Detecting the critical point through entanglement in the Schwinger model
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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Detection of Critical Surface Features in PTLs and GDLs for Improved Device Performance and Manufacturing Reliability
High points, or features that protrude above the surface of the material, on porous transport layers (PTLs) and gas diffusion layers (GDLs) can be critical features that may affect the manufacturing process and the performance of the device containing the feature. High points on PTLs and GDLs may stress the membrane of a polymer electrolyte membrane (PEM) during lamination and cell operation of a PEM electrolyzer or fuel cell. Additionally, high points on GDLs may impact the reliability of the manufacturing process. Thus, understanding these critical features and developing procedures to detect them are a key part of developing quality control techniques for PTLs and GDLs. This work evaluates the effectiveness of the Keyence VR6200 benchtop-scale structured light optical profilometer for detection of surface protrusions on PTLs and GDLs. Standard testing procedures for detecting and measuring high points were created for use on both material types. These procedures were evaluated using Gage Repeatability and Reproducibility (Gage R&R), where the repeatability, reproducibility, and effectiveness of the system to detect and measure high points were quantified. We have shown with high statistical power that the system is very effective in detection and measurement of high points, with Gage R&R contributions measured to be 2.2% and 3.6% for PTLs and GDLs, respectively.
36 MATERIALS SCIENCE↗
Sequence-Based Anomaly Detection in Critical Infrastructure Networks
United States critical infrastructure faces new cyber threats from adversarial nation-state actors in the form of malware-free attacks. Traditional cybersecurity techniques use rules-based methods to identify indicators of compromise on networks, often missing these sophisticated attacks. Our approach leverages multiple state of the art machine learning models in a pipeline to identify abnormal network events through sequential analysis. We combine both device and packet-level information into individual events to characterize anomalous network actions. The model is trained and tested on real network traffic from the Idaho National Lab High Performance Computing (HPC) with greater than 98% precision. It is capable of flagging malicious tactics used by adversaries in malware-free attacks, severe changes to the network, and abnormal user activity by network devices.
99 - GENERAL AND MISCELLANEOUS↗
From Quantum to Classical: Sensing Materials, Methods, and Platforms for Energy
This presentation covers several ongoing sensors projects at NETL, including the use of quantum sensors for energy sector applications (e.g. infrastructure monitoring, critical metal detection, grid modernization) as well as NETL-developed materials and platforms for detecting economically critical metals including rare earth elements, cobalt, and aluminum. Taken together, these projects demonstrate NETL's leadership in unleashing American energy through the development of high performance sensors for reliable energy production and consumption.
critical minerals↗
Magnetostrictive EMAT for Nuclear Spent Fuel Canisters
One approach for mitigation of stainless steel (SS) spent fuel canisters (SFCs) is to coat the welds or even the entire SFC with a cold-sprayed nickel coating. The coating can serve as a magnetostrictive interface between an electromagnetic acoustic transducer (EMAT) and the SS plate material. The EMAT can send ultrasound into the canister and “listen” for reflections caused by chloride-initiated stress corrosion cracking (CISCC) or other damage mechanisms that may lead to leaks if undetected and unmitigated. Here, this project outlines an approach for monitoring the integrity of SFCs using magnetostrictive EMAT sensors, which emit and detect discontinuity-initiated reflections indicative of pits or cracks likely arising from CISCC. Flat 13 mm SS plates with welds similar to actual canister welds were prepared, with flat-bottom holes 3 mm and 6 mm deep and 6 mm in diameter placed within the weld heat-affected zone (HAZ). The most effective sensor configurations were able to detect critical discontinuities more than 2 m away. Although a single sensor could be robotically deployed, the large volume covered by each sensor could support a small number of permanently mounted sensors, equipped with an external connector for periodic interrogation. The HAZ of a hypothetical SFC cylindrical surface could be effectively monitored with just 16 sensors.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Voltage stability smart meter for analyzing voltage data and controlling an electrical power source and/or an electric appliance
Systems and methods for voltage stability monitoring and active/reactive power support are disclosed herein. In some embodiments, a smart electric meter of an end user in a grid power system can measure the voltage supplied to the end user via the grid power system, and can analyze the voltage data to detect critical voltage characteristics. The critical voltage characteristics may indicate that a voltage collapse event is likely. The smart electric meter can further estimate a voltage stability margin based on the voltage data. If necessary, the smart electric meter can control an electrical power source and/or an electric appliance positioned at or near the end user to increase the voltage stability margin.
Min, Liang↗
Luminescent Materials for the Detection of Economically Critical Metals in Harsh Environments
Renewable energy technologies used for electric vehicles and wind turbines are heavily reliant upon metals, such as rare earth elements, cobalt, lithium, and nickel. Indeed, there are 50 minerals that are currently considered “economically critical” by the 2022 United States Geological Survey. With anticipated global adoption of renewable energy technologies, producing sufficient metals to meet this demand presents a significant challenge, particularly due to the current monopolistic market for many of these metals. The production of metals from unconventional sources, such as coal utilization byproducts, is one of many promising strategies to boost domestic supply. However, sensitive, rapid, and inexpensive characterization technologies are needed to minimize production costs associated with metals prospecting and processing. Photoluminescence-based sensing techniques are particularly intriguing due to their potential for low cost and portability, coupled with high sensitivity and selectivity. This presentation focuses on the development of high-performance sensing materials for a range of critical metals, including metal-organic frameworks capable of sensitizing detection of parts-per-billion concentrations of six different rare earth elements, nanoparticles that can detect down to 600 parts-per-billion levels of cobalt, and thin films that sense aluminum down to 120 parts-per-billion. These materials are highly selective, capable of withstanding low pH conditions, and provide a response within minutes. Importantly, each sensing material is integrated with a custom-built, fully portable fiber-optic spectrometer for potential field deployment, providing significant cost savings over commercial instruments, along with potential advantages such as material regeneration for use across multiple sensing cycles and solvent removal for enhanced emission signal. These results highlight the exciting potential of luminescence platforms as cost-effective alternatives for metals characterization.
Crawford, Scott↗
Detecting fractionalization in critical spin liquids using color centers
Quantum spin liquids are highly entangled ground states of insulating spin systems, in which magnetic ordering is prevented down to the lowest temperatures due to quantum fluctuations. One of the most extraordinary characteristics of quantum spin liquid phases is their ability to support fractionalized, low-energy quasiparticles known as spinons, which carry spin-1/2 but bear no charge. Relaxometry based on color centers in crystalline materials—of which nitrogen-vacancy (NV) centers in diamond are a well-explored example—provides an exciting new platform to probe the spin spectral functions of magnetic materials with both energy and momentum resolution and to search for signatures of these elusive, fractionalized excitations. In this work, we theoretically investigate the color-center relaxometry of two archetypal quantum spin liquids: the two-dimensional U(1) quantum spin liquid with a spinon Fermi surface and the spin-1/2 antiferromagnetic spin chain. The former is characterized by a metallic, spin-split ground state of mobile, interacting spinons, which closely resembles a spin-polarized Fermi liquid ground state but with neutral quasiparticles. We show that the observation of the Stoner continuum and the collective spin wave mode in the spin spectral function would provide a strong evidence for the existence of spinons and fractionalization. In one dimension, mobile spinons form a Luttinger liquid ground state. We show that the spin spectral function exhibits strong features representing the collective density and spin-wave modes, which are broadened in an algebraic fashion with an exponent characterized by the Luttinger parameter. The possibilities of measuring these collective modes and detecting the power-law decay of the spectral weight using NV relaxometry are discussed. We also examine how the transition rates are modified by marginally irrelevant operators in the Heisenberg limit. Published by the American Physical Society 2024
Takei, So (ORCID:0000000191771895)↗
Materials for the Photoluminescence-Based Detection of Economically Critical Metals
Critical metals, such as rare earth elements (REEs), cobalt, lithium, aluminum, nickel, and others, are essential to advanced technologies and renewable energy in particular. Widespread global adoption of renewable energy technologies has spurred dramatic demand increases for these metals; however, the global supply of these metals is highly monopolistic and conventional mining poses economic and environmental challenges. As a result, there is increasing interest in domestic production from alternative resources such as coal and its utilization byproducts. Slow and expensive characterization costs remain a significant barrier for domestic production. Here, luminescent sensing materials and platforms are presented that provide an alternative to the current state-of-the-art characterization methods; highly sensitive and selective sensing materials for cobalt, aluminum, and rare earth elements are presented, as well as compact, inexpensive platforms capable of analyzing signal from these materials for rapid characterization of critical metal content.
