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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ROADRUNNER uranium nitride MiniFuel: Experimental design, fabrication and pre-irradiation baseline characterization for accelerated burnup testing

Uranium nitride (UN) is a promising fuel candidate for advanced reactor systems owing to its high uranium density and thermal conductivity; however, its qualification remains constrained by the scarcity of well-controlled irradiation performance data. Here, to address this limitation, the ROADRUNNER (Research On ADvancing the peRformance of UraNium Nitrides in Extreme enviRonments) campaign employs the MiniFuel platform in the High Flux Isotope Reactor (HFIR) to enable accelerated burnup irradiation testing under tightly controlled and largely isothermal conditions. This paper presents the experimental design, fuel fabrication, and pre-irradiation baseline characterization of the ROADRUNNER UN MiniFuel campaign. Thirty-six UN minidisc specimens were fabricated with systematically varied as-fabricated density (86–96% of theoretical density), carbon impurity content (961–5240 ppm), oxygen content (≤ ∼2000 ppm), and grain size (2.5–24 μm). The irradiation matrix spans nominal fuel temperatures of 873 K, 1173 K, and 1473 K and target burnups of 3.75%, 6.0%, and 7.5% fissions per initial metal atom (FIMA). Neutronic and thermal analyses were performed to define specimen-specific burnup accumulation and temperature histories, establishing the boundary conditions for subsequent in-pile behavior. Comprehensive pre-irradiation characterization—including dimensional metrology, density verification, impurity analysis, X-ray diffraction, Raman spectroscopy, scanning electron microscopy, X-ray computed tomography, and confocal profilometry—provides a detailed baseline for post-irradiation examination. Pre-irradiation data were further used to generate predictive estimates of fission gas release and swelling using existing empirical correlations. This quantitative comparison reveals substantial inter-model divergence at intermediate and elevated temperatures that exceeds propagated input uncertainties, highlighting structural gaps in the historical irradiation database. The ROADRUNNER irradiation campaign is currently underway in HFIR, with initial firs cycle completed in late 2025 and remaining targets scheduled through 2027. The experimental design and baseline dataset presented here establish the framework needed to interpret forthcoming post-irradiation measurements and to provide discriminating data for the validation and refinement of physics-based UN fuel performance models.

Lopes, Denise Adorno [Oak Ridge National Laborator↗
DOE OSTI2021

Multi-energy reconstructions, central electron temperature measurements, and early detection of the birth and growth of runaway electrons using a versatile soft x-ray pinhole camera at MST

A multi-energy soft x-ray pinhole camera has been designed, built, and deployed at the Madison Symmetric Torus to aid the study of particle and thermal transport, as well as MHD stability physics. This novel imaging diagnostic technique employs a pixelated x-ray detector in which the lower energy threshold for photon detection can be adjusted independently on each pixel. The detector of choice is a PILATUS3 100 K with a 450 μm thick silicon sensor and nearly 100 000 pixels sensitive to photon energies between 1.6 and 30 keV. An ensemble of cubic spline smoothing functions has been applied to the line-integrated data for each time-frame and energy-range, obtaining a reduced standard-deviation when compared to that dominated by photon-noise. The multi-energy local emissivity profiles are obtained from a 1D matrix-based Abel-inversion procedure. Central values of Te can be obtained by modeling the slope of the continuum radiation from ratios of the inverted radial emissivity profiles over multiple energy ranges with no a priori assumptions of plasma profiles, magnetic field reconstruction constraints, high-density limitations, or need of shot-to-shot reproducibility. In tokamak plasmas, a novel application has recently been tested for early detection, 1D imaging, and study of the birth, exponential growth, and saturation of runaway electrons at energies comparable to 100 × Te,0; thus, early results are also presented.

Delgado-Aparicio, L. F. (ORCID:0000000167394380)↗
DOE OSTI2021

Well Stimulation Treatment in California: Evaluation of Disclosure Data, May 2015 – October 2019

In this document, disclosure data from 1,228 well stimulations occurring May 2015 to October 2019 in California are evaluated. This evaluation updates a previous study that was based on 618 well stimulations occurring May 2015 to June 2016 (Stringfellow, W.T., Camarillo, M.K., and Jordan, P. 2017, Status of Well Stimulation in California Since Implementation of SB-4 Regulations, Berkeley National Laboratory, Berkeley, CA). While the goal of the previous study was to provide a summary of information obtained since passage of California Senate Bill No. 4—Oil and Gas, the goal of the current evaluation is to provide a basis for studying chemical indicators that could potentially be used to detect aquifer contamination. We evaluate chemical indicators is a separate document using the data contained herein as well as data from monitoring wells located within oil fields. Compared with the data used by Stringfellow et al. 2017, the current evaluation is based on a more diverse data set. While the previous study contained data for only two oil fields with more than 30 well stimulations, the current data set contains four oil fields with more than 30 well stimulations, making these data more ideal for comparisons using statistical tests. More producers and a new oil field, Buena Vista Nose, are represented in the current data set. Some well stimulation practices remain relatively unchanged since the previous study. Well stimulation is mostly occurring in Kern County with the exception of a single well stimulation in Orange County. Almost all well stimulations are hydraulic fracturing. The current data set contains two acid fracturing treatments and one matrix acidizing treatment. The median number of chemicals added per well stimulation—excluding water and proppant—was previously 21; the number in the expanded data set is 20. Median water added per well stimulation was previously 89,000 gallons and is now 98,000 gallons. While this water use represents a 10% increase, water use for hydraulic fracturing in California is still lower than water use in other oil and gas fields. Chemical formulations appear to have shifted since the previous study. This shift may be related to a change in the predominant service company. We identified 26 chemicals for which the frequency of use has changed by more than 30%. The total number of chemicals used has also expanded. Previously, 178 unique chemicals were identified as being added to well stimulation fluids. The total number of unique chemicals is now 205. Analytical data available for base and recovered fluids has expanded. The number of base fluid samples has increased from 12 to 35 and the number of recovered fluid samples has increased from 1,078 to 2,166. The expanded data set is more ideal for an evaluation of indicator chemicals and comparisons across different formations. The analytical data for recovered fluid samples indicates that many ions, radioactive constituents, and organics are consistently observed, making these chemicals ideal for consideration as potential indicators of aquifer contamination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Slides for MS&T2022: Carbon-Carbon Composites from 3D Printed Preforms Graphitized by Electric Field Assisted Sintering

Formation of Carbon-Carbon (C-C) composite materials requires putting a carbon fiber preform through a cycle of densification, carbonization, and graphitization. This cycle is then repeated until the desired properties are achieved. The graphitization phase is particularly energy demanding as it involves heating the components to in excess of 2000°C. In traditional furnaces this is also quite time consuming as typical heating rates are low, and the actual heat treatment may be limited by component dimensions, as heat is conducted throughout the part primarily by thermal diffusion. Joule heating by means of Electric Field Assisted Sintering (EFAS) systems enables high heating rates and causes the part to be heated from the inside, which eliminates the thermal diffusion time constraint. In our process, C-C composites are formed by pyrolyzation of 3D-printed carbon fiber preforms, vacuum infiltration of naphthalene based mesophase pitch, and carbonization. The part is then graphitized in the EFAS system. By this method graphitization of the composite matrix was achieved in less than an hour. A part density of greater than 1.6g/cc (up to 1.9g/cc demonstrated) was achieved after only two rounds of infiltration and a single graphitization. Overall the process took less than a week which is significantly faster than traditional methods. Optical microscopy and XRD results are discussed to elucidate the internal porosity and characterize the degree of graphitization. Testing was performed showing the impact on fiber direction to electrical and thermal properties of the final part.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

We propose a framework to learn the time-dependent Hartree–Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Although the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from first principles, we use this problem as a testbed to develop strategies that can be applied to learn a priori unknown terms that arise in other methods/approaches to quantum dynamics, e.g., emerging problems such as learning exchange–correlation potentials for time-dependent density functional theory. We develop, train, and test three models of the TDHF inter-electronic potential, each parameterized by a four-index tensor of size up to 60 × 60 × 60 × 60. Two of the models preserve Hermitian symmetry, while one model preserves an eight-fold permutation symmetry that implies Hermitian symmetry. Across seven different molecular systems, we find that accounting for the deeper eight-fold symmetry leads to the best-performing model across three metrics: training efficiency, test set predictive power, and direct comparison of true and learned inter-electronic potentials. All three models, when trained on ensembles of field-free trajectories, generate accurate electron dynamics predictions even in a field-on regime that lies outside the training set. To enable our models to scale to large molecular systems, we derive expressions for Jacobian-vector products that enable iterative, matrix-free training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine learning applied to classifying neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this project, we apply a machine learning technique, namely decision trees, to automate the quantum number assignments. The tree is trained from simulated data generated to mimic the errors found in real data. We explore the use of several physics-motivated features for training our trees, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results using random matrix theory motivated fits which demonstrated that we can determine resonance spin groups somewhat reliably. If we use these fits as features in our trees, we can train them to spot outliers corresponding to misassigned resonances. We found that with the large number of features used in this project that the decision tree tended to over t training data resulting in poor performance with respect to the test data. By reducing the number of features, we can achieve nearly perfect assignment of quantum numbers with our training data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

PISCES two-detector covariance matrix fit for the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.

Rajaoalisoa, Miriama [Cincinnati U.] (ORCID:000000↗
DOE OSTI2020

Electron Microscopic Examination of an Irradiated TRISO Coated Particle of AGR-2 Experiment: AGR2-222-RS019

The transmission electron microscopic (TEM) examination of an irradiated tristructural isotropic (TRISO) coated particle from advanced gas reactor (AGR) 2 fuel Compact 2-2-2 is presented in this report. Compact 2-2-2 refers to the compact in Capsule 2 at Level 2 of Stack 2. The examination on AGR2 222 RS019 is focused on the interface between the silicon carbide (SiC) to inner pyrolytic carbon (IPyC) layer, central part of SiC, and the interface between SiC and outer pyrolytic carbon layer. The SiC grain boundary distribution and associated fission product precipitates were characterized by scanning transmission electron microscopy (STEM) energy dispersive spectroscopy (EDS), and precession electron diffraction (PED) in the transmission electron microscope. Two separate locations on SiC layer were selected for on the basis of the intact of the buffer layer in the irradiated TRISO particle. The key observation in this particle is that the compositional nature of grain boundary precipitates is more complex near IPyC/SiC layer where Pd, Pd-Ag, Pd-U containing precipitates can be observed. However, the compositions of precipitate towards outer most region of SiC mainly contain Pd. Ag has been observed in the IPyC/SiC layer in one of the two locations. Comparison of the results to previously irradiated and analyzed, Variant 3, safety tested AGR-1 TRISO particles (AGR1-433-001 and AGR1 222-RS004) showed the grain boundary distributions have some noticeable differences. The differences in coincident site lattice grain boundary and misorientation angles are possibly inherent to the fabrication process. Neutron irradiation induced void distribution has been found to be nonuniform. A high concentration of smaller voids is found at stacking faults compared to those in the SiC matrix. Larger void sizes are observed in the SiC location that is near to the broken buffer layer. This void size variation with integrity of buffer layer can potentially affect the fission product retention. However, the average void size continuously decreases along the radius of SiC in both locations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Fracture behavior of a melt-infiltration-processed SiC/Si composite at 900°C in argon, air, and steam

The fracture behavior of a melt-infiltration-processed SiC/Si composite, used to mimic the matrices of industrial fiber-reinforced ceramic composites, was examined in different atmospheres and temperatures. Specimens tested in four-point bending at 900 °C in oxygen-gettered argon, dry air, or steam-rich atmospheres exhibited higher average fracture strengths than specimens tested at 25°C. Higher mean fracture strength values were obtained for specimens tested in dry air or in a steam-rich atmosphere at 900°C than for specimens tested in high-purity, oxygen-gettered argon at this temperature. The increased fracture strengths obtained in air and in steam-rich atmospheres coincided with increased specimen oxidation and apparent oxide filling and blunting of flaws in these composites. Further, a transition in the location of catastrophic failure, from sites of preexisting damage created by Vickers indentations for tests in argon to other locations for tests in air or steam-rich atmospheres, was also consistent with such apparent oxide filling/blunting of indentation-induced flaws.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Test of CP-invariance of the Higgs boson in vector-boson fusion production and in its decay into four leptons

A search for CP violation in the decay kinematics and vector-boson fusion production of the Higgs boson is performed in the H → ZZ * → 4 ℓ ( ℓ = e, μ ) decay channel. The results are based on proton-proton collision data produced at the LHC at a centre-of-mass energy of 13 TeV and recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb –1 . Matrix element-based optimal observables are used to constrain CP-odd couplings beyond the Standard Model in the framework of Standard Model effective field theory expressed in the Warsaw and Higgs bases. Differential fiducial cross-section measurements of the optimal observables are also performed, and a new fiducial cross-section measurement for vector-boson-fusion production is provided. All measurements are in agreement with the Standard Model prediction of a CP-even Higgs boson.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Deconvoluting experimental decay energy spectra: The O 26 case

In nuclear reaction experiments, the measured decay energy spectra can give insights into the shell structure of decaying systems. However, extracting the underlying physics from the measurements is challenging due to detector resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a deblurring method that is commonly used in optics and has proven to be a successful technique for restoring images, was applied to our experimental nuclear physics data. The only inputs to the method are the observed energy spectrum and the detector's response matrix also known as the transfer matrix. We demonstrate that the technique can help access information about the shell structure of particle-unbound systems from the measured decay energy spectrum that is not immediately accessible via traditional approaches such as χ-square fitting. For a similar purpose, we developed a machine learning model that uses a deep neural network (DNN) classifier to identify resonance states from the measured decay energy spectrum. We tested the performance of both methods on simulated data and experimental measurements. Then, we applied both algorithms to the decay energy spectrum of 26 O → 24 O + n + n measured via invariant mass spectroscopy. Here, the resonance states restored using the RL algorithm to deblur the measured decay energy spectrum agree with those found by the DNN classifier. Both deblurring and DNN approaches suggest that the raw decay energy spectrum of 26 O exhibits three peaks at approximately 0.15 MeV, 1.50 MeV, and 5.00 MeV, with half-widths of 0.29 MeV, 0.80 MeV, and 1.85 MeV, respectively.

Spectrometers & spectroscopic techniques↗
DOE OSTI2024

Impact of dark matter on tidal signatures in neutron star mergers with the Einstein Telescope

If dark matter (DM) accumulates inside neutron stars (NS), it changes their internal structure and causes a shift of the tidal deformability from the value predicted by the dense-matter equation of state (EOS). In principle, this shift could be observable in the gravitational-wave (GW) signal of binary neutron star (BNS) mergers. We investigate the effect of fermionic, noninteracting DM when observing a large number of GW events from DM-admixed BNSs with the precision of the proposed Einstein telescope (ET). Specifically, we study the impact on the recovery of the baryonic EOS and whether DM properties can be constrained. For this purpose, we create event catalogs of BNS mock events with DM fraction up to 1%, from which we reconstruct the posterior uncertainties with the Fisher matrix approach. Using this data, we perform joint Bayesian inference on the baryonic EOS, DM particle mass, and DM particle fraction in each event. Here, our results reveal that when falsely ignoring DM effects, the EOS posterior is biased toward softer EOSs, though the offset is rather small. Further, we find that within our assumptions of our DM model and population, ET will likely not be able to test the presence of DM in BNSs, even when combining many events and adding Cosmic Explorer (CE) to the next-generation detector network. Likewise, the potential constraints on the DM particle mass will remain weak because of degeneracies with the fraction and EOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Single Cobalt Sites Dispersed in Hierarchically Porous Nanofiber Networks for Durable and High-Power PGM-Free Cathodes in Fuel Cells

Increasing catalytic activity and durability of atomically dispersed metal–nitrogen–carbon (M–N–C) catalysts for the oxygen reduction reaction (ORR) cathode in proton-exchange-membrane fuel cells remains a grand challenge. In this study, a high-power and durable Co–N–C nanofiber catalyst synthesized through electrospinning cobalt-doped zeolitic imidazolate frameworks into selected polyacrylonitrile and poly(vinylpyrrolidone) polymers is reported. The distinct porous fibrous morphology and hierarchical structures play a vital role in boosting electrode performance by exposing more accessible active sites, providing facile electron conductivity, and facilitating the mass transport of reactant. The enhanced intrinsic activity is attributed to the extra graphitic N dopants surrounding the CoN 4 moieties. The highly graphitized carbon matrix in the catalyst is beneficial for enhancing the carbon corrosion resistance, thereby promoting catalyst stability. The unique nanoscale X-ray computed tomography verifies the well-distributed ionomer coverage throughout the fibrous carbon network in the catalyst. The membrane electrode assembly achieves a power density of 0.40 W cm –2 in a practical H 2 /air cell (1.0 bar) and demonstrates significantly enhanced durability under accelerated stability tests. The combination of the intrinsic activity and stability of single Co sites, along with unique catalyst architecture, provide new insight into designing efficient PGM-free electrodes with improved performance and durability.

