Results for “range degradation” — Page 3

DOE OSTI2020

Antifoam Development for Eliminating Flammability Hazards and Decreasing Cycle Time in the Defense Waste Processing Facility

The Savannah River National Laboratory (SRNL) was requested to develop a new antifoam control method for the Defense Waste Processing Facility’s (DWPF) Chemical Process Cell (CPC). SRNL completed testing of both chemical and nonchemical foam controls. The nonchemical foam controls were either ineffective (or worse, created more foam) or impractical (a water spray can control foam, but excessive water is needed). As a result, the focus of this study was on finding a superwetter or commercial antifoam for controlling foam. Thirty potential antifoams were tested as part of this study. A series of tests were developed to help screen out ineffective alternatives including: 1. Spreading testing of superspreaders, 2. Foam column testing with physical simulants, 3. Boiling testing with physical and chemical simulants, 4. Days-only Sludge Receipt and Adjustment Tank (SRAT) process simulations with sludge (containing noble metals and mercury), Precipitate Reactor Feed Tank (PRFT), and Slurry Mix Evaporator Feed Tank (SEFT) simulants in the RC1 Reaction Calorimeter (purchased for antifoam testing), and 5. Around-the-clock SRAT and Slurry Mix Evaporator (SME) process simulations with sludge(containing noble metals and mercury), PRFT, and SEFT simulants in the RC1 Reaction Calorimeter. Evonik Surfynol® MD20, a commercially available defoamer, was relatively effective in controlling foam, while remaining chemically stable in SRAT and SME processing across the pH range of 4 to 13. No degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. In nitric-glycolic acid flowsheet testing, 250 mg/kg Evonik Surfynol® MD20 was needed for foam control compared to 1,625 mg/kg for Antifoam 747, DWPF’s current antifoam. In nitric-formic acid flowsheet testing, 1,125 mg/kg of Evonik Surfynol® MD20 was needed to control foam throughout the SRAT and SME cycles. The commercially available superspreader Momentive™ Y-17112 was even more effective than Evonik Surfynol® MD20 as both a defoamer and an antifoam. Not only was the foam destroyed upon addition but also was less persistent between additions. It was the most effective antifoam in testing using both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. In nitric-glycolic acid flowsheet testing, only 100 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. In nitric-formic acid flowsheet testing, 300 mg/kg Momentive™ Y-17112 was needed to control foam throughout the SRAT and SME cycles. Momentive™ Y-17112 is also resistant to hydrolysis as demonstrated by its chemical stability in SRAT and SME processing across the pH range of 4 to 13 and lack of degradation products in offgas or condensate. Both candidates were effective as potential replacements for Antifoam 747, with Y-17112 demonstrating superior foam control. During nitric-glycolic flowsheet testing 50% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. During nitric-formic flowsheet testing 75% less antifoam was needed when using Momentive™ Y-17112 compared to MD20. Foam remediated with Momentive™ Y-17112 was less persistent throughout testing. In addition, no degradation products were detected in the offgas, in the condensate or in the SRAT and SME products. Based on this testing, Momentive™ Y-17112 is clearly superior to Evonik Surfynol® MD20 and Antifoam 747, especially for the nitric-formic acid flowsheet processing; it is recommended that Momentive™ Y-17112 replace Antifoam 747 in DWPF. An antifoam addition strategy is recommended for both the nitric-glycolic acid flowsheet and the nitric-formic acid flowsheet. Implementation of Momentive™ Y-17112 is expected to decrease SRAT and SME boiling times by up to 50%, eliminate the issues resulting from Antifoam 747 degradation products, and minimize foamovers. To validate the effectiveness of these defoaming agents, SRNL recommends irradiation of a SRAT or SME product simulant containing fresh antifoam. The goal of this testing is to determine whether the irradiation causes decomposition of the antifoam that would make it less effective or produce new species in the offgas or slurry. This testing began in April 2020. An evaluation should be completed to determine the thermolytic hydrogen and methane generation rate in downstream equipment, including the High-Level Waste evaporators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Anaerobic Degradation of Alkanes by Marine Archaea

