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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Threat Hunt Guide for BESS Environments

The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.

25 - ENERGY STORAGE↗
DOE OSTI2022

Subgrain geometrically necessary dislocation density mapping in spalled Ta in three dimensions

Here, in this work, the intragranular distributions of the densities of geometrically necessary dislocations (GNDs) generated in a polycrystalline tantalum (Ta) sample loaded in shock compression to spall are investigated. The sample studied is a highly resolved three-dimensional (3D) map of the orientations, grain boundary morphology, and voids across a 0.276mm 3 (with sample dimensions of 688.5µm x 669µm x 600µm) region of the microstructure about the spall plane obtained by TriBeam tomography. We present a formulation for calculating the crystallographic screw and edge character GND densities at a material point within body centered cubic (BCC) crystals when provided any number of potential slip systems or modes. Combining the 3D characterization, GND formulation, and a sample containing ≈ 6000 grains, we sought statistically relevant correlations between GND density per grain with grain size, grain orientation, void formation, and proximity from the spall plane. We show that GND density per grain increases with decreasing distance from the spall plane. Most grains had heterogeneously distributed GND densities but were free of any detectable voids, at least within the ≈ 1.5µm resolution of the characterization technique. It is found that the GND density for most grains is insensitive to grain size and grain orientation. Only the fraction of grains with the highest GND densities tended to have a [001] parallel to shock-direction orientation. Intragranular 3D maps of GNDs reveals that grains that contain voids have high GND density concentrations in the intragranular region immediately surrounding the void. The local intragranular GND mapping also showed voids that are connected to grain boundaries preferred to grow into the grain with the larger size, regardless of orientation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Gas puff imaging on the TCV tokamak

We present the design and operation of a suite of Gas Puff Imaging (GPI) diagnostic systems installed on the Tokamak à Configuration Variable (TCV) for the study of turbulence in the plasma edge and Scrape-Off-Layer (SOL). These systems provide the unique ability to simultaneously collect poloidal 2D images of plasma dynamics at the outboard midplane, around the X-point, in both the High-Field Side (HFS) and Low-Field Side (LFS) SOL, and in the divertor region. We describe and characterize an innovative control system for deuterium and helium gas injection, which is becoming the default standard for the other gas injections at TCV. Extensive pre-design studies and the different detection systems are presented, including an array of avalanche photodiodes and a high-speed CMOS camera. First results with spatial and time resolutions of up to ≈2 mm and 0.5 µs, respectively, are described, and future upgrades of the GPI diagnostics for TCV are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗
DOE OSTI2025

Field Insights: Strengthening Digital Assurance Through On-Site Network Monitoring

The accelerating deployment of digital energy infrastructure, ranging from inverter-based resources (IBRs), battery energy storage systems (BESS), to advanced grid control platforms, has brought unprecedented visibility, flexibility, and efficiency to the electric grid. However, this digital transformation also introduces new cybersecurity challenges, particularly in the form of supply chain risks and operational blind spots at the grid edge. Over the past year, the Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), through its Rapid Risk Assessment initiative, along with the Grid Deployment Office (GDO), through its Technical Assistance for Digital Assurance (TADA) initiative, have supported a series of on-site network engagements led by Idaho National Laboratory (INL). These engagements, conducted in partnership with asset owners across the country, have focused on identifying real-world vulnerabilities and misconfigurations in operational environments, many of which are not detectable through remote assessments or traditional compliance audits. The goal of this report is to distill key findings and lessons learned during network hunt engagements from INL’s fiscal year (FY) 2024 - 2025. It is intended to help asset owners—regardless of their participation in the program—better understand the evolving threat landscape and adopt practical measures to secure their digital energy infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

CHESS 2025: Orthorectified airborne RGB imagery from NEON AOP surveys

This dataset provides Level 1 (L1) and Level 3 (L3) orthorectified Red-Green-Blue (RGB) imagery collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). This high-resolution imagery is a photographic record of red, green, and blue visible light from sunlight reflected off of the Earth’s surface. The data comprise full-color images of the ground surface and are primarily intended to provide context to imaging spectroscopy and light detection and ranging (LiDAR) data. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. RGB images were acquired using the PhaseOne IXM-RS150F high-resolution digital camera onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). The package data include both an L1 product comprising one camera frame per file and an L3 mosaic aligned to the Universal Transverse Mercator (UTM) Zone 13N grid and the World Geodetic System (WGS) 84 projection. Both products are provided in geotif (.tif) format at 0.1 m ground resolution. The bulk of the imagery was collected during the main CHESS field campaign from June 13 to July 15, 2025. Additional images of a portion of the Upper Taylor (UPTA) domain were collected on September 18, 2025, to fill gaps in imagery identified after the main campaign was complete. RGB camera imagery is not radiometrically calibrated, and therefore pixel values should not be exploited for scientific analysis. Pixel values have undergone a manual adjustment to enhance feature identification. The imagery is rigorously geolocated which does allow for reliable geometric information to be retrieved. To generate the orthorectified imagery, the NEON AOP camera captured visible spectrum in red, green, and blue bands. The raw images were then processed using NEON’s camera orthorectification workflow. A boresight calibration flight was made to build a complete camera, distortion, and alignment model. Color balance/white balance and exposure correction were applied to the raw RGB images. The corrected images were orthorectified by ray-tracing image pixels to a lidar-derived digital surface model (DSM) mesh using the refined camera model, outputting orthorectified raster pixels on a regular grid. Flightline-level data were mosaicked by selecting per-pixel contributions from overlapping orthorectified images using line-of-sight (LOS) zenith angle minimization to reduce edge distortions. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Common Column Identification for Table Similarity Detection in Electrified Transportation Data Lakes

