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At least 163 records · Page 9

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DOE OSTI2025

Local lattice distortions drive the transition of BaIrO 3 into a ferromagnetic insulator state

Using variable temperature total and resonant x-ray scattering at the K edge of Ir species, we study the “bad metal” to insulator transition in BaIrO 3 , a canonical third transition series oxide. The usage of advanced experimental techniques and large-scale computer modeling helps us show that, contrary to the widely accepted view, charge disproportionation leading to the formation of Ir-trimers with a different number of 5d valence electrons already exists at room temperature. The charge disbalance between the trimers does not evolve much with decreasing temperature while local lattice distortions do, suggesting that the latter and not the former make a key contribution to the emergence of the enigmatic ferromagnetic insulator state of BaIrO 3 . The conclusion is supported by DFT calculations based on unmodified experimental structure data. Our work calls for a reconsideration of the role of lattice distortions in determining the electronic properties of third transition series oxides. It also charts a path to assessing these properties on a realistic and not assumed crystal structure basis.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Catalytic pyrolysis as a platform technology for supporting the circular carbon economy

Catalytic pyrolysis, a process that combines pyrolysis and vapour-phase catalytic upgrading, is a versatile technology platform capable of direct liquefaction of biomass and waste plastic into intermediates that can enable the decarbonized production of chemicals and/or transportation fuels. Recently, catalytic pyrolysis has attracted substantial research and commercialization attention, with over 15,000 journal articles and patents published in the past decade alone. Here, in this Perspective, we chart a path towards commercial-scale catalytic pyrolysis of waste plastic and biomass by identifying key short-term and long-term technological barriers. Within the proposed development roadmap addressing these barriers, catalytic pyrolysis can move from the demonstration scale to integrated biorefinery networks producing fuels and plastics precursors at a scale of between 0.1 and 1 billion tonnes of carbon per year.

09 BIOMASS FUELS↗

DOE OSTI2025

Extreme shape coexistence observed in 70 Co

The shape of the atomic nucleus is a property that underpins our understanding of nuclear systems, impacts the limits of nuclear existence, and enables probes of physics beyond the Standard Model. Nuclei can adopt a variety of shapes, including spheres, axially deformed spheroids, and pear shapes. In some regions of the nuclear chart where a spherical nucleus would naively be expected, deformed nuclear states can result from the collective action of constituent protons and neutrons. In a small subset of nuclei both spherical and deformed nuclear states have been experimentally observed, a phenomenon termed shape coexistence. We present spectroscopic evidence for the coexistence of J π = 1 + spherical and deformed states in 70 Co, separated by less than 275 keV. This close degeneracy of levels with the same J π and different shapes demonstrates an extreme example of shape coexistence resulting from the interplay of independent particle motion and collective behavior in highly unstable nuclear systems and identifies the Co isotopes as a transition point between deformed ground states observed in the Cr isotopes and spherical configurations observed in the closed-shell Ni isotopes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Discovering nuclear models from symbolic machine learning

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Nuclear structure↗

DOE OSTI2023

The quest for superheavy elements and the limit of the periodic table

The borders of the periodic table of the elements and of the chart of nuclides are not set in stone. The desire to explore the properties of atoms and their nuclei in a regime of very large numbers of electrons, protons and neutrons has motivated new experimental facilities to create new elements and nuclides at the limits of atomic number and mass. But the small production rates and short lifetimes of superheavy nuclei and their atoms mean that ‘atom-at-a-time’ studies are the only experimental way to probe them. The physical and chemical data obtained so far, augmented by theoretical calculations, indicate significant deviations from extrapolations from lighter elements and isotopes. This situation raises the following question: how much further can one push the limits of the periodic table? Here, in this Review, we describe the major challenges in the field of the superheavy elements and speculate about future directions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Microscopic relaxation channels in materials for superconducting qubits

Despite mounting evidence that materials imperfections are a major obstacle to practical applications of superconducting qubits, connections between microscopic material properties and qubit coherence are poorly understood. Here, we combine measurements of transmon qubit relaxation times ( T 1 ) with spectroscopy and microscopy of the polycrystalline niobium films used in qubit fabrication. By comparing films deposited using three different techniques, we reveal correlations between T 1 and intrinsic film properties such as grain size, enhanced oxygen diffusion along grain boundaries, and the concentration of suboxides near the surface. Qubit and resonator measurements show signatures of two-level system defects, which we propose to be hosted in the grain boundaries and surface oxides. We also show that the residual resistance ratio of the polycrystalline niobium films can be used as a figure of merit for qubit lifetime. This comprehensive approach to understanding qubit decoherence charts a pathway for materials-driven improvements of superconducting qubit performance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts

Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here, in this work, we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.

catalytic mechanisms↗

DOE OSTI2020

Analytical model for drag reduction on liquid-infused structured non-wetting surfaces

Liquid-infused structured non-wetting surfaces provide alternating no-slip and partial slip boundary conditions to the fluid flow, resulting in reduced friction at the interface. In this paper, an analytical model is developed for the evaluation of effective slip and, in turn, friction factor and drag reduction on liquid-infused structured non-wetting surfaces. By considering the entire range of anisotropy and heterogeneity of the surface structures as well as the full range of partial slip offered by the infusion liquid, the present model eliminates empirical fitting or correlations that are inherent in previous studies. Based on the effective slip length, drag reduction and skin friction coefficient values for Newtonian flow between two infinite parallel plates and flow in round tubes are presented. Extension of Moody charts for non-wetting surfaces and design maps of surface meso/micro/nano texturing for achieving desired drag reduction are presented for a broad range of engineering applications. The paper further presents independent validation of the model across experimental and computational data from the literature and brings together several previous studies in a unified manner.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Porosity modeling in a TiNbTaZrMo high-entropy alloy for biomedical applications

High-entropy alloys (HEAs) have attracted great attention for many biomedical applications. However, the nature of interatomic interactions in this class of complex multicomponent alloys is not fully understood. We report, for the first time, the results of theoretical modeling for porosity in a large biocompatible HEA TiNbTaZrMo using an atomistic supercell of 1024 atoms that provides new insights and understanding. Our results demonstrated the deficiency of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with available experimental data to reduce Young's modulus. We utilized the novel concepts of the total bond order density (TBOD) and partial bond order density (PBOD) via ab initio quantum mechanical calculations as an effective theoretical means to chart a road map for the rational design of complex multicomponent HEAs for biomedical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Integrated electro- and chemical characterization of sulfide-based solid-state electrolytes

Sulfide solid-state electrolytes (SSEs) represent a critical advancement towards enabling next-generation lithium metal batteries. However, a profound knowledge gap remains in understanding the structure–property relationships inherent to these sulfide SSEs. Electrochemical assessment and spectroscopic tools, such as Raman spectroscopy, offer bench-top ready, non-invasive, powerful avenues for operando and in situ analyses. Despite this potential, the integration of these methodologies, particularly for real-time interrogation, is markedly under-investigated. This review endeavors to catalog the use of diverse electrochemical techniques and spectroscopic tools in elucidating the structural and functional nuances of sulfide SSEs. Through the harmonization of these multifaceted evaluation strategies, our objective is to chart a course towards optimized sulfide SSEs, thereby aiding in the development of informed protocols for a deeper comprehension and understanding of the structure–property relationship and interfacial engineered design of solid-state batteries using sulfide-based SSEs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The cosmic web of X-ray active galactic nuclei seen through the eROSITA Final Equatorial Depth Survey (eFEDS)

Which galaxies in the general population turn into active galactic nuclei (AGNs) is a keystone of galaxy formation and evolution. Thanks to SRG/eROSITA’s contiguous 140 square degree pilot survey field, we constructed a large, complete, and unbiased soft X-ray flux-limited (FX > 6.5 × 10 -15 erg s -1 cm -2 ) AGN sample at low redshift, 0.05 < z < 0.55. Two summary statistics, the clustering using spectra from SDSS-V and galaxy-galaxy lensing with imaging from HSC, are measured and interpreted with halo occupation distribution and abundance matching models. Both models successfully account for the observations. We obtain an exceptionally complete view of the AGN halo occupation distribution. The population of AGNs is broadly distributed among halos with a mean mass of 3.9 -2.4 +2.0 × 10 12 M ⊙ . This corresponds to a large-scale halo bias of b(z = 0.34) = 0.99 -0.10 +0.08 . The central occupation has a large transition parameter, σ log 10 (M) = 1.28 ± 0.2. The satellite occupation distribution is characterized by a shallow slope, α sat = 0.73 ± 0.38. We find that AGNs in satellites are rare, with f sat < 20%. Most soft X-ray-selected AGNs are hosted by central galaxies in their dark matter halo. A weak correlation between soft X-ray luminosity and large-scale halo bias is confirmed (3.3σ). We discuss the implications of environmental-dependent AGN triggering. This study paves the way toward fully charting, in the coming decade, the coevolution of X-ray AGNs, their host galaxies, and dark matter halos by combining eROSITA with SDSS-V, 4MOST, DESI, LSST, and Euclid data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