Alkanes are saturated apolar hydrocarbons that range from their simplest form, methane, to high-molecular-weight compounds. Although alkanes were once considered biologically recalcitrant under anaerobic conditions, microbiological investigations have now identified several microbial taxa that can anaerobically degrade alkanes. Here we review recent discoveries in the anaerobic oxidation of alkanes with a specific focus on archaea that use specific methyl coenzyme M reductases to activate their substrates. Our understanding of the diversity of uncultured alkane-oxidizing archaea has expanded through the use of environmental metagenomics and enrichment cultures of syntrophic methane-, ethane-, propane-, and butane-oxidizing marine archaea with sulfate-reducing bacteria. A recently cultured group of archaea directly couples long-chain alkane degradation with methane formation, expanding the range of substrates used for methanogenesis. This article summarizes the rapidly growing knowledge of the diversity, physiology, and habitat distribution of alkane-degrading archaea.

Microbiology↗

DOE OSTI2022

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Abstract While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Nondestructive Evaluation of Carbon Fiber Reinforced Polymers

The American Society of Mechanical Engineers Boiler and Pressure Vessel Code requires repair and replacement of safety-related piping materials to meet the original Construction Code; however, no construction criteria currently exist for carbon fiber reinforced polymer (CFRP) materials in the nuclear industry. Although nondestructive examination (NDE) techniques for cast and wrought ferritic and austenitic steels are well established, licensees are increasingly deploying novel materials such as CFRP for which assessment of the use of NDE is required, as the inspectability of such materials and the influence of manufacturing processes on inspectability remain insufficiently understood. To address these gaps, Pacific Northwest National Laboratory (PNNL) evaluated the fabrication of several CFRP repair mockups and the effectiveness of NDE methods to support regulatory review of CFRP repairs in nuclear power plant applications. Representative flat-plate mockups containing realistic fabrication defects were manufactured and inspected using manual and automated tap testing, dynamic response spectroscopy (DRS), and ultrasonic testing (UT). The study identified significant fabrication variability, particularly in controlling defect size and epoxy thickness, which strongly influenced defect detectability by NDE methods. Tap testing was effective for shallow defects in thin epoxy configurations but unreliable for thicker repairs and deeper flaws. DRS demonstrated higher sensitivity to dry spots but produced unconfirmed indications for thicker plates, requiring further validation. Conventional UT, particularly at 1.0 MHz, showed the strongest overall capability for detecting a range of defects, although performance degraded with increasing repair thickness and complexity. The results highlight the need to better define critical defect sizes, develop reliable mockup fabrication methods, and validate NDE methods needed to support CFRP repairs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2023

Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository

Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Further, using fuel cask temperature, dose rate, and the environmental concentrations of CO 3 2- , O 2 , Fe 2+ , and H 2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO 2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Development of Human Carbonic Anhydrase II Heterobifunctional Degraders

Human carbonic anhydrase II (hCAII) is a metalloenzyme essential to critical physiological processes in the body. hCA inhibitors are used clinically for the treatment of indications ranging from glaucoma to epilepsy. Targeted protein degraders have emerged as a promising means of inducing the degradation of disease-implicated proteins by using the endogenous quality control mechanisms of a cell. Here, a series of heterobifunctional degrader candidates targeting hCAII were developed from a simple aryl sulfonamide fragment. Degrader candidates were functionalized to produce either cereblon E3 ubiquitin ligase (CRBN) recruiting proteolysis targeting chimeras (PROTACs) or adamantyl-based hydrophobic tags (HyTs). Screens in HEK293 cells identified two PROTAC small-molecule degraders of hCA. Optimization of linker length and composition yielded a degrader with sub-nanomolar potency and sustained depletion of hCAII over prolonged treatments. Mechanistic studies suggest that this optimized degrader depletes hCAII through the same mechanism as previously reported CRBN-recruiting heterobifunctional degraders.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Comparative Genomics and Transcriptomics Analyses Reveal Divergent Plant Biomass-Degrading Strategies in Fungi

