-like chemical averaging, and is independent of grain size and whole-pattern microstrain. These observations suggest that the way the fluorite lattice accommodates compositional complexity, rather than cation size disorder alone, provides a more informative descriptor of thermal transport. Overall, this work establishes a high-throughput workflow for combinatorial thin-film synthesis and multimodal characterization, enabling the rapid identification of previously inaccessible structure-property relationships in compositionally complex ceramics.

cond-mat.mtrl-sci↗

arXiv2026

Surface Platinum Alloying for Passivation of Oxide Interfaces on Superconducting Niobium Films

Dielectric loss arising from two-level systems (TLS) at surfaces and interfaces remains a primary limitation to coherence in superconducting transmon qubits. Niobium (Nb), a widely used material in superconducting quantum circuits, readily forms native oxides under ambient conditions, leading to lossy dielectric interfaces that degrade device performance. Here, a robust and scalable fabrication strategy is demonstrated for chemically stabilizing Nb surfaces and mitigating further oxidation, including protection of both surface and sidewall regions. High-purity Nb films were fabricated with bulk-like superconducting transition temperatures ($T_c = 9.30\pm0.10$) K. We demonstrate that a thin Pt encapsulation layer, deposited after native oxide formation, can be transformed via thermal annealing into a Nb-Pt alloy at the surface. Spectroscopic and microscopic analyses confirm the formation of a chemically stable metallic alloy layer and its ability to suppress further oxide growth. Ab initio simulations elucidate the atomic-scale rearrangement and electronic structure evolution associated with Pt incorporation on native niobium oxide, providing insight into the stabilization mechanism of the alloyed surface. This approach offers a materials pathway for engineering chemically robust Nb interfaces, including sidewalls, toward higher-coherence superconducting qubit architectures."

cond-mat.supr-con↗

arXiv2025

A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments

High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enabling real-time orchestration of multi-beamline, multi-modal experiments. AI agents perform on-the-fly reduction, clustering, Gaussian process modelling, and Bayesian optimization driven data acquisition, while users monitor agent behavior and visualize results live. Combinatorial libraries of the ternary Al-Ni-Pt system were spatially mapped by X-ray diffraction and X-ray absorption fine structure measurements at the PDF and BMM beamlines, respectively. Dynamic switching between AI-driven and conventional grid mapping strategies was achieved, demonstrating the flexible workflows possible through this framework. A digital twin constructed from a simulated Al-Li-Fe oxide dataset shows that AI-driven mapping strategies outperform conventional mapping as well as random sampling by prioritizing measurements that better resolve both phase boundaries and localized minority phases. This framework supports plug-and-play capabilities, and establishes a foundation for routine multi-modal, AI-assisted large-scale user-facility operations.

physics.app-ph↗

arXiv2023

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design

The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a sample space, without an explicit scalar objective (e.g., the characterization of a wafer or sample library). In this work, we propose a flexible formulation of scientific value that recasts a dataset of input conditions and higher-dimensional observable data into a continuous, scalar metric. Intuitively, the scientific value function measures where observables change significantly, emulating the perspective of experts driving an experiment, and can be used in collaborative analysis tools or as an objective for optimization techniques. We demonstrate this technique by exploring simulated phase boundaries from different observables, autonomously driving a variable temperature measurement of a ferroelectric material, and providing feedback from a nanoparticle synthesis campaign. The method is seamlessly compatible with existing optimization tools, can be extended to multi-modal and multi-fidelity experiments, and can integrate existing models of an experimental system. Because of its flexibility, it can be deployed in a range of experimental settings for autonomous or accelerated experiments.

cond-mat.mtrl-sci↗

arXiv2023

Why is EXAFS analysis for multicomponent metals so hard? Challenges and opportunities for measuring ordering in complex concentrated alloys using x-ray absorption spectroscopy

Short range order is a critical driver of properties (e.g. corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended x-ray absorption fine structure (EXAFS) is a powerful technique well suited for quantifying this short range order.Here, we described in detail the characteristics of CCAs that make the already challenging task of analyzing EXAFS data even more difficult. We then illustrate novel paths towards robust and scalable quantitative SRO analysis which will accelerate the scientific understanding and development of CCAs.

cond-mat.mtrl-sci↗

arXiv2023

Self-driving Multimodal Studies at User Facilities

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

cs.LG↗

arXiv2009

Condensed Matter Astrophysics: A Prescription for Determining the Species-Specific Composition and Quantity of Interstellar Dust using X-rays

We present a new technique for determining the *quantity and composition* of dust in astrophysical environments using <6keV X-rays. We argue that high resolution X-ray spectra as enabled by the Chandra and XMM-Newton gratings should be considered a powerful and viable new resource for delving into a relatively unexplored regime for directly determining dust properties: composition, quantity, and distribution. We present initial cross-section measurements of astrophysically likely iron-based dust candidates taken at the Lawrence Berkeley National Laboratory Advanced Light Source synchrotron beamline, as an illustrative tool for the formulation of our methodology. Focused at the 700eV Fe LIII and LII photoelectric edges, we discuss a technique for modeling dust properties in the soft X-rays using L-edge data, to complement K-edge X-ray absorption fine structure analysis techniques discussed in Lee & Ravel 2005. This is intended to be *a techniques paper* of interest and usefulness to both condensed matter experimentalists and astrophysicists. For the experimentalists, we offer a new prescription for normalizing relatively low S/N L-edge cross section measurements. For astrophysics interests, we discuss the use of X-ray absorption spectra for determining dust composition in cold and ionized astrophysical environments, and a new method for determining *species-specific gas-to-dust ratios*. Possible astrophysical applications of interest, are offered. Prospects for improving on this work with future X-ray missions with higher throughput and spectral resolution are presented in the context of spectral resolution goals for gratings and calorimeters, for proposed and planned missions such as Astro-H and the International X-ray Observatory.

astro-ph.IM↗

arXiv2004

Determining the grain composition of the interstellar medium with high resolution X-ray spectroscopy

We investigate the ability of high resolution X-ray spectroscopy to directly probe the grain composition of the interstellar medium. Using iron K-edge experimental data of likely ISM dust candidates taken at the National Synchrotron Light Source at Brookhaven National Laboratory and the Advanced Photon Source at Argonne National Laboratory, we explore the prospects for determining the chemical composition of astrophysical dust and discuss a technique for doing so. Focusing on the capabilities of the AstroE2 XRS micro-calorimeters, we assess the limiting effects of spectral resolution and noise for detecting significant X-ray absorption fine structure signal in astrophysical environments containing dust. We find that given sufficient signal, the resolution of the XRS will allow us to definitively distinguish gas from dust phase absorption, and certain chemical compositions.

astro-ph↗