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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probing topological phase transitions using high-harmonic generation

We report the prediction and realization of topological insulators have sparked great interest in experimental approaches to the classification of materials. The phase transition between non-trivial and trivial topological states is important, not only for basic materials science but also for next-generation technology, such as dissipation-free electronics. It is therefore crucial to develop advanced probes that are suitable for a wide range of samples and environments. Here we demonstrate that circularly polarized laser-field-driven high-harmonic generation is distinctly sensitive to the non-trivial and trivial topological phases in the prototypical three-dimensional topological insulator bismuth selenide. The phase transition is chemically initiated by reducing the spin–orbit interaction strength through the substitution of bismuth with indium atoms. We find strikingly different high-harmonic responses of trivial and non-trivial topological surface states that manifest themselves as a conversion efficiency and elliptical dichroism that depend both on the driving laser ellipticity and the crystal orientation. The origins of the anomalous high-harmonic response are corroborated by calculations using the semiconductor optical Bloch equations with pairs of surface and bulk bands. As a purely optical approach, this method offers sensitivity to the electronic structure of the material, including its nonlinear response, and is compatible with a wide range of samples and sample environments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

High‐Resolution Electron Tomography of Ultrathin Boerdijk–Coxeter–Bernal Nanowire Enabled by Superthin Metal Surface Coating

Abstract The rapid advancement of transmission electron microscopy has resulted in revolutions in a variety of fields, including physics, chemistry, and materials science. With single‐atom resolution, 3D information of each atom in nanoparticles is revealed, while 4D electron tomography is shown to capture the atomic structural kinetics in metal nanoparticles after phase transformation. Quantitative measurements of physical and chemical properties such as chemical coordination, defects, dislocation, and local strain have been made. However, due to the incompatibility of high dose rate with other ultrathin morphologies, such as nanowires, atomic electron tomography has been primarily limited to quasi‐spherical nanoparticles. Herein, the 3D atomic structure of a complex core–shell nanowire composed of an ultrathin Boerdijk–Coxeter–Bernal (BCB) core nanowire and a noble metal thin layer shell deposited on the BCB nanowire surface is discovered. Furthermore, it is demonstrated that a new superthin noble metal layer deposition on an ultrathin BCB nanowire could mitigate electron beam damage using an in situ transmission electron microscope and atomic resolution electron tomography. The colloidal coating method developed for electron tomography can be broadly applied to protect the ultrathin nanomaterials from electron beam damage, benefiting both the advanced material characterizations and enabling fundamental in situ mechanistic studies.

Song, Xiaohui↗
DOE OSTI2022

Probing growth of metal–organic frameworks with X-ray scattering and vibrational spectroscopy

Nucleation and crystallization arising from liquid to solid phase are involved in a multitude of processes in fields ranging from materials science to biology. Controlling the thermodynamics and kinetics of growth is advantageous to help tune the formation of complex morphologies. In this report, we harness wide-angle X-ray scattering and vibrational spectroscopy to elucidate the mechanism for crystallization and growth of the metal–organic framework Co-MOF-74 within microscopic volumes enclosed in a capillary and an attenuated total reflection microchip reactor. The experiments reveal molecular and structural details of the growth processes, while the results of plane wave density functional calculations allow identification of lattice and linker modes in the formed crystals. Synthesis of the metal–organic framework with microscopic volumes leads to monodisperse and micron-sized crystals, in contrast to those typically observed under bulk reaction conditions. Reduction in the volume of reagents within the microchip reactor was found to accelerate the reaction rate. The coupling of spectroscopy with scattering to probe reactions in microscopic volumes promises to be a useful tool in the synthetic chemist's kit to understand chemical bonding and has potential in designing complex materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Portable Miniature Cryogenic Environment for In Situ Neutron Diffraction