Plant biomass is one of the most abundant renewable carbon sources, which holds great potential for replacing current fossil-based production of fuels and chemicals. In nature, fungi can efficiently degrade plant polysaccharides by secreting a broad range of carbohydrate-active enzymes (CAZymes), such as cellulases, hemicellulases, and pectinases. Due to the crucial role of plant biomass-degrading (PBD) CAZymes in fungal growth and related biotechnology applications, investigation of their genomic diversity and transcriptional dynamics has attracted increasing attention. In this project, we systematically compared the genome content of PBD CAZymes in six taxonomically distant species, Aspergillus niger, Aspergillus nidulans, Penicillium subrubescens, Trichoderma reesei, Phanerochaete chrysosporium, and Dichomitus squalens, as well as their transcriptome profiles during growth on nine monosaccharides. Considerable genomic variation and remarkable transcriptomic diversity of CAZymes were identified, implying the preferred carbon source of these fungi and their different methods of transcription regulation. In addition, the specific carbon utilization ability inferred from genomics and transcriptomics was compared with fungal growth profiles on corresponding sugars, to improve our understanding of the conversion process. This study enhances our understanding of genomic and transcriptomic diversity of fungal plant polysaccharide-degrading enzymes and provides new insights into designing enzyme mixtures and metabolic engineering of fungi for related industrial applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Analysis of Microwave Resonant Cavity Transducer Degradation Mechanisms

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with the flat wall of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the resonant frequency, which can be detected with a spectrum analyzer. Because the transducer is intended for immersion in a high temperature corrosive fluid, understanding of material degradation is crucial for estimation of transducer performance lifetime, and development of measurement interpretation algorithms. We conducted a preliminary computational investigation of relevant damage mechanisms of a stainless steel 316 cylindrical resonator in FLiBe salt. The two main damage mechanisms, creep and corrosion, were modeled using multiphysics COMSOL software. Degradation was modeled for a temperature range 500°C to 700°C. Coupling of the damage mechanisms was not considered. These models predict significant inelastic deformation at most temperatures due to creep, and qualitatively predict chromium depletion both along the liquid/solid interface and along the grain boundaries. An algorithmic approach for compensation of these degradation effects during fluid flow measurements will be developed in the future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Second Annual Report on Development of Microwave Resonant Cavity Transducer for Fluid Flow Sensing

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with the flat wall of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the resonant frequency, which can be detected with a spectrum analyzer. Because the transducer is intended for immersion in a high temperature corrosive fluid, understanding of material degradation is crucial for estimation of transducer performance lifetime, and development of measurement interpretation algorithms. We conducted a preliminary computational investigation of relevant damage mechanisms of a stainless steel 316 cylindrical resonator in FLiBe salt. The two main damage mechanisms, creep and corrosion, were modeled using multiphysics COMSOL software. Degradation was modeled for a temperature range 500°C to 700°C. Coupling of the damage mechanisms was not considered. These models predict significant inelastic deformation at most temperatures due to creep, and qualitatively predict chromium depletion both along the liquid/solid interface and along the grain boundaries. An algorithmic approach for compensation of these degradation effects during fluid flow measurements will be developed in the future work. To validate sensor physics, we have performed proof-of-principle test of flow sensing in water. For this test, we have developed a cylindrical resonator for K-band, which was machined from brass. The cavity was excited through WR-42 waveguide through a subwavelength hole on the side of the wall of the cylinder. To increase the spectral signal visibility, we developed a signal processing method for baseline subtraction. A flow loop for proof-of-principle test of transducer performance in water was assembled. A commercial flow meter was installed in the loop for reference measurements. Cylindrical cavity was excited in the TE 011 mode with resonant frequency f ≈ 17.8GHz. Frequency shift of cavity spectral response was obtained by gradually increasing water flow rate from 0 to 60gpm. Corresponding monotonic increase of resonant frequency shift by several MHz was observed. Approximate figure of merit of sensitivity to flow rate is 100KHz/GPM. In addition, we have identified an existing liquid sodium experimental setup for demonstration of flow sensing in environment similar to that of an advanced reactor. The setup consists of a cylindrical vessel and center feed line, where transducer inserted through the lid will measure velocity of the impinging liquid jet. As a calibration experiment, we have assembled a water vessel with center feed with the same dimensions as those of the liquid sodium setup. We have also developed and insertion probe consisting of a 50cm brass waveguide enclosed in protective SS316 tube. Using the water loop, we have demonstrated feasibility of sensing the impinging liquid jet in the vessel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Photocatalytic degradation of orange II dye via high pore volume polymorph zirconium-doped titania under visible light irradiation