Electrified transportation often requires researchers and operators to interact with datasets from a wide range of sources and disciplines, such as transportation, power systems, public health, policies, and regulations. These datasets vary in quality and format, making it difficult to understand, preprocess, and identify key columns representing real-world entities or values for indexing and joining, which can negatively impact downstream analysis and operation. Existing solutions are limited, requiring extensive manual customization or data expertise to utilize. In this article, we propose a multi-layered approach to automatically identify key columns to expedite preprocessing and aid in analysis of electrified transportation data. Our method leverages a dynamic ontology to identify common fields and an information theory-based strategy for edge cases that are difficult to generalize. Evaluations on a number of datasets from data.gov and kaggle.com show improved performance of our methods over several baseline techniques, and our ablation analyses illustrate the efficacy of individual components of our method. Our case studies also demonstrate that our methods have the potential to improve analysis of electrified transportation data and aid in automatic integration of such datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

The Anatomy of an Unusual Edge-on Protoplanetary Disk. II. Gas Temperature and a Warm Outer Region

We present high-resolution {sup 12}CO and {sup 13}CO 2–1 ALMA observations, as well as optical and near-infrared spectroscopy, of the highly inclined protoplanetary disk around SSTC2D J163131.2–242627. The spectral type we derive for the source is consistent with a 1.2 M {sub ⊙} star inferred from the ALMA observations. Despite its massive circumstellar disk, we find little to no evidence for ongoing accretion on the star. The CO maps reveal a disk that is unusually compact along the vertical direction, consistent with its appearance in scattered light images. The gas disk extends about twice as far away as both the submillimeter continuum and the optical scattered light. CO is detected from two surface layers separated by a midplane region in which CO emission is suppressed, as expected from freeze-out in the cold midplane. We apply a modified version of the tomographically reconstructed distribution method presented by Dutrey et al. to derive the temperature structure of the disk. We find a temperature in the CO-emitting layers and the midplane of ∼33 K and ∼20 K at R < 200 au, respectively. Outside of R > 200 au, the disk’s midplane temperature increases to ∼30 K, with a nearly vertically isothermal profile. The transition in CO temperature coincides with a dramatic reduction in the submicron and submillimeter emission from the disk. We interpret this as interstellar UV radiation providing an additional source of heating to the outer part of the disk.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A microwave SQUID multiplexer optimized for bolometric applications

A microwave SQUID (superconducting quantum interference device) has been optimized for reading out large arrays of superconducting transition-edge sensor (TES) bolometers. We present the scalable cryogenic multiplexer chip design that may be used to construct an 1820-channel multiplexer for the 4-8 GHz rf band. The key metrics of yield, sensitivity, and crosstalk are determined through measurements of 455 readout channels, which span 4-5 GHz. The median white-noise level is 45 pA/ $\sqrt{Hz}$, evaluated at 2 Hz, with a 1/f knee ≤ 20 mHz after common-mode subtraction. The white-noise level decreases the sensitivity of a TES bolometer optimized for detection of the cosmic microwave background at 150 GHz by only 3%. The measured crosstalk between any channel pair is ≤ 0.3%.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

Materials science↗
DOE OSTI2020

Higher-dimensional generalizations of Berry curvature

A family of finite-dimensional quantum systems with a nondegenerate ground state gives rise to a closed two-form on the parameter space, the curvature of the Berry connection. Its integral over a surface detects the presence of degeneracy points inside the volume enclosed by the surface. In this work, we seek generalizations of the Berry curvature to gapped many-body systems in $\textit{D}$ spatial dimensions which can detect gapless or degenerate points in the phase diagram of a system. Field theory predicts that in spatial dimension $\textit{D}$ the analog of the Berry curvature is a closed ($\textit{D}$ + 2) -form on the parameter space (the Wess-Zumino-Witten form). We construct such closed forms for arbitrary families of gapped interacting lattice systems in all dimensions. We show that whenever the integral of the Wess-Zumino-Witten form over a ($\textit{D}$ + 2)-dimensional surface in the parameter space is nonzero, there must be gapless edge modes for at least one value of the parameters. These edge modes arise even when the bulk system is in a trivial phase for all values of the parameters and are protected by the nontrivial topology of the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Near-Monochromatic Tuneable Cryogenic Niobium Electron Field Emitter

We report creating, manipulating, and detecting coherent electrons is at the heart of future quantum microscopy and spectroscopy technologies. Leveraging and specifically altering the quantum features of an electron beam source at low temperatures can enhance its emission properties. Here, we describe electron field emission from a monocrystalline, superconducting niobium nanotip at a temperature of 5.9 K. The emitted electron energy spectrum reveals an ultranarrow distribution down to 16 meV due to tunable resonant tunneling field emission via localized band states at a nanoprotrusion’s apex and a cutoff at the sharp low-temperature Fermi edge. This is an order of magnitude lower than for conventional field emission electron sources. The self-focusing geometry of the tip leads to emission in an angle of 3.7°, a reduced brightness of 3.8 × 10 8 A/(m 2 srV), and a stability of hours at 4.1 nA beam current and 69 meV energy width. This source will decrease the impact of lens aberration and enable new modes in low-energy electron microscopy, electron energy loss spectroscopy, and high-resolution vibrational spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deconvolution of X-ray natural and magnetic circular dichroism in chiral Dy-ferroborate

Structural chirality and magnetism, when intertwined, can have profound implications on materials properties. Using X-ray imaging and spectroscopic measurements that leverage the natural and magnetic circular dichroic effects present in magnetized chiral crystal structures, we probe the interplay between chirality and magnetism across the field-induced spin-flop transition of Dy ferroborate, DyFe 3 (BO 3 ) 4 . Deconvolution of natural and magnetic circular dichroic signals at the Fe K and Dy L 2,3 absorption edges of the non-centrosymmetric structure was enabled by use of tunable temperature and magnetic field, providing access to element-specific magnetic information across the spin-flop transition. The magnetic response of Fe and Dy sublattices was found to be independent of domain chirality. The chiral domains were robust against both the (chirality preserving) R32 to P3 1 21/P3 2 21 structural phase transition at 280 K, and application of magnetic field up to 4 Tesla. A third flavor of X-ray dichroism, magneto-chiral dichroism, was not detected within the accuracy of our measurements. The absence of significant Fe magnetization along the screw, c-axis for the magnetic field strength used in this study, together with non-linear coupling of magnetic field to electric polarization across the spin-flop transition, may hinder observation of magneto-chiral dichroic effects in this system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Observation of anomalous thermal Hall effect in a Kagome superconductor

Broken time-reversal symmetry (TRS) in superconductors can induce not only spontaneous magnetization by the finite angular momentum of Cooper pairs but also the anomalous thermal Hall effects (ATHEs), whose detection has been extremely challenging. Here, we report the successful observation of an ATHE developing below the superconducting transition temperature at zero magnetic field in the kagome-lattice superconductor CsV 3 Sb 5 . This finding is verified by the absence of a signal in a conventional type-II superconductor using the same setup and by ruling out the trapped-vortex effects through micro-Hall array measurements. Both the temperature dependence and the magnitude of the observed anomalous thermal Hall conductivity are quite different from those expected for the quantized thermal edge current of an intrinsic ATHE but consistent with extrinsic impurity-induced ATHEs in chiral superconductivity. Our study of ATHE offers an alternative approach to probe TRS breaking in the superconducting states.

Yoshida, Hiroki [Univ. of Tokyo, Chiba (Japan)] (O↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2023

Measurements of Magnetic Field Penetration of Materials for Superconducting Radiofrequency Cavities

Superconducting Radio Frequency (SRF) cavities used in particle accelerators are typically formed from or coated with superconducting materials. Currently high purity niobium is the material of choice for SRF cavities which have been optimized to operate near their theoretical field limits. This brings about the need for significant R&D efforts to develop next generation superconducting materials which could outperform Nb and keep up with the demands of new accelerator facilities. To achieve high quality factors and accelerating gradients, the cavity material should be able to remain in the superconducting Meissner state under high RF magnetic field without penetration of quantized magnetic vortices through the cavity wall. Therefore, the magnetic field at which vortices penetrate in a supercon- ductor is one of the key parameters of merit of SRF cavities. Techniques to measure the onset of magnetic field penetration on thin film samples need to be developed to mitigate the issues with the conventional magnetometry measurements which are strongly influenced by the film orientation and shape and edge effects. The applied magnetic field is also needed to be parallel to the one side of the superconductor to resemble the magnetic field profile at the surface of the SRF cavities operating at fundamental accelerating mode. In this work we report the development of an experimental setup called Magnetic Field Penetration (MFP) magnetometer to measure the field of full flux penetration through bulk, thin films and multilayered superconductors. Our system combines a small superconducting solenoid which can generate the magnetic field up to 500 mT at the sample surface and three Hall probes to detect the full flux penetration through the planner superconductor with 2 inch diameter. This setup was used to study alternative materials which could potentially outperform niobium, as well as SIS multilayer coatings on niobium.

Senevirathne, Iresha↗