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

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At least 109 records · Page 6

Exploring New Diamond Surfaces with Precision Chemistry and Quantum Spectroscopy

Point defects in diamond known as color centers are a promising platform for quantum sensing. As atom-like systems, they can exhibit excellent spin coherence and can be manipulated with light. As solid-state defects, they can be produced at high densities and be incorporated into scalable devices. Diamond is a uniquely excellent host; it has a large band gap, can be synthesized with sub-ppb impurity concentrations, and can be isotopically purified to eliminate magnetic noise from nuclear spins. Specifically, the nitrogen vacancy (NV) center has been demonstrated to be a highly sensitive, non-invasive magnetic probe capable of resolving the magnetic field of a single electron spin with nanometer spatial resolution. However, the development of NV centers and other color centers in diamond as a quantum platform crucially relies on developing methods to functionalize and process diamond surfaces. Diamond is the hardest material, making polishing and processing challenging, and diamond surfaces are inert and sterically hindered, making them notoriously difficult to chemically terminate and functionalize. Our approach is to discover new surface chemistries for diamond by combining multimodal surface spectroscopy with NV-based quantum spectroscopy to carefully characterize the diamond surface. We have developed a suite of techniques for characterizing the diamond surface using rapid tools for reaction screening and detailed surface spectroscopy tools for definitive identification of the surface bonding.

36 MATERIALS SCIENCE↗

Novel Tools for Analyzing Life Cycle Energy Use, Carbon Emissions, and Cost of Additive Manufacturing

Decarbonizing industrial manufacturing is a significant challenge in the effort to limit the impacts of global climate change. Additive manufacturing (AM) is one pathway for reducing the impacts of manufacturing as it creates parts layer-by-layer rather than by removing (i.e., subtracting) material from solid stock as with conventional techniques. This reduces material inputs and generates less waste, which can substantially lower life cycle energy consumption and greenhouse gas emissions. However, AM adoption in the manufacturing sector has been slow, partly due to challenges in making a strong business case compared with more traditional and widely available techniques. This paper highlights the need for the development of simple screening analysis tools to speed the adoption of AM in the manufacturing sector by providing decision-makers easy access to important production life cycle emissions, and cost information. Details on the development of two Microsoft Excel software tools are provided: upgrades to an existing tool on the energy and carbon impacts of AM and a new tool for analyzing the major cost components of AM. A case study applies these two tools to the production of a lightweight aerospace bracket, showing how the tools can be used to estimate the environmental benefits and production costs of AM.

GHG emissions↗

Fuel Properties and Kinetics

The Co-Optimization of Fuels and Engines (CoOptima) program is focused on a two-pronged approach to develop new, lower net-carbon fuels and advanced engine technologies in tandem. This effort brings nine national labs and several university and industry partners together to simultaneously identify new fuel candidates and to further engine technology development in the light-, medium-, and heavy-duty vehicle sectors with a focus on fuel economy and emission reduction. The fuel properties and kinetics project is aimed at achieving a better understanding of fuel property kinetics and how they impact engine performance such that more efficient and lower emitting engines can be made a reality. Several promising biofuel candidates were identified through tiered screening approaches and were evaluated utilizing unique bench-scale measurement techniques. Kinetic models were developed to describe autoignition and soot pre-cursor formation mechanisms to identify the most promising candidates that advanced various combustion strategies.

ADVANCED PROPULSION SYSTEMS↗

An automated workflow to screen alkene reductases using high-throughput thin layer chromatography

Background: Synthetic biology efforts often require high-throughput screening tools for enzyme engineering campaigns. While innovations in chromatographic and mass spectrometry-based techniques provide relevant structural information associated with enzyme activity, these approaches can require cost-intensive instrumentation and technical expertise not broadly available. Moreover, complex workflows and analysis time can significantly impact throughput. To this end, we develop an automated, 96-well screening platform based on thin layer chromatography (TLC) and use it to monitor in vitro activity of a geranylgeranyl reductase isolated from Sulfolobus acidocaldarius (SaGGR). Results: Unreduced SaGGR products are oxidized to their corresponding epoxide and applied to thin layer silica plates by acoustic printing. These derivatives are chromatographically separated based on the extent of epoxidation and are covalently ligated to a chromophore, allowing detection of enzyme variants with unique product distributions or enhanced reductase activity. Herein, we employ this workflow to examine farnesol reduction using a codon-saturation mutagenesis library at the Leu377 site of SaGGR. We show this TLC-based screen can distinguish between fourfold differences in enzyme activity for select mutants and validated those results by GC–MS. Conclusions: With appropriate quantitation methods, this workflow can be used to screen polyprenyl reductase activity and can be readily adapted to analyze broader catalyst libraries whose products are amenable to TLC analysis.