A series of mixed-phase Zr-doped TiO 2 was utilized for the photocatalytic degradation of orange II dye. These materials were synthesized through a sol–gel technique by varying the amounts of zirconium in the photocatalysts. These materials were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray fluorescence (XRF), Raman spectroscopy, thermogravimetry analysis (TGA), and X-ray photoelectron spectroscopy (XPS) among others. XRF and XPS data showed that the elemental composition of the photocatalysts consists of zirconium, titanium, and oxygen. In addition, XRD and Raman spectroscopy confirmed the existence of anatase and rutile phases of TiO 2 . The TGA analyses showed that the undoped TiO 2 was the most stable, with no significant weight loss during the heating range. Furthermore, the materials can effectively degrade orange II dye solution up to 7 cycles through asymmetric cleavage of the azo bond.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Acoustic Codes in 2D Spherical Coordinate

Finite-difference methods are widely used to simulate infrasound propagation in the atmosphere. Flexibility of finite-difference scheme allows implementation of highly heterogeneous media for sound propagation as well as complex source models for sound generation. While full 3-D finite-difference methods have been utilized for local infrasound propagation with pronounced topography, 2-D modeling approach has been preferred for regional and global propagation as full 3-D methods generally require enormous computational resources. Infrasound propagation is often simulated with a second-order finite difference scheme. This lowest-order finite-difference scheme is robust and straightforward to implement complex boundary conditions, but the solution includes large error with numerical dispersion and dissipation. This large numerical error may make the second-order finite-difference not optimal for long range infrasound propagation modeling as the numerical dispersion degrades the accuracy of the solution unacceptably. Here, we developed a high-order finite-difference solver for long-range infrasound simulation. The high-order scheme is particularly popular for linear wave modeling in aeroacoustics owing to its low-dispersive and low-dissipative behavior. We develop and evaluate a high-order finite difference scheme in 2-D axisymmetric coordinates. The axisymmetry allows to approximate 3-D spherical sound propagation and amplitude attenuation by a 2-D method. AC2Dr is developed to simulate infrasound propagation in realistic atmosphere, but can be used for linear acoustic waves in general materials with background flow. AC2Dr in an axisymmetric coordinates allows for spherical radiation of acoustic waves from compact sources.

Sjogreen, Bjorn↗

DOE OSTI2026

UV-Induced Degradation and Associated Metastability in TOPCon Photovoltaic Modules: Understanding Kinetics and Cell Variance

Tunnel oxide passivated contact (TOPCon) silicon photovoltaic (PV) modules are dominating the PV market, but they may be susceptible to degradation under ultraviolet (UV)-containing light. Quantifying the impacts of UV-induced degradation (UVID) is complicated by an associated metastability causing further degradation under dark storage and rapid recovery under sunlight. Here, we study modules that have -2.3% to -3.2% nonrecoverable UVID loss after 60 kWh/m2 dose of 340 nm light and additional recoverable loss under dark storage. We use in situ electroluminescence (EL) imaging to characterize the post-UVID metastability at the module level. The cell-by-cell dark degradation and recovery kinetics span a wide range from +6% to -70% changes in EL intensity after 520 h of dark storage, which returns to +- 4% of the initial post-UVID EL intensity after illumination. The kinetics follow double exponential rates with dark storage degradation time constants of 345 and 45 h, and UV light recovery time constants of 5 min and 36 s. We propose that this is consistent with prior reports of kinetics for light-soaking metastability in Al2O3 passivation. Finally, we further show that cells having high UVID also have injection-dependent effective carrier lifetimes and significant intra-cell variance, suggesting possible origins of processing inconsistency.