25 ENERGY STORAGE↗
DOE OSTI2024

Environmental barrier coatings on SiC without a silicon bond coating: oxidation resistance, failure modes, and future improvements

Environmental barrier coatings (EBCs) are used to mitigate chemical reactions between SiC ceramic matrix composite (CMC) components and the H 2 O in combustion gas in turbine hot sections. CMCs are currently temperature-limited by the Si-bond coating, which melts at ~ 1414 °C. This work explores EBCs where the bond coating was removed to achieve higher operating temperatures. Various versions of enhanced roughness SiC were utilized to improve EBC adhesion to the substrates prior to 1 h furnace cycle testing in steam at 1250–1425 °C. The enhanced SiC roughness resulted in short coating lifetimes as the roughness was oxidized away with SiO 2 formation. Further, isothermal furnace exposures at 1400–1600 °C showed Yb 2 Si 2 O 7 /Yb 2 SiO 5 EBC microstructural changes, resulting in premature debonding from the substrates. Finally, this work provides baseline requirements for the development of both next-generation EBCs and bond coating strategies to overcome the current limitation of the Si-bond coating melting temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interlayer fusion bonding of semi-crystalline polymer composites in extrusion deposition additive manufacturing

This work focuses on the evolution of interlayer fracture toughness properties of fiber-reinforced, semi-crystalline polymers in the extrusion deposition additive manufacturing (EDAM) process. Further, this work bridges the gap between the additive process conditions (time-temperature history) and the effective layer-to-layer fracture properties developed within a printed component. This is the first step to predict delamination that can occur during printing, during cooling to room temperature after printing, and during service performance of an additively manufactured geometry. A phenomenological model is developed for fusion bonding of semi-crystalline polymer matrix composites by coupling the interdiffusion of polymer chains with the evolution of polymer crystallinity. While the interdiffusion is captured by reptation theory of polymer dynamics, the evolution of crystallinity is modeled by phenomenological crystallization kinetics and crystal melting dynamics. Further, a methodology is developed to determine the critical strain energy release rate, G IC of the interlayer interface and experiments are conducted utilizing the double cantilever beam fracture test geometry. In conclusion, predictions of GIC as a function of thermal history are compared with experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chiral-odd generalized parton distributions in the large-𝑁 𝑐 limit of QCD: Spin-flavor structure, polynomiality, and sum rules

We study the nonperturbative properties of the nucleon’s chiral-odd generalized parton distributions (transversity GPDs) in the large-𝑁 𝑐 limit of QCD. This includes the parametric ordering of the spin-flavor components, the polynomiality property of the moments, and the sum rules connecting the GPDs with the tensor form factors. A multipole expansion in the transverse momentum transfer is used to enumerate and interpret the structures in the nucleon matrix element of the chiral-odd partonic operator, including monopole, dipole and quadrupole terms. The 1/𝑁 𝑐 expansion of the GPDs is performed using the abstract mean-field picture of baryons in the large-𝑁 𝑐 limit and its symmetries. We derive a large-𝑁 𝑐 relation between the flavor-nonsinglet GPDs 𝐸$^{𝑢−𝑑}_𝑇$ and $\tilde{𝐻}^{𝑢−𝑑}_𝑇$ and test it with recent lattice QCD results. We show that the polynomiality property and sum rules of the GPDs are fulfilled with the restricted realization of translational and rotational invariance in the mean-field picture. The results provide a basis for the phenomenological analysis of chiral-odd GPDs and hard exclusive processes in the large-𝑁 𝑐 limit, and for calculations in specific dynamical models.

generalized parton distributions↗
DOE OSTI2022

Translating Material-Level Characterization of Carbon-Nanotube-Reinforced Composite Gridlines to Module-Level Degradation

Cell cracks in PV modules caused by poor handling during shipping and installation as well as from extreme weather events can lead to gradual or immediate power degradation. To directly address cell-crack-induced degradation, we have formulated a carbon nanotube additive for commercial screen printed silver pastes. We have shown in previous work that these metal matrix composites have little to no effect on the cell's efficiency while enhancing the metallization's fracture toughness and electrical gap-bridging capability. In this work, we focus on translating materials level characterization techniques to module level degradation. We found that we get conflicting results from two different methods of measuring the metallization's ability to electrically bridge gaps in cracked solar cells. Mini-module stress testing is currently underway to determine which materials characterization correlates well with the min-module degradation characteristics.

carbon nanotubes↗