09 BIOMASS FUELS↗

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗

“Development and application of analytical detection techniques for droplet-based microfluidics”-A review

Droplet-based microfluidics has emerged as a powerful platform for high-throughput and low-volume analysis and screening. At present, droplet-based microfluidics is transitioning from the proof-of-concept stage to real-world applications. During this process, analytical detection techniques play indispensable roles for successfully implementing droplet-based chemical or biological assays. Here, we provide an overview of recent developments in analytical techniques for droplet analysis and elucidate the advantages and limitations of each technique. We cover the majority of technology categories, including optical detection, electrical detection, mass spectrometry, and nuclear magnetic resonance spectroscopy. Additionally, we highlight new research areas that have been enabled by these technical advances. Finally, we provide perspectives on both future technological directions and potential enabling applications.

47 OTHER INSTRUMENTATION↗

Local microwave sensing of excitons and their electrical environment

Excitons in atomically thin transition metal dichalcogenides (TMDs) possess intriguing optical properties, drawing interest for both technology and fundamental research. However, as the demands for nanodevice applications and the exploration of fundamental physics push toward smaller, subwavelength scales, studying them locally is challenging. In this work, we introduce a cryogenic scanning probe photoelectrical sensing technique, termed exciton-resonant microwave impedance microscopy (ER-MIM), to measure the excitonic responses in a monolayer MoSe 2 device at 1.5K. From the microwave signal changes, we identify exciton polarons and their Rydberg states. Building on these observations, we systemically reveal the local and nonlocal effects of carrier density, inhomogeneous electric fields, as well as dielectric screening on excitons, beyond the reach of conventional probes. By further integrating deep learning techniques, we precisely extracted the electrical parameters surrounding excitons, demonstrating a quantified, exciton-assisted nanoscale electrometry. Our results provide new insight into exciton-environment interactions, establish ER-MIM as a powerful optoelectronic sensing platform, and open avenues for exciton-based quantum control and device technologies.

scanning probe microscopy↗

Improving biomass and lipid yields of Desmodesmus armatus and Chlorella vulgaris through mutagenesis and high-throughput screening

Advancement of microalgae for use as biofuel feedstock depends on bioprospecting novel strains and then applying classical strain improvement techniques to enhance desirable traits. Here we successfully demonstrate this approach using two novel strains of green algae, identified as Desmodesmus armatus and Chlorella vulgaris, to improve biomass and lipid yields. UV irradiation was utilized to generate random mutations and the resulting populations were analyzed via flow cytometry to quantify lipid fluorescence while enriching for high lipid variants using fluorescence activated cell sorting. Secondary screening was performed via in situ fluorescence staining to identify the single highest lipid producing mutants from each strain. This approach produced a Desmodesmus armatus mutant that exhibited both increased biomass productivity and higher lipid content, which doubled lipid yield from the culture. The resulting Chlorella vulgaris mutant exhibited an increase in growth only, resulting in a 33% increase in lipid yield.

09 BIOMASS FUELS↗

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗

Measurement and analysis of clothes dryer air leakage

Each year clothes dryer appliances are sold globally in the tens of millions. Both vented and ventless types are common and are heated by combustion, electric resistance, or electric heat pumps. In the dyer air path, segments can be defined between components such as the drum, blower, filter, screens or grills, and heat exchangers (where applicable). In this work, a technique was developed to experimentally measure air leakage into and from the segments of a clothes dryer. Detailed leakage measurements were taken on two vented and one ventless residential clothes dryer. The measurements were quantified as a leakage flow coefficient for each segment. For two dryers (one vented and one ventless), these flow coefficients were combined with in situ operating pressure measurements to determine leakage flow rates for each segment. For these two units under normal operation with air pressures within 0.5 kPa (50 mm water column) of ambient pressure, volumetric system air leakage was found to be about 20–60% of the blower airflow. Furthermore, a quasi–steady state psychrometric analysis was conducted on vented dryers with negatively pressurized drums. The analysis revealed that leakage quantity, location, and direction are essential to achieving an acceptable energy balance and accurate modeling results for a vented heat pump clothes dryer but are of limited significance for vented electric resistance clothes dryers.

