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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A MHz X-ray diffraction set-up for dynamic compression experiments in the diamond anvil cell

An experimental platform for dynamic diamond anvil cell (dDAC) research has been developed at the High Energy Density (HED) Instrument at the European X-ray Free Electron Laser (European XFEL). Advantage was taken of the high repetition rate of the European XFEL (up to 4.5 MHz) to collect pulse-resolved MHz X-ray diffraction data from samples as they are dynamically compressed at intermediate strain rates (≤10 3 s −1 ), where up to 352 diffraction images can be collected from a single pulse train. The set-up employs piezo-driven dDACs capable of compressing samples in ≥340 µs, compatible with the maximum length of the pulse train (550 µs). Results from rapid compression experiments on a wide range of sample systems with different X-ray scattering powers are presented. A maximum compression rate of 87 TPa s −1 was observed during the fast compression of Au, while a strain rate of ∼1100 s −1 was achieved during the rapid compression of N 2 at 23 TPa s −1 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Investigation of properties of CdSeTe/Sb and Sb/CdSeTe stacks

The effect of Sb inclusion in CdSeTe thin films was examined. CdSeTe alloys were grown by co-evaporation, either on 5 nm thick electron-beam evaporated Sb layers (Sb/CdSeTe) or coated with Sb post-deposition (CdSeTe/Sb). Sb inclusion improved CdSeTe grain structure, a critical factor for device performance, with average grain size increasing from 0.6 to 1.5 μm. All samples crystallized on annealing to the cubic structure, with a change in film orientation from (111) to random or (220). The presence of Sb in CdSeTe/Sb or Sb/CdSeTe samples, however, had a limited effect on the carrier concentration compared to Sb-free samples. Time resolved photoluminescence showed Sb inclusion increased decay lifetimes, with τ1 = 0.8 ns for and τ2 = 6.9 ns for the Sb/CdSeTe sample.

14 SOLAR ENERGY↗
DOE OSTI2026

Effects of heteroatom doping on hydrogen uptake in tungsten oxide

Redox-active transition metal oxides (TMOs) that can undergo proton-insertion coupled electron transfer (PICET) are promising candidates for catalyzing molecular conversion reactions which require the transfer of hydrogen atoms (or the thermochemical equivalent, H + , e − ). Herein, we studied the effects of isovalent (Mo 6+ ) and aliovalent (V 5+ and Nb 5+ ) heteroatom doping on the electrochemical PICET behavior of monoclinic tungsten oxide (WO 3 ). Cyclic voltammetry in aqueous acidic electrolytes shows that the addition of redox-active heteroatoms (Mo 6+ and V 5+ ) leads to systematic shifts in redox couple half-wave potentials (E 1/2 ), broadening, and an overall decrease in the current response. Conversely, the non-redox active heteroatom (Nb 5+ ) only reduces the current response with no observable peak-current broadening. This broadening is attributed to changes in the proton binding affinities of oxygen in different chemical environments, i.e., bridging different pairs of redox-active transition metal cations. We determined the hydrogen bond dissociation free energy (H BDFE) values to elucidate the thermodynamic effect of heteroatom substitution. Density functional theory calculations reveal a differentiation in the hydrogen binding and oxygen vacancy formation energies between heteroatom doped structures. The PICET-induced structural phase transitions of the pristine and doped samples were further probed with operando electrochemical X-ray diffraction (EC-XRD) and with ex situ chemical reduction. The broadening of the potential-dependent current response with increased heteroatom doping manifests in the operando EC-XRD results as prolonged structural regions where multiple hydrogen bronze phases exist and the appearance of cubic bronze phases at lower degrees of reduction compared to pristine WO 3 .

Holzapfel, Noah P. [North Carolina State Universit↗
DOE OSTI2024

Boron-10 Doped Polysiloxanes as Matrix Materials for Application in the Simultaneous Detection and Discrimination of Gamma Rays and Fast and Thermal Neutrons

Three boron-10 enriched aromatic molecules have been synthesized and incorporated into two different commercial polysiloxane resins, Shin Etsu KER-6000 and Wacker SilRes H62-C. Scintillating fluorophores, 9,9-dimethyl-2-phenylfluorene (PhF) and 2,5-diphenyloxazole (PPO), were tested in combination with each resin and boron-10 molecule for the simultaneous detection of gamma rays, fast neutrons, and thermal neutrons. Here, the H62-C resin was able to solubilize a large amount of PhF, in excess of 20 wt%. Cure kinetics were controlled through the addition of divinylbenzene and phenyl tris(dimethylsiloxy)silane cross-linker solution to the H62-C resin, with rheology experiments demonstrating a large reduction in time to gelation when 20 wt% cross-linker solution was added, from more than 4 hours to less than 1 hour. These polysiloxane resins can be cured in 3 hours under air, while common poly(vinyltoluene) scintillators require at least 4 days of heating and oxygen-free conditions. PhF-doped KER-6000 with tolyl boronate pinacol ester exhibited the best overall performance with a light yield of 62% relative to EJ-200 and thermal neutron capture at energies up to 103 keVee (84 keVee for EJ254B-5). Additionally, four samples exhibited light yields surpassing an industry-standard boron-doped plastic scintillator, Eljen’s EJ254B-5. Over the course of ten months, the KER-6000 samples showed precipitation of dopant molecules, which reduced their light yield by 15% on average, while H62-C proved to be more stable with only a 6% reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗
DOE OSTI2026

Probing Physics beyond the Standard Model through Combined Analyses of Next-generation Type Ia Supernova, Cosmic Microwave Background, and Baryon Acoustic Oscillation Surveys

Observations of Type Ia supernovae (SNe Ia), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SN Ia sample expected from the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra (TT/EE/TE/ϕϕ) from the South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4–like experiment. We derive constraints on ΛCDM and its extensions involving the dark energy equation-of-state parameters (w 0 , w a ) and the sum of neutrino masses ∑m ν using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year 3 SN Ia sample can improve upon the DES Year 5 dark energy constraints by a factor of 2−2.5×, with the gains driven primarily by the significantly higher SN Ia density in the LSST sample. Similarly, DESI-DR3 shows up to a 1.8× improvement on dark energy parameters over DR2, driven largely by the substantial increase in the low-redshift sample. Combining CMB with LSST-Y3-SN Ia and DESI-DR3-BAO yields σ(w 0 ) = 0.028 and σ(w a ) = 0.11 for w 0 w a CDM cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%–30% when freeing ∑m ν and spatial curvature. Moreover, the joint analysis of the three datasets can enable a 2σ–3σ detection of ∑m ν .

Raghunathan, Srinivasan [University of California;↗
DOE OSTI2020

Development of rare-earth-free bulk magnets with energy product up to 12 MGOe in field annealed Mn–Bi–Mg–In–Sb alloys

A treatment consisting of annealing in a moderately strong, ≤30 kOe, magnetic field was applied to arc-melted ingots and compacted melt-spun alloys with composition Mn 50 Bi 50-x-y-z Mg x In y Sb z . The high degree of texture of the magnetic α phase induced with annealing of the ingot samples (except the Mn–Bi–Sb one) allowed for accurate determination of the phase anisotropy field. Alloying was found to increase the anisotropy field from 45 kOe in the Mn 50 Bi 50 alloy to 65 kOe in the Mn 50 Bi 45 Mg 3 In 0.5 Sb 1.5 alloy. In the compacted melt-spun alloys subjected to field annealing, a higher degree of texture could be achieved after compaction which had involved less heating, when a smaller fraction of the metastable β' phase had been converted into the α phase; compaction at room temperature allowed for the highest degree of texture in the Mn 50 Bi 45 Mg 3 In 0.5 Sb 1.5 magnet. The addition of indium was found to decrease the solidus temperatures of the alloys, facilitating the development of the α phase during the field annealing and modifying the properties of the resulting magnets. Introduction of indium resulted in a higher coercivity H c of the Mn–Bi–Mg–In magnets and in a better texture of the Mn–Bi–Mg–Sn–In magnets. The former effect allowed, in particular, for a viable maximum energy product ( BH ) max of 10 MGOe to be developed in the Mn 50 Bi 46.5 Mg 3 In 0.5 magnet through annealing in a relatively low field of 15 kOe, whereas the latter effect allowed for an excellent combination of ( BH ) max = 11.6 MGOe and H c = 8.5 kOe in the Mn 50 Bi 45 Mg 3 In 0.5 Sb 1.5 magnet. The highest ( BH ) max of 12 MGOe was realized in the Mn 50 Bi 46 Mg 3 In 0.5 Sb 0.5 magnet.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Atomic force microscopy phase imaging of epitaxial graphene films

Abstract Dynamic mode atomic force microscopy phase imaging is known to produce distinct contrast between graphene areas of different atomic thickness. But the intrinsic complexity of the processes controlling the tip motion and the phase angle shift excludes its use as an independent technique for a quantitative type of analysis. By investigating the relationship between the phase shift, the tip-surface interaction, and the thickness of the epitaxial graphene areas grown on silicon carbide, we shed light on the origin of such phase contrast, and on the complex energy dissipation processes underlying phase imaging. In particular, we study the behavior of phase shift and energy dissipation when imaging the interfacial buffer layer, single-layer, and bilayer graphene regions as a function of the tip-surface separation and the interaction forces. Finally, we compare these results with those obtained on differently-grown quasi free standing single- and bilayer graphene samples.

Lavini, Francesco (ORCID:0000000257223040)↗
DOE OSTI2020

Quantum-well states in fractured crystals of the heavy-fermion material CeCoIn5

Quantum-well states appear in metallic thin films due to the confinement of the wave function by the film interfaces. Using angle-resolved photoemission spectroscopy, we unexpectedly observe quantum-well states in fractured single crystals of CeCoIn5. We confirm that confinement occurs by showing that these states’ binding energies are photon-energy independent and are well described with a phase accumulation model, commonly applied to quantum-well states in thin films. This indicates that atomically flat thin films can be formed by fracturing hard single crystals. For the two samples studied, our observations are explained by free-standing flakes with thicknesses of 206 and 101 Å. We extend our analysis to extract bulk properties of CeCoIn5. Specifically, we obtain the dispersion of a three-dimensional band near the zone center along in-plane and out-of-plane momenta. We establish part of its Fermi surface, which corresponds to a hole pocket centered at G. We also reveal a change of its dispersion with temperature, a signature that may be caused by the Kondo hybridization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2023

Using defects as a ‘fossil record’ to help interpret complex processes during additive manufacturing: as applied to raster-scanned electron beam powder bed additively manufactured Ti–6Al–4V

Abstract Defects in parts produced by additive manufacturing, instead of simply being perceived as deleterious, can act as important sources of information associated with the complex physical processes that occur during materials deposition and subsequent thermal cycles. Indeed, they act as materials-state ‘fossil’ records of the dynamic AM process. The approach of using defects as epoch-like records of prior history has been developed while studying additively manufactured Ti–6Al–4V and has given new insights into processes that may otherwise remain either obscured or unquantified. Analogous to ‘epochs,’ the evolution of these defects often is characterized by physics that span across a temporal length scale. To demonstrate this approach, a broad range of analyses including optical and electron microscopy, X-ray computed tomography, energy-dispersive spectroscopy, and electron backscatter diffraction have been used to characterize a raster-scanned electron beam Ti–6Al–4V sample. These analysis techniques provide key characteristics of defects such as their morphology, location within the part, complex compositional fields interacting with the defects, and structures on the free surfaces of defects. Observed defects have been classified as banding, spherical porosity, and lack of fusion. Banding is directly related to preferential evaporation of Al, which has an influence on mechanical properties. Lack-of-fusion defects can be used to understand columnar grain growth, fluid flow of melt pools, humping, and spattering events. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2023

Prediction on X-ray output of free electron laser based on artificial neural networks

Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Direct NeTS sampling of nuclear graphite $S(α, β, T)$ in Serpent

For advanced reactor applications, Neural Thermal Scattering (NeTS) modules were developed to predict the thermal scattering law (TSL or $S(α, β, T)$) of a nuclear graphite neutron moderator. NeTS are multi-layer, feedforward artificial neural networks, which act as universal function approximators designed for TSL datasets. In this case, a 4-layer neural network with 164 neurons per layer is trained using FLASSH evaluated data in PyTorch and serialized as a torchscript dictionary to predict $S(α, β, T)$ on-the-fly. Relative, absolute and maximum percent deviations of NeTS from File 7 data generated using the FLASSH code are on the order of 0.01%, 0.1% and 1%, respectively, with low inference latencies of 0.000172 s per $S(α, β, T)$ at a given temperature. Capturing the full dimensionality of possible inelastic neutron-lattice interactions, NeTS functionality is embedded in the Serpent Monte Carlo code, where $S(α, β, T)_{NeTS}$ sampling is conducted on-the-fly and compared to ACE look-up-tables for predicting TREAT criticality. k-eff differences between sampling algorithms of 6 pcm are observed and are within the order of Monte Carlo uncertainty. Compared to discrete and continuous-energy ACE files (30 MB and 131 MB per temperature), the NeTS format is on the order of 200–300 kB for a continuous-temperature, interpolation-free representation of $S(α, β, T)$ and cross sections. NeTS-in-Serpent runtimes comparable with ACE look-up tables are achieved by scaling NeTS for high performance computing architectures with hybrid OpenMP + MPI parallelization. This work validates a novel, self-contained reactor physics framework for predictive cross sections, and demonstrates a general methodology for embedding modern machine learning libraries within existing neutronic analysis frameworks.

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids—including the density response, surface tension and size-dependent cavitation free energies—for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Studies on the compositional dependent structural and electrical properties of CaTiO3-modified K0.5Na0.5NbO3 piezoelectric system

Lead-free piezoelectric ceramics of (1 − x)K0.5Na0.5NbO3-xCaTiO3 were fabricated, and their crystal structure, microstructure, and electrical properties were systematically studied. Rietveld refinement of the x-ray diffraction data and Raman spectroscopic analyses revealed a composition-dependent structural phase transition: three phase transitions, namely, from a pure orthorhombic phase for x ≤ 0.02 to a mixed phase of orthorhombic and tetragonal phases (0.03 ≤ x ≤ 0.08) and finally another mixed phase of tetragonal + cubic for x = 0.10 and 0.15 at room temperature (RT). The morphological study reveals a decrease in grain size along with a more uniform distribution of grains as the concentration of CaTiO3 (CT) increases; notably, a homogeneous distribution of grains is observed for x = 0.05. The temperature-dependent dielectric properties show two phase transitions, from orthorhombic to tetragonal (TO-T) and tetragonal to cubic (TC), for unmodified K0.5Na0.5NbO3 (KNN). However, both the phase transition temperatures (TO-T and TC) decrease, and the transition peaks broaden with an increase in CT substitution, and for x > 0.06, the TO-T shifted below RT. The broadening of the transition peak at TO-T may be due to the relaxation behavior. Among the prepared samples, the 5 mol. % CT-modified KNN shows the optimum electrical properties (d33 = 114 pC/N, ɛr = 412, and 2Pr = 15.25 μC/cm2) at RT. The enhanced electrical properties for x = 0.05 are due to the coexistence of orthorhombic and tetragonal phases, facilitating easy polarization rotation and flattening of the free energy profile. A phase diagram has been constructed based on the information gathered from the temperature-dependent dielectric measurements, RT x-ray diffraction, and Raman spectroscopy data and is discussed in detail.

Physics↗
DOE OSTI2025

Reducing module soiling with scalable and robust photocatalytic coatings

The air-glass interface at the front of a photovoltaic (PV) module reflects approximately 4% of incident light, decreasing the potential power output of the module by the same amount. Today’s modules reduce this loss by adding a low-refractive-index (1.25-1.30) SiO2 coating to the sunward side of the module glass; this antireflection coating recovers approximately 3% of the 4% light that would otherwise be lost. While such antireflection coatings work very well on clean, new modules, they do not inhibit soiling—the accumulation of soilants such as dust, pollen, soot, or other foreign material—on the module glass, and soilants reflect and scatter incident light. An improved coating would serve provide not only an antireflection effect, but also an anti-soiling effect. The goal of this project was to develop such a coating and provide a path for it to be manufactured in the U.S. The project successfully designed and fabricated coatings that provided >3% transmittance gain compared to bare glass (matching the performance of commercial antireflection coatings) and displayed anti-soiling behavior in standard laboratory soiling effects. This was achieved by using a Swift Coat proprietary coating deposition technique, aerosol impact-driven assembly (AIDA), to control the porosity and thus refractive index of coatings of photocatalytic materials—such as TiO2—that would otherwise increase (instead of decrease) reflection. These combined antireflection/anti-soiling coatings passed PV industry standard module reliability tests as well as coating-specific abrasion tests, showing that they have the durability needed for decades in the field. Swift Coat scaled the AIDA hardware and deposition process to make mini-modules that were monitored for nearly two years during field tests administered by a third party, as well as demonstrated scaling to the widths of full-sized modules. The fielded mini-modules outperformed reference modules (with commercial antireflection coatings) in two locations, providing a 1% absolute average performance boost and larger increases during periods of heavier soiling. Swift Coat’s cost analysis indicated a coating manufacturing cost below the sales price of today’s antireflection coatings. More than five module manufacturers sampled and assessed the coatings, and three provided letters of support. The coating developed in this project increases the energy output of PV modules, thereby decreasing the cost per kilowatt-hour of solar energy generated. Cheaper solar electricity benefits the public by accelerating the transition to a stable, affordable, carbon-free energy economy. In addition, for select applications in which PV modules are highly visible—such as on residential rooftops—the coating provides an aesthetic benefit because it stays cleaner than today’s modules. Finally, Swift Coat and its prospective customers are U.S. companies, and successful commercialization of this technology will provide U.S. jobs and a secure solar supply chain.

14 SOLAR ENERGY↗
DOE OSTI2025

X-ray thermal diffuse scattering as a texture-robust temperature diagnostic for dynamically compressed solids

We present a model of x-ray thermal diffuse scattering (TDS) from a cubic polycrystal with an arbitrary crystallographic texture, based on the classic approach of Warren [B. E. Warren, Acta Crystallogr. 6, 803 (1953)]. We compare the predictions of our model with femtosecond x-ray diffraction patterns gathered from ambient and dynamically compressed rolled copper foils obtained at the High Energy Density instrument of the European X-Ray Free-Electron Laser facility and find that the texture-aware TDS model yields more accurate results than does the conventional powder model owed to Warren. Nevertheless, we further show: with sufficient angular detector coverage, the TDS signal is largely unchanged by sample orientation and in all cases strongly resembles the signal from a perfectly random powder; shot-to-shot fluctuations in the TDS signal resulting from grain-sampling statistics are at the percent level, in stark contrast to the fluctuations in the Bragg-peak intensities (which are over an order of magnitude greater); and TDS is largely unchanged even following texture evolution caused by compression-induced plastic deformation. We conclude that TDS is robust against texture variation, making it a flexible temperature diagnostic applicable just as well to off-the-shelf commercial foils as to ideal powders.

Crystal lattices↗