14 SOLAR ENERGY↗

DOE OSTI2024

One hundred priority questions for advancing seagrass conservation in Europe

Societal Impact Statement Seagrass ecosystems are of fundamental importance to our planet and wellbeing. Seagrasses are marine flowering plants, which engineer ecosystems that provide a multitude of ecosystem services, for example, blue foods and carbon sequestration. Seagrass ecosystems have largely been degraded across much of their global range. There is now increasing interest in the conservation and restoration of these systems, particularly in the context of the climate emergency and the biodiversity crisis. The collation of 100 questions from experts across Europe could, if answered, improve our ability to conserve and restore these systems by facilitating a fundamental shift in the success of such work. Summary Seagrass meadows provide numerous ecosystem services including biodiversity, coastal protection, and carbon sequestration. In Europe, seagrasses can be found in shallow sheltered waters along coastlines, in estuaries & lagoons, and around islands, but their distribution has declined. Factors such as poor water quality, coastal modification, mechanical damage, overfishing, land‐sea interactions, climate change and disease have reduced the coverage of Europe’s seagrasses necessitating their recovery. Research, monitoring and conservation efforts on seagrass ecosystems in Europe are mostly uncoordinated and biased towards certain species and regions, resulting in inadequate delivery of critical information for their management. Here, we aim to identify the 100 priority questions, that if addressed would strongly advance seagrass monitoring, research and conservation in Europe. Using a Delphi method, researchers, practitioners, and policymakers with seagrass experience from across Europe and with diverse seagrass expertise participated in the process that involved the formulation of research questions, a voting process and an online workshop to identify the final list of the 100 questions. The final list of questions covers areas across nine themes: Biodiversity & Ecology; Ecosystem services; Blue carbon; Fishery support; Drivers, Threats, Resilience & Response; Monitoring & Assessment; Conservation & Restoration; Governance, Policy & Management; and Communication. Answering these questions will fill current knowledge gaps and place European seagrass onto a positive trajectory of recovery.

Nordlund, Lina Mtwana↗

DOE OSTI2025

Low two-level-system noise in hydrogenated amorphous silicon

At sub-Kelvin temperatures, two-level systems (TLSs) present in amorphous dielectrics source a permittivity noise, degrading the performance of a wide range of devices using superconductive resonators such as qubits or kinetic inductance detectors. We report here on measurements of TLS noise in hydrogenated amorphous silicon (a-Si:H) films deposited by plasma-enhanced chemical vapor deposition in superconductive lumped element resonators using parallel-plate capacitors. In conclusion, the TLS noise results presented in this article for two recipes of a-Si:H improve on the best results achieved in the literature by a factor >5 for a-Si:H and other amorphous dielectrics and are comparable to those observed for resonators deposited on crystalline dielectrics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Effect of Low Temperature Nitrogen Baking on Superconducting Radio Frequency Cavities With Different Frequency

We report the rf performance of several single-cell superconducting radiofrequency cavities frequency ranging from 1.3-3.0 GHz after low temperature baking in nitrogen environment The cavities are treated at different temperature in the range of (120-160oC) for extended period of time (~24-48 hours) with nitrogen gas injection in the furnace. The improvement in Q0 with Q-rise in some case was observed when nitrogen gas was injected at elevated temperature (~250-290 oC) and held at the temperature range 120-200 oC without any degradation in accelerating gradient over the baseline performance. The temperature and time of baking play significant role in dependence of quality factor on accelerating gradient.

Khanal, B.↗

DOE OSTI2021

Forged Ingot Niobium Technology for Accelerator Applications and Scientific Frontiers

We report the rf performance of several single-cell superconducting radiofrequency cavities frequency ranging from 1.3-3.0 GHz after low temperature baking in nitrogen environment The cavities are treated at different temperature in the range of (120-160oC) for extended period of time (~24-48 hours) with nitrogen gas injection in the furnace. The improvement in Q0 with Q-rise in some case was observed when nitrogen gas was injected at elevated temperature (~250-290 oC) and held at the temperature range 120-200 oC without any degradation in accelerating gradient over the baseline performance. The temperature and time of baking play significant role in dependence of quality factor on accelerating gradient.

Khanal, B.↗