42 ENGINEERING↗

High Throughput In-Line Coating Metrology Development for Solid Oxide Fuel Cell Manufacturing

Coatings play key roles in solid oxide fuel cell (SOFC) stack durability. For example, diffusion barrier coatings on Cr-containing interconnect and balance of plant (BOP) components protect electrodes from Cr poisoning over the long operational lifetimes (>10,000 hours) of the fuel cell stack. Common defects in coatings, such as cracks, pinholes, and porosity, result in a failure to protect the electrodes, resulting in shorter operational lifetime and thus higher cost. It is very unlikely, even in the best coating process, that all these defects can be mitigated, hence identifying critical defects in parts, and removing defective parts from production before they can damage the stack, becomes paramount. Furthermore, these quality control techniques must be operational in the production/assembly line (in-line), i.e., high throughput and non-destructive, and cost effective. Redox Power Systems, LLC (Redox) together with the National Renewable Energy Laboratory (NREL) developed much needed high throughput, in-line metrology techniques for protective coatings. The overall goal of the project is to lower cost while increasing robustness, reliability, and endurance of SOFC stacks. To accomplish this, we had several objectives, including: to identify key coating and substrate defects that lead to coating failure through the use of detailed characterization methods (e.g., microscopy, XRD, EDS, electrochemistry); to assess capabilities of in-line metrology techniques, e.g., optical profilometry (Redox) and thermography (NREL), to probe these defects, or evidence thereof; demonstrate long-term performance of “defect-free” protective coatings, as identified by in-line metrology, in solid oxide fuel cell (SOFC) stack operation. In the first part of this project, the ability to identify key defects expected to lead to coating and SOFC degradation using in-line metrology tools were evaluated. Coated interconnect samples with controlled defect types and populations were tested under conditions similar to SOFC operation, followed by detailed post-test analysis to reveal the defects responsible for observed degradation. In the second part of the project, the optimal in-line metrology techniques and methodologies were used to map the defect distribution in full-size interconnects with critical defects intentionally allowed to exist in some cases. These interconnects underwent SOFC testing for extended periods (up to ~3,000 hours) followed by post-test analysis to evaluate the effectiveness of in-line metrology techniques in mitigating MCO coating related degradation. Key accomplishments in this project included the following: Demonstrated ASR of < 0.05 ohm-cm 2 at 650 °C for 1,000 hours with low defect (determined by in-line metrology) interconnect samples (average ASR=37 milliohms-cm 2 after over 1,000 hours). Demonstrated that low defect coatings on interconnects (as screened using in-line metrology) have low volatilization of chromium at ~650 °C for 1,000 hours as detected using Cr-getter material (< 5 at% increase above baseline); 1022 hour duration tests under humidified, elevated temperature (750 °C rather than 650 °C) compared a base case against different coating thicknesses. Demonstrated capability to identify initial key defects of interest with in-line metrology techniques using up to 8 cm by 10 cm having coatings with and without intentional defects of interest using thermal imaging and optical profilometry. Correlated key defects identified using metrology techniques with observed coating performance (e.g., ASR and Cr volatility). Conducted several 4 cm by 4 cm cell tests using MCO-interconnects that were pre-screened using some of the metrology techniques developed in the project (e.g., optical profilometry). An analysis of ASR measurements were able to show that defect-free coatings resulted in the anticipated performance in the cell tests.

01 COAL, LIGNITE, AND PEAT↗

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗

Serial crystallography using automated drop dispensing

Automated, pulsed liquid-phase sample delivery has the potential to greatly improve the efficiency of both sample and photon use at pulsed X-ray facilities. In this work, an automated drop on demand (DOD) system that accelerates sample exchange for serial femtosecond crystallography (SFX) is demonstrated. Four different protein crystal slurries were tested, and this technique is further improved here with an automatic sample-cycling system whose effectiveness was verified by the indexing results. Here, high-throughput SFX screening is shown to be possible at free-electron laser facilities with very low risk of cross contamination and minimal downtime. The development of this technique will significantly reduce sample consumption and enable structure determination of proteins that are difficult to crystallize in large quantities. This work also lays the foundation for automating sample delivery.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Electrochemical polymer pen lithography

Disclosed herein is a massively parallel patterning tool for the deposition of single metals or metal alloys with size and composition control. Methods of the disclosure use a hydrogel array of pyramidal pen tips as a medium for localized electrodeposition, in conjunction with a scanning probe lithography platform and a three-electrode cell. This versatile technique can be used for high-throughput 3D printing, biomolecule patterning, or screening of catalyst nanoparticles or thin films.