Neutron diffraction instruments offer a platform for materials science and engineering studies at extended temperature ranges far from ambient. As one of the widely used neutron sample environment types, cryogenic furnaces are usually bulky and complex, and they may need hours of beamtime overhead for installation, configuration, cooling, and sample change, etc. To reduce the overhead time and expedite experiments at the state-of-the-art high-flux neutron source, we developed a low-cost, miniature, and easy-to-use cryogenic environment (77–473 K) for in situ neutron diffraction. A travel-size mug serves for the environment where the samples sit inside. Immediate cooling and an isothermal dwell at 77 K are realized on the sample by direct contact with liquid N 2 in the mug. The designed Al inserts serve as the holder of samples and heating elements, alleviate the thermal gradient, and clear neutron pathways. Both a single-sample continuous measurement and multi-sample high-throughput measurements are demonstrated in this environment. High-quality and refinable in situ neutron diffraction patterns are acquired on model materials. The results quantify the orthorhombic-to-cubic phase transformation process in LiMn 2 O 4 and differentiate the anisotropic lattice thermal expansions and bond length evolutions between rhombohedral perovskite oxides with composition variation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2021

Towards Efficient Uncertainty estimation in deep learning for robust energy prediction in crystal materials

In material science, recent studies have started to explore the potential of using deep learning to improve property prediction from high-fidelity simulations, e.g, density functional theory (DFT). However, the design spaces are sometimes too large and intractable to sample completely. This results in a critical question that is how to evaluate the confidence and robustness of the prediction. In this paper, we propose an efficient approach to estimate uncertainty in deep learning using a single forward pass and then apply it for robust prediction of the total energy in crystal lattice structures. Our approach is built upon the deep kernel learning (DKL) that originally introduces to leverage the expressiveness of deep neural networks as input with a probabilistic prediction of Gaussian processes (GPs) as output. Existing DKL methods have difficulties in the accuracy of predictive uncertainty, training stability, and scaling to large datasets, which lead to significant barriers in real-world applications. We propose to address these challenges by using an inducing point approximate GP in feature space combined with spectral normalization as a regularization. We finally demonstrate our robust performance on an artificial example and a real-world application from materials chemistry.

Bi, Sirui↗
DOE OSTI2024

Comparing first-principles density functionals plus corrections for the lattice dynamics of YBa 2 Cu 3 O 6

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa 2 Cu 3 O 6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa 2 Cu 3 O 6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. Finally, in addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Strain-stabilized superconductivity

Superconductivity is among the most fascinating and well-studied quantum states of matter. Despite over 100 years of research, a detailed understanding of how features of the normal-state electronic structure determine superconducting properties has remained elusive. For instance, the ability to deterministically enhance the superconducting transition temperature by design, rather than by serendipity, has been a long sought-after goal in condensed matter physics and materials science, but achieving this objective may require new tools, techniques and approaches. Here, we report the transmutation of a normal metal into a superconductor through the application of epitaxial strain. We demonstrate that synthesizing RuO 2 thin films on (110)-oriented TiO 2 substrates enhances the density of states near the Fermi level, which stabilizes superconductivity under strain, and suggests that a promising strategy to create new transition-metal superconductors is to apply judiciously chosen anisotropic strains that redistribute carriers within the low-energy manifold of d orbitals.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Hyperspectral and Nanosecond Temporal Resolution Widefield Infrared Photothermal Heterodyne Imaging

Label-free, bond-selective imaging offers new opportunities for fundamental and applied studies in chemistry, biology, and materials science. Preventing its broader application to investigating spectrally congested specimens are issues related to low sensitivity as well as low spatial and temporal resolution. Here, we demonstrate a widefield, mid-infrared (MIR) photothermal imaging technique, called widefield Infrared Photothermal Heterodyne imaging (wIR-PHI), that massively parallelizes acquisition of MIR absorption data through use of a high-speed complementary metal-oxide-semiconductor camera. wIR-PHI possesses notable features that include: spatial resolution significantly below the MIR diffraction limit, hyperspectral imaging capabilities, high sensitivity, and ~100 ns temporal resolution. Here, the first two features are highlighted by hyperspectral imaging of proximally close poly(methyl methacrylate) (PMMA) and polystyrene (PS) nanoparticles where clear, bond-specific imaging of nanoparticles, separated by less than the MIR diffraction limit, is demonstrated. Sensitivity is highlighted by imaging individual PMMA and PS nanoparticles with radii between r = 97-556 nm. This leads to a current, peak absorption cross-section limit-of-detection of σ abs = 1.9× 10 –16 cm 2 . wIR-PHI's 100 ns temporal resolution is simultaneously demonstrated by observing the decay of photothermal contrast on individual nanoparticles on a ~200-6000 ns timescale. In whole, wIR-PHI’s dramatic increase in acquisition speed opens opportunities for future MIR kinetic imaging and spectroscopic studies of important chemical, biological, and material processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Charge transfer driven by ultrafast spin transition in a CoFe Prussian blue analogue

Photoinduced charge-transfer is an important process in nature and technology and is responsible for the emergence of exotic functionalities, such as magnetic order for cyanide-bridged bimetallic coordination networks. Despite its broad interest and intensive developments in chemistry and material sciences, the atomic-scale description of the initial photoinduced process, which couples intermetallic charge-transfer and spin transition, has been debated for decades; it has been beyond reach due to its extreme speed. In this work we study this process in a prototype cyanide-bridged CoFe system by femtosecond X-ray and optical absorption spectroscopies, enabling the disentanglement of ultrafast electronic and structural dynamics. Our results demonstrate that it is the spin transition that occurs first on the Co site within ~50 fs, and it is this that drives the subsequent Fe-to-Co charge-transfer within ~200 fs. This study represents a step towards understanding and controlling charge-transfer-based functions using light.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantum algorithm for electronic band structures with local tight-binding orbitals

While the main thrust of quantum computing research in materials science is to accurately measure the classically intractable electron correlation effects due to Coulomb repulsion, designing optimal quantum algorithms for simpler problems with well-understood solutions is a useful tactic to advance our quantum “toolbox”. With this in mind, we consider the quantum calculation of a periodic system’s single-electron band structure over a path through reciprocal space. Previous efforts have used the Variational Quantum Eigensolver algorithm to solve the energy of each band, which involves numerically optimizing the parameters of a variational quantum circuit to minimize a cost function, constructed as the expectation value of a Hamiltonian operator. Traditionally, a unique Hamiltonian operator is constructed for each k-point, so that many cost functions, each with their own parameter space, must be optimized to generate a single band. Similarly, calculating higher bands than the first has traditionally involved modifying the cost function with additional overlap terms to ensure higher-energy eigenstates are orthogonal to those of lower bands. In this paper, we adopt a direct space approach, using a novel hybrid first/second-quantized qubit mapping which allows us to construct a single Hamiltonian, and a single cost-function, suitable for solving the entire electronic band structure. In contrast to previous approaches, the k-point and the band index are selected by additional parameters in our quantum circuit, rather than through modifications to the cost function. The result is a technically and conceptually simpler approach to band structure calculations on a quantum computer. Moreover, we expect that the tools developed herein will motivate new strategies for tackling highly-correlated materials beyond the grasp of classical computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Fast-current-heating devices to study in situ phase formation in metallic glasses by using high-energy synchrotron radiation

Details of fast-resistive-heating setups, controlled heating ranging from ~10 1 K s –1 to ~10 3 K s –1 , to study in situ phase transformations (on heating and on cooling) in metallic glasses by high-energy synchrotron x-ray diffraction are discussed here. Both setups were designed and custom built at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden) and have been implemented at the P02.1 Powder Diffraction and Total Scattering Beamline and the P21.1 Swedish Materials Science Beamline at PETRA III storage ring, DESY, Hamburg. The devices are interchangeable at both beamlines. Joule heating is triggered automatically and is timed with the incident beam and detector. The crystallization process can be controlled via a feedback circuit by monitoring the change in the time-dependent resistivity and temperature of glasses. Different ambient atmospheres, such as vacuum and inert gases (He and Ar), can be used to control oxidation and cooling. The main focus of these devices is on understanding the crystallization mechanism and kinetics in metallic glasses, which are brittle and for which fast heating gives defined glass–crystal composites with enhanced plasticity. As an example, phase-transformation sequence(s) in a prototyped Cu–Zr-based metallic glass is described on heating, and a crystalline phase beneficial to the plasticity is identified.

47 OTHER INSTRUMENTATION↗