Crawford, Scott↗
Materials for the Photoluminescence-Based Detection of Economically Critical Metals
Critical metals, such as rare earth elements (REEs), cobalt, lithium, aluminum, nickel, and others, are essential to advanced technologies and renewable energy in particular. Widespread global adoption of renewable energy technologies has spurred dramatic demand increases for these metals; however, the global supply of these metals is highly monopolistic and conventional mining poses economic and environmental challenges. As a result, there is increasing interest in domestic production from alternative resources such as coal and its utilization byproducts. Slow and expensive characterization costs remain a significant barrier for domestic production. Here, luminescent sensing materials and platforms are presented that provide an alternative to the current state-of-the-art characterization methods; highly sensitive and selective sensing materials for cobalt, aluminum, and rare earth elements are presented, as well as compact, inexpensive platforms capable of analyzing signal from these materials for rapid characterization of critical metal content.
Crawford, Scott↗
Early Fault Detection in Nuclear Systems: A Digital Engineering Approach
Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.
42 - ENGINEERING↗
Portable and Cost-Effective Device for Reliable Detection of Counterfeit and Non-compliant Refrigerants in Diverse Applications
Counterfeit refrigerants pose significant challenges to safety, system reliability, and operational effectiveness due to their harmful contaminants or incompatible chemical compositions. Utilizing these noncompliant products can lead to reduced efficiency, equipment failures, and expensive repairs. Additionally, heightened demand for alternative refrigerants during the industry's transition has created supply gaps, enabling counterfeit products to proliferate. Accurate detection and analysis tools are therefore essential to verify refrigerant authenticity and ensure system integrity in diverse applications. This paper presents the development of a portable device designed for reliable identification and detailed analysis of refrigerant composition. By integrating precision gas sampling, controlled pressure regulation, and automated sensor technology, the device not only detects deviations from standard refrigerant properties but also provides a comprehensive composition breakdown. Pre-calibrated sensors measure the refrigerant gas to identify specific concentrations and contaminants, with an intuitive LED-based indicator system ensuring quick interpretation of results. The user-friendly interface enables operators to select refrigerant types for targeted testing, further enhancing accuracy and usability for field technicians. Comprehensive testing was conducted on mildly flammable A2L refrigerants, showcasing the device’s robustness and adaptability in analyzing composition and detecting discrepancies. The device demonstrated consistent accuracy across a range of refrigerant samples, affirming its reliability in diverse operational environments. Its design minimizes contamination risks during sampling and provides detailed composition results within 90 seconds, ensuring efficient and precise analysis. With a projected price point under $150, the proposed solution delivers affordability alongside its lightweight portability and straightforward operation. Unlike complex and costly alternatives, such as gas chromatography systems, this device provides an accessible option for technicians, customs personnel, and industry operators in need of quick and effective refrigerant verification. Compatible with both current formulations and emerging refrigerant technologies, the device addresses critical counterfeit detection needs across a range of applications. By delivering accurate composition analysis and counterfeit identification, this innovation enhances system performance, safety, and operational reliability in crucial industries.
Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗
Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing
Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.
Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
Thermally Activated Circularly Polarized Photoluminescence in a 2D Hybrid Perovskite with Giant Spin Splitting
Circularly polarized light generation and detection are critical for future spin-based technologies that inter-convert circularly polarized photons and electron spins. However, detailed mechanisms in such spin-photon interfaces are often either poorly understood or operate at cryogenic temperatures since typically small energies separating spin-split electronic bands facilitate thermally driven spin depolarization. Recently, several 2D hybrid perovskites with polar achiral cations were theoretically demonstrated to exhibit conduction and valence band spin-splitting energies greatly exceeding room-temperature thermal energy, suggesting their utility as spin-photon interfaces with practical operating temperatures. Here, a strong "spin memory" effect is reported in such a polar achiral layered perovskite that enables large room-temperature circularly polarized emission anisotropy following excitation with circularly polarized light. The polarization anisotropy depends strongly on temperature (thermally activated), excitation energy, and crystal orientation with respect to the excitation source. Temperature-dependent photoconductance measurements reveal similar thermally activated carrier generation. These observations suggest a mechanism whereby giant in-plane splitting of single-particle levels protects spin-polarization of photogenerated electrons and holes before recombination. Although polarized light emission is explored in greater detail in chiral perovskites, these results reveal that even without chirality, large spin memory in polar achiral perovskites can enable spin-photon interfaces that operate at elevated temperatures.
14 SOLAR ENERGY↗
Temporal entanglement entropy as a probe of renormalization group flow
The recently introduced concept of timelike entanglement entropy has sparked a lot of interest. Unlike the traditional spacelike entanglement entropy, timelike entanglement entropy involves tracing over a timelike subsystem. In this work, we propose an extension of timelike entanglement entropy to Euclidean space (“temporal entanglement entropy”), and relate it to the renormalization group (RG) flow. Specifically, we show that tracing over a period of Euclidean time corresponds to coarse-graining the system and can be connected to momentum space entanglement. We employ Holography, a framework naturally embedding RG flow, to illustrate our proposal. Within cutoff holography, we establish a direct link between the UV cutoff and the smallest resolvable time interval within the effective theory through the irrelevant $T\bar{T}$ deformation. Increasing the UV cutoff results in an enhanced capability to resolve finer time intervals, while reducing it has the opposite effect. Moreover, we show that tracing over a larger Euclidean time interval is formally equivalent to integrating out more UV degrees of freedom (or lowering the temperature). As an application, we point out that the temporal entanglement entropy can detect the critical Lifshitz exponent z in non-relativistic theories which is not accessible from spatial entanglement at zero temperature and density.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Ecological connectivity and habitat loss shape patterns of genetic diversity in a threatened salamander
Context The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. Objectives We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Methods Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Results Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Conclusions Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.
Hemidactylium scutatum↗
Vortical interactions in turbulent thermoacoustic systems
This study examines the dynamics of vortical interactions and their implications for mitigating thermoacoustic instability in a turbulent combustor. The regions of intense vortical interactions are identified as vortical communities in the network space of weighted directed vortical networks constructed from two-dimensional experimental velocity data. One can expect vortical interactions in the combustor to be strongest near the moment of vortex shedding, as the shed vortices gradually weaken due to dissipation while convecting downstream. However, we show that, during the state of thermoacoustic instability, there is a non-trivial consistent phase lag of approximately 52° between the shedding of the coherent structures from the backward-facing step and the time instant when the vortical interactions attain their local maximum value. We explain this phase lag by investigating the correlation between acoustic pressure fluctuations, spatio-temporal dynamics of coherent structures and vortical interactions in the reaction field of the combustor. We also show the aperiodic variation of vortical interactions during the states of combustion noise and aperiodic epochs of intermittency. Furthermore, the spatio-temporal evolution of pairs of vortical communities with the maximum inter-community interactions provides insight into explaining the critical regions detected in the reaction field during the states of intermittency and thermoacoustic instability, also identified in previous studies. As a result, we further show that the most efficient suppression of thermoacoustic instability via air microjet injection is achieved when steady air jets are introduced to disrupt the maximum inter-community interactions present during the state of thermoacoustic instability.
Sahay, Ankit [Indian Institute of Technology Madra↗
Polarized target nuclear magnetic resonance measurements with deep neural networks
Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.
Metrology↗