Mirkin, Chad A.↗

Advancing Elemental and Isotopic Analysis of Uranium Mineral Inclusions: Rapid Screening via Laser-Induced Breakdown Spectroscopy and High-Resolution Laser Ablation-ICP-MS Mapping

The work presented herein employs two laser-based analytical techniques (laser-induced breakdown spectroscopy (LIBS) and laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS)) to spatially determine the elemental and isotopic composition of uranium bearing minerals. Uniquely, this work leverages the high-speed applicability of LIBS to “screen” the sample(s) for their elemental constituents. After determining the location of the uranium inclusions (via LIBS), high-resolution LA-ICP-MS was employed to further characterize the inclusions. The high-resolution (sub-µm) capabilities of LA-ICP-MS were able to extract important information from the uranium minerals including discerning its chemical form (e.g., finchite from carnotite, Sr- and K-bearing uranyl vanadates, respectively) as well as their 235 U/ 238 U isotopic composition. This approach, LIBS followed by LA-ICP-MS, significantly reduces the analysis time (~95 %) in comparison to employing a LA-ICP-MS only approach. Furthermore, this work presented a novel approach to analyzing inclusions via a particle/inclusion analysis tool which is commercially available within the iolite 4 software. This tool allowed for a more accurate characterization of the isotopic distribution of the inclusions, as well as allowing for rapid sizing of the inclusions. Finally, this analytical approach could readily be applied to other sample types in which the target species (e.g., µm-sized inclusions) are embedded in complex matrices (e.g., cm-sized samples).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗

Thin film charged particle detectors

Silicon tracking detectors have grown to cover larger surface areas up to hundreds of square meters, and are even taking over other sub-detectors, such as calorimeters. However, further improvements in tracking detector performance are more likely to arise from the ability to make a low mass detector comprised of a high ratio of active sensor to inactive materials, where dead materials include electrical services, cooling, mechanical supports, etc. In addition, the cost and time to build these detectors is currently large. Therefore, advancements in the fundamental technology of tracking detectors may need to look at a more transformative approach that enables extremely large area coverage with minimal dead material and is easier and faster to build. The advancement of thin film fabrication techniques has the potential to revolutionize the next-to-next generation of particle detector experiments. Some thin film deposition techniques have already been developed and widely used in the industry to make LED screens for TVs and monitors. If large area thin film detectors on the order of several square meters can be fabricated with similar performance as current silicon technologies, they could be used in future particle physics experiments. This paper aims to review the key fundamental performance criteria of existing silicon detectors and past research to use thin films and other semi-conductor materials as particle detectors in order to explore the important considerations and challenges to pursue thin film detectors.

Solid state detectors↗

Ceramic Composite Experimental Testing Status

Over recent years, ceramic matrix materials such as SiC–SiC and C–C have been gaining interest for use in fusion reactors, light water reactors (LWRs), and high-temperature reactors (HTRs). These materials are good candidates to operate in very high temperature and moderate to high radiation environments. The evaluation of composite materials, in general, is challenging because of variations in precursor materials, variations in the fabrication process across fabricators, and the wide range of potential fiber architectures, to name a few. However, the need to evaluate neutron-irradiated properties adds another layer of complexity, which includes cost, timeline, and specimen size limitations (often associated with irradiation testing). A qualification methodology for the use of ceramic composites is provided in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code Section III-5-HHB. The methodology is supported by ASTM International (ASTM) guides, which provide a pathway to accomplish this effort. Part of the qualification strategy is for the designer to collect material property data on environmental conditions representative of its design envelope. These data include irradiation effects. This report presents an experimental study and test campaign developed to partially address this gap by providing initial mechanical and physical property data required for design. A variety of different materials using different manufacturing techniques are considered as part of this campaign. The test plan suggests performing a screening or partial irradiation study to assist the designer during the material selection process. The designer can then perform a more comprehensive qualification study if the material performance is promising. This work focuses on the status of the specimen preparations (machining of samples), the current test methods and failure analysis as well as the preparation of irradiation vehicles for the irradiation campaign. The irradiation will be performed at Oak Ridge National Laboratory (ORNL) in the High Flux Isotope Reactor (HFIR) and at Idaho National Laboratory (INL) in the Advanced Test Reactor (ATR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