BigPanDA monitoring system evolution in the ATLAS Experiment

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

dCache CI/CD migration to Kubernetes

For over two decades, the dCache project has provided open-source to satisfy ever-more demanding storage requirements. More than 80 sites worldwide rely on dCache to provide services for LHC experiments, Belle-II, Eu- XFEL, and others. This can be achieved only with a well-established process from a whiteboard, where ideas are created through development, packaging, and testing. The project’s build and test infrastructure is based on Jenkins CI and a set of virtual machines. This infrastructure is maintained by dCache developers. With the introduction of the DESY-central Gitlab server, the developers have started migrating from VM-based testing to container-based deployments in the onsite Kubernetes cluster. As a result, we have packaged dCache containers and Helm charts that can be used by other sites to reproduce our test and build steps quickly or to evaluate new releases on their pre-production systems and, eventually, become a standard model of dCache deployment at the sites. This paper describes the challenges we have faced, the techniques we used to solve them, and the issues that still need to be addressed.

Mkrtchyan, Tigran [DESY]↗

DOE OSTI2022

Promise of nonthermal plasmas in addressing emerging environmental and health problems: Present and future

Nonthermal atmospheric pressure plasmas transform input electrical energy efficiently into reactive species, charged particles, and photons. This “activated gas” is being investigated as solutions for a range of environmental and health problems facing society today. In this Perspective, we take a cursory look at a few of these societal problems and the reflected role that plasmas may play in charting the pathway to a solution buoyed by supporting research. Here, we survey the plasma-based opportunities in the removal of trace contaminants in water supporting methodologies such as water reuse, which addresses scarcity and pollution, the opportunity posed by plasmas-based chemical depolymerization for plastics recycling, and the application of plasmas for food security, which includes sterilization of foodstuffs and the improvement of crop yield. Finally, we also included a short review on how plasmas may help control disease spread. In each case, the scope of the problem is presented along with the potential plasma-based solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Radio frequency electrical resistance measurement under destructive pulsed magnetic fields

We developed a resistance measurement using radio frequency reflection to investigate the electrical transport characteristics under destructive pulsed magnetic fields above 100 T. A sample stage consisting of a homemade flexible printed circuit reduced the noise caused by the induced voltage from the pulsed magnetic fields, improving the accuracy of the measurements of the reflected waves. From the obtained reflectance data, the absolute value of the magnetoresistance was successfully determined by analyzing the phase with admittance charts. In conclusion, these developments enable more accurate and comprehensive measurements of electrical resistance in pulsed magnetic fields.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Coherent diffractive imaging with twisted X-rays: Principles, applications, and outlook

Recent technological breakthroughs in synchrotron and x-ray free electron laser facilities have revolutionized nanoscale structural and dynamic analyses in condensed matter systems. This review provides a comprehensive overview of the advancements in coherent scattering and diffractive imaging techniques, which are now at the forefront of exploring materials science complexities. These techniques, notably Bragg coherent diffractive imaging and x-ray photon correlation spectroscopy, x-ray magnetic dichroism, and x-ray correlation analysis leverage beam coherence to achieve volumetric three-dimensional imaging at unprecedented sub-nanometer resolutions and explore dynamic phenomena within sub-millisecond timeframes. Such capabilities are critical in understanding and developing advanced materials and technologies. Simultaneously, the emergence of chiral crystals—characterized by their unique absence of standard inversion, mirror, or other roto-inversion symmetries—presents both challenges and opportunities. These materials exhibit distinctive interactions with light, leading to phenomena such as molecular optical activity, chiral photonic waveguides, and valley-specific light emissions, which are pivotal in the burgeoning fields of photonic and spintronic devices. This review elucidates how novel x-ray probes can be leveraged to unravel these properties and their implications for future technological applications. A significant focus of this review is the exploration of new avenues in research, particularly the shift from conventional methods to more innovative approaches in studying these chiral materials. Inspired by structured optical beams, the potential of coherent scattering techniques utilizing twisted x-ray beams is examined. This promising direction not only offers higher spatial resolution but also opens the door to previously unattainable insights in materials science. By contextualizing these advancements within the broader scientific landscape and highlighting their practical applications, this review aims to chart a course for future research in this rapidly evolving field.

Physics↗

DOE OSTI2024

Digital twins and deep learning segmentation of defects in monolayer MX 2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX 2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX 2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX 2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. Further, this digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