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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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Fabricating structural components for a spent nuclear fuel container using the steps of forming cylindrical or rectangular channels to produce a structural component for a spent nuclear fuel container and applying a coating that includes tantalum-based material to the cylindrical or rectangular channels.
Abstract The Mu2e experiment at Fermilab will search for the CLFV neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the evidence of physics beyond the Standard Model. Mu2e comprises a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides electron identification, a fast trigger and aids track reconstruction. It is a state-of-the-art crystal calorimeter and employs 1340 pure CsI crystals readout by UV-extended SiPM and fast electronics. The design consists of two identical annular disks positioned at the relative distance of 70 cm downstream the target.The hostile Mu2e conditions (total ionizing dose of 12 krad and a neutron fluence of 5 × 10 10 n/cm 2 @ 1 MeVeq (Si)/y, 1 T magnetic field and vacuum level of 10 −4 Torr) posed tight constraints on the mechanical structures and materials choice. The support structure of the two crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. SiPMs and front-end electronics for each crystal are assembled in one mechanical unit inserted in a machined copper holder. The units are supported by a plate made of vacuum-compatible material. The plate integrates the cooling system made of a network of copper lines flowing a low temperature fluid and placed in thermal contact with the copper holders. The DAQ is hosted in aluminum crates positioned on the lateral surface of the disks. The crates also integrate the DAQ electronics cooling system. We review the constraints on the calorimeter structures design, the development of all the structural components, including the simulations that have determined the materials and technological choices and the specifications of the cooling station, components production and quality assurance tests, the procedures for detector assembly, transportation and installation in the experimental area.
THz scattering-type scanning near-field microscopy (s-SNOM) has become a powerful technique for measuring carrier dynamics in nanoscale materials and structures. Changes in a material’s local THz reflection or transmission can be correlated to changes in electrical conductivity. Here, we perform tip-based THz nano-imaging of subwavelength gold nanostructures and demonstrate image contrast unrelated to any spatially varying material properties. We show that the specific physical configuration of the gold structures can have a strong influence on local excitations which can obscure the sample’s true dielectric response, even in cases where the relevant structures are far outside of the spatial region probed by the AFM tip.
Embodiments provide a nanoparticle and a method for preparing the nanoparticle, as well as a membrane that includes the nanoparticle and a fuel cell that includes the membrane. The method comprises a thermal treatment method that provides from a nanoparticle comprising a structurally disordered material the nanoparticle comprising: (1) a structurally ordered core comprising a first material; and (2) a shell surrounding and further structurally aligned with the structurally ordered core and comprising a second material different from the first material. Particularly desirable is a nanoparticle comprising a Pt3Co@Pt/C structurally ordered core-shell composition supported upon a carbon support.
The need to produce complex geometries incompatible with traditional manufacturing techniques has fueled rapid growth in Large-Format Additive Manufacturing (LFAM). Printing of polymer composite materials have generated significant interest, but the production of Multi-Material (MM) structures with location-based material properties continues to be a challenge. Extrusion-based techniques have utilized multiple deposition heads to successfully print MM structures with both stiff and flexible regions, but these techniques often result in discrete material boundaries that concentrate stress and act as failure points. To avoid discrete interfaces, a novel dual-hopper configuration was developed for the Big Area Additive Manufacturing (BAAM) system that creates a blended material region within the structure. The ability to blend and freely switch between stiff polymer composites and flexible polymers enables printing of robust MM structures with site-specific properties. This study characterizes the influence of viscosity on a blended material transition between carbon fiber-filled acrylonitrile butadiene styrene (CF-ABS) and unfilled thermoplastic polyurethane (TPU), which have significantly different viscoelastic behaviors
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
A molecular crystal structure prediction workflow, based on the random structure generator, Genarris, and the genetic algorithm (GA), GAtor, is successfully applied to two energetic materials and a chiral arene.
The use of subsize specimens in nuclear materials testing has been a subject of ongoing interest due to radiation safety concerns and resource conservation needs. It is also of interest in additive manufacturing (AM), also known as 3D-printing, as subsize specimens can more accurately represent the behavior of the small, intricate geometries often produced using AM. A novel, extremely small geometry, called the Subsize Teeny (SST) was recently developed and is of interest for implementation. Given its extraordinarily small size and the complexities associated with subsize specimen testing, adequate vetting of this geometry is necessary to ensure data quality. Here, a variety of unirradiated nuclear structural materials were tested in the SST geometry and compared against the well-established SSJ3 geometry. In addition, two case studies implementing the SST as a screening geometry for AM materials were also conducted. The question of SST viability was found to be highly nuanced and will often be dependent on the context or application in question. It was determined, however, that the SST is a largely invalid geometry for exceptionally coarse-grained materials or in cases where the physical defect volume equals or exceeds 0.1 % or where the specimen machining parameters result in significant surface alterations. On the other hand, it was determined that the SST may be employed with confidence if the test material is nearly or totally free of physical defects, isotropic, demonstrates homogeneous plastic deformation, and possesses a fine-grained, nearly or totally homogeneous microstructure with at least twelve slip systems.
The muon-to-electron conversion (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1348 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named disks ) positioned at the relative distance of 70 cm, downstream the aluminum target along the muon beamline, satisfies the Mu2e physics requirements. The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5 10^{10} n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10−4 Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 674 crystal matrices employs two aluminum hollow rings and parts made of open- cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 674 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal con- tact with the copper holders. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system. In this poster we will review the constraints on the calorimeter mechanical structures, the technological choices, and the status of assembling at Fermilab.
The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides excellent electron identification, complementary information to aid pattern recognition and track reconstruction, and a fast calorimetric online trigger. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1340 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements.The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5x1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10$^{-4}$ Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 670 crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 670 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system.The constraints on the calorimeter mechanical structures design, the development from the conceptual design to the specifications of all the structural components, the status of components production and the components quality assurance tests are presented.
Material design and accessible manufacturing are often at odds with each other, calling for creative solutions to adapt high-performance materials to available processes. This challenge is represented well by in-mold electronics, an innovative approach to the manufacture of 3D circuitry and electronic components that offers game-changing advantages. In-mold electronics relies on vacuum forming processes, which are historically limited to thermoplastics. Extending these methods to include thermosets would enable manufacturing of robust components with desirable properties. Here, we provide a solution to make thermoset materials amenable to vacuum forming. Specifically, an ambient polymerization is used to transition a liquid monomeric solution to an elastomeric gel. These free-standing gels can then be vacuum formed, and the reaction can be completed via frontal polymerization. Thermoset materials produced with this method have properties that provide benefits over traditionally employed thermoplastic substrates and enable 3D device integration into environmentally demanding architectural, automotive, and extraterrestrial structures.
We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.
Abstract Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly proposed model predicts dislocation densities consistent with estimates from eCMWP. Advantageously, this data driven LPA model can distinguish broadening originating from the instrument and from the dislocation content even at low dislocation densities. Finally, the data-driven model is used to explore the effect of heterogeneous dislocation densities in microstructures containing grains, which may lead to more accurate data-driven predictions of dislocation density in plastically deformed polycrystals.
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Project is 13 months into the 30 month on complete project plan. We are continuing effort on 3 main technical thrusts: algorithm development, multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In FY20 we underspent the initial budget, partly due to work restrictions from Covid-19 pandemic, but are working to make up lost time
Tardigrade is an effort to create small, passive remote sensor for material detection and identification. The microbolometer based sensor design uses custom bandpass filter bands combined with tuned thermal expansion materials in a ruggedized lens housing to provide passive mechanical correction for optical changes across a wide temperature range. This requires developing computational tools to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Current efforts in the project are in 3 areas: material design algorithm development (transitioning into full 3D material design space), multi-material additive manufacturing process exploration, and sensor/optics design and engineering.
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing.
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing.
Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing.