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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Apomixis in Farmers’ Fields: Overview, Case Studies from Forage Grasses and Considerations for Future Apomictic Crops

Apomixis occurs naturally in several commercially important species from diverse plant families. While in some of these species apomixis is yet to be exploited in breeding schemes aimed at fixing heterosis, genetic progress and cultivar development, in other species apomixis has been integrated at different stages of breeding. Some of the most relevant examples come from the subfamily Panicoideae, the second largest subfamily of the Poaceae, and are the main focus of this review. The subfamily encompasses many tropical and sub-tropical grasses and grains of worldwide economic importance. Apomictic tropical forages are prime examples of how apomixis can be used and exploited in the development of marketable cultivars, which are essential to the meat and milk production industries globally. The main commercial forages used as grass pastures covering millions of hectares in tropical and sub-tropical regions are polyploids exhibiting gametophytic apomixis that belong to the genus Urochloa spp. (brachiariagrasses) and to the species Megathyrsus maximus (guineagrass). Buffel grass (Cenchrus ciliaris) and Paspalum spp. are other important apomictic forages bred and used in these regions. Breeding involves large germplasm collections from the centers of origin of the species, and for most of them, sexually reproducing diploid plants have been found. Chromosomically duplicated plants that maintain sexual reproduction are used in crosses with apomictic genotypes for the development and selection of cultivars to be marketed or used as progenitors in subsequent breeding cycles. The peculiarities of each genus/species breeding programs, the cultivars obtained from these programs, and the impact of use of marker assisted selection in cultivar development are presented. In addition, the test or implementation of new technologies such as high throughput phenotyping, and the use of machine learning methods for trait prediction and genomic selection are positively impacting the selection and speed of development of new polyploid apomictic cultivars. Furthermore, genetic transformation techniques, including genome editing, provide an additional layer for design of tailor-made, customer-oriented cultivars.

Cenchrus↗
DOE OSTI2024

The Impact of Bias Row Noise to Photometric Accuracy: Case Study Based on a Scientific CMOS Detector

Abstract We tested a new model of CMOS detector manufactured by the Gpixel Inc, for potential space astronomical application. In laboratory, we obtain some bias images under the typical application environment. In these bias images, clear random row noise pattern is observed. The row noise also contains some characteristic spatial frequencies. We quantitatively estimated the impact of this feature to photometric measurements, by making simulated images. We compared different bias noise types under strict parameter control. The result shows the row noise will significantly deteriorate the photometric accuracy. It effectively increases the readout noise by a factor of 2–10. However, if it is properly removed, the image quality and photometric accuracy will be significantly improved.

Astronomy & Astrophysics↗
DOE OSTI2024

On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL

The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.

17 WIND ENERGY↗
DOE OSTI2024

Behavior and mechanisms of Doppler wind lidar error in complex terrain: stable flow case study at Perdigão

A numerical experiment is carried out investigating the magnitude of biases in ground-based lidar measurements in complex flow conditions. Biases assessed include those arising from flow curvature and from the interaction of turbulence with the wind field reconstruction (WFR) algorithms used by a WindCube lidars and anemometers. RANS-CFD and WRF-LES simulations were performed for the Perdig˜ao Field Experiment site for a range of atmospheric conditions. Virtual anemometer and lidar data were generated for four locations: two near exposed ridge tops and two in low-speed regions in the valley. The LES data at these four locations show that the scalar inflation terms (the relation between scalar and vector averaged wind speed) for virtual lidar and virtual cups agree very well with predictions using perturbation theory. While the lidar errors vary greatly with location and height, the contribution from the flow curvature tends to be larger than the differences arising from scalar inflation. For one lidar/mast pair near the ridge top, comparisons between simulations and measurements are carried out for a resonant mountain wave event on June 14th, 2017, and for the whole duration of the Perdigão campaign for winds perpendicular to the ridges. The lidar error during the mountain wave, a period of strong stability and low inversion height, is significantly larger than the campaign average. The sensitivity of the lidar error to atmospheric stability is confirmed by the RANS simulations, which suggests strong sensitivity of flow curvature error to stability conditions and to the shape of the wind speed profile near the top of the boundary layer.

17 WIND ENERGY↗
DOE OSTI2024

Urban morphology and urban water demand: a case study in the land constrained Los Angeles region using urban growth modeling

The interactions between population growth, urban morphology, and water demand have important implications for water resources and supply in urban regions. Water use for irrigation comprises a significant fraction of urban water demand, and is potentially influenced by long-term changes in urban morphology. To investigate this, we used spatially explicit projections of urban land development intensity (fraction impervious area) generated from a 30 m resolution urban growth model for the Los Angeles (LA) region. Recent historical data on water use and high resolution landcover were used to establish relationships between green area, urban development intensity, and outdoor water demand. These relationships were then used to project outdoor and total water demand in 2100 using the urban growth model outputs. We considered two different population scenarios informed by the shared socioeconomic pathway (SSP) projections for the region (SSP3 and SSP5), and three scenarios of urban development intensification. Our analysis is resolved for over 80 water providers in the region, from the urban core to suburban fringe, and highlights diverse demand responses influenced by initial urban form and water demand attributes. Assumptions about outdoor water use factors based on recent water supply data were found to be nearly as influential on future outdoor demand as the urban growth scenario settings. Compared to previous studies, our work is unique in coherently linking high resolution SSP population scenarios, urban land cover evolution, and urban water demand projections, demonstrating the approach for the LA region—the largest population center in the western United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗
DOE OSTI2024

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning

Abstract Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Plant Sciences↗
DOE OSTI2025

Nanoscale Phase Identification Using Two-Dimensional Pair Correlation Functions: A Case Study on Hafnium Oxide

Accurate identification of local phases in nanocrystalline materials is essential for understanding their functional properties, but it remains a significant challenge for polymorphic materials to locally differentiate them at nanoscale. This challenge is further compounded in polycrystalline materials with randomly oriented grains and the coexistence of multiple phases. In this report, we present a methodology for phase and orientation identification at the nanoscale by leveraging vector pair correlation functions extracted from atomically resolved scanning transmission electron microscopy (STEM) images. We demonstrate the accuracy of the methodology on both simulated and experimental data from HfO 2 -based films, a material that exhibits multiple coexisting phases in films with thicknesses ranging from 5 to 20 nm. While demonstrated on HfO 2 films, the methodology can be extended to other polymorphic nanocrystalline systems with complex phase coexistence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dark matter substructure or source model systematics? A case study of cluster lens Abell S1063

Mapping the small-scale structure of the universe through gravitational lensing is a promising tool for probing the particle nature of dark matter. Curved Arc Basis (CAB) has been proposed as a local lensing formalism in galaxy clusters, with the potential to detect low-mass dark matter substructure. In this work, we analyse the cluster lens Abell S1063 in search of dark matter substructure with the CAB formalism, using multiband imaging data from James Webb Space Telescope ( JWST ). We use two different source modelling methods: shapelets and pixel-based source reconstruction based on Delaunay triangulation. We find that source modelling systematics from shapelets result in a disagreement between CAB parameters measured from different filters. Source modelling with Delaunay significantly alleviates this systematic, as seen in the improvement in agreement across filters. We also find that inadequate complexity in source modelling can result in convincing spurious detections of dark matter substructure from strong gravitational lenses, as seen by our $\Delta \text{BIC} > 20$ measurement of a $M \sim 10^{10}$ ${\rm M}_{\odot }$ subhalo with shapelets, a spurious detection that is not reproduced with Delaunay source modelling. We demonstrate that multiband analysis with different JWST filters is key for disentangling source and lens model systematics from dark matter substructure detections.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Importance of finite-size corrections for accurate ab initio modeling of carrier capture at semiconductor defects: A case study of substitutional C N in GaN

In ab initio studies of carrier-capture processes in defective semiconductor materials, the single-effective-mode formalism and the static-coupling approximation have become the predominant theoretical approaches for determining carrier-capture coefficients. The single-mode formalism relies on accurate nonequilibrium defect energies obtained from density-functional theory (DFT), where required inputs are a series of configurationally displaced, defect-containing supercells obtained using an interpolative ansatz, and where the DFT outputs are corresponding total energies that have traditionally been postprocessed using a long-established ground-state formulation of finite-size corrections and defect-formation energies. This formulation remains commonly used even though the defects that form a configuration-coordinate (CC) diagram typically exist as structures that are displaced from the ground state. To remedy this inconsistency, Kumagai has recently proposed novel methods for implementing finite-size corrections specifically intended for DFT calculations of the defect energies used to construct CC diagrams and implement the single-mode formalism [Y. Kumagai, Phys. Rev. B 107, L220101 (2023)]. Kumagai's approach builds on the latest finite-size-correction methods introduced to describe vertical charge-state transitions for charge-localizing point defects in semiconductors and insulators [T. Gake et al., Phys. Rev. B 101, 020102 (2020); S. Falletta et al., Phys. Rev. B 102, 041115 (2020)]. The newly identified finite-size artifact treated in these studies is the polarization charge induced on a configurationally frozen defect and its subsequent interaction with a vertical transition in charge state. In this work, we evaluate Kumagai's proposed methodology by applying it in a high-precision DFT study of carrier capture by substitutional C N in GaN, a well-characterized and technologically relevant defect and material. We have rigorously calculated C N defect energies across various supercell sizes for each defect configuration and charge state on the hole-capture CC diagram of C N (𝑞=−1), enabling a direct comparison of the slopes of the defect energies versus inverse cell size with those predicted by Kumagai. The most consequential prediction of Kumagai's method is that these slopes distinctly vary as the square of the linear-interpolation parameter used to construct the nonequilibrium defect configurations. Our results quantitatively support this prediction. Moreover, with these new finite-size corrections and multiple-cell-size DFT calculations in place, we find that the classical energy barrier for hole capture by C N (𝑞=−1) in GaN decreases to 0.092–0.127 eV. This finding confirms the recent ≈ 0.1 eV prediction of Reshchikov based on the weak temperature dependence for hole capture observed in photoluminescence experiments [M. A. Reshchikov, J. Appl. Phys. 129, 121101 (2021)]. These results stand in stark contrast to previously calculated barriers of 0.486 and 0.73 eV, which also used the single-mode formalism but were obtained by instead using ground-state-based finite-size corrections. Our reduced classical barrier for capture increases the temperature-dependent hole-capture coefficient of a C N (𝑞=−1) defect by more than two to four orders of magnitude for temperatures of 100–600 K, compared to the previous 0.486 eV results. While other defects may not be as dramatically affected as here, we suggest that incorporating proper finite-size corrections for the vertical-transition-like states embedded within CC diagrams is an essential, yet previously unrecognized, component of accurate modeling of carrier-capture when using the single-effective-mode formalism.

dielectric properties↗
DOE OSTI2025

Close-up view of the interplay between lattice distortions and charge density waves: Case study on rare-earth nickel carbides

Using variable temperature total x-ray scattering and large-scale structure modeling, we study the temperature and composition evolution of lattice distortions and charge density waves (CDWs) in the archetypal strongly correlated system RENi⁢ C 2 (RE=Dy, Tb, Gd, and Sm). Joint analysis of reciprocal and real space data reveals the presence of strong lattice distortions that, depending on the RE species and temperature, appear periodic or remain local, forming a complex CDW phase diagram. Apparently, the CDWs in RENi⁢ C 2 materials arise from a common to the system lattice instability involving diverse distortion modes that persist when magnetic order also sets in at very low temperature. In conclusion, the results support the notion that intrinsic lattice distortions are important to the emergence of exotic electronic phases in strongly correlated systems and call for their further investigation using the advanced approach adopted here.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interstitial solute segregation at triple junctions: Implications for nanomaterials and a case study of hydrogen in palladium

At very fine grain sizes, grain boundary segregation can deviate from conventional behavior due to triple junction effects. While this issue has been addressed in prior work for substitutional alloys, here we develop a framework that accounts for interstitial sites in the grains, grain boundaries, and triple junctions of model Pd(H) polycrystals. This approach allows computation of interstitial segregation spectra separately at both defect types, which permits an understanding of segregation at all grain sizes via a size-scaling spectral isotherm. Here, the size dependencies of dilute Pd(H) are found to be influenced not only by the triple junction content, but also by grain size–dependent lattice strains; the latter effect is evidenced by size dependencies of individual grain boundary and junction subspectra. The framework proposed here is applicable to interstitial alloys in general and may serve as a basis for interfacial engineering in interstitial nanocrystalline alloys. As an example, we show that using the dilute limit isotherm, hydrogen density can triple in nanocrystalline vis-à-vis microcrystalline Pd due to hydrogen adsorption at intergranular defect sites.

Alloys↗
DOE OSTI2025

Nontrivial critical behavior at magnetic transitions: A case study of Sm 7 ⁢Pd 3

We present a comprehensive analysis of the critical behavior of Sm 7 ⁢Pd 3 in the vicinity of its second-order magnetoelastic transition at 𝑇 c =173 K. The critical exponents (CEs) 𝛽 and 𝛾, determined using both the standard convergence procedure and the average normalized slope (ANS) method, diverge at 𝑇 c –-a characteristic typically associated with first-order transitions. Notably, none of the established universality classes satisfactorily describe the critical behavior of Sm 7 ⁢Pd 3 , and we discuss the possible origins of this deviation in the context of the strong spin-lattice coupling intrinsic to the sample. We emphasize the importance of accurately selecting the critical temperature and magnetic field ranges to ensure robust critical behavior analysis, and propose a quantitative approach to assess the reliability of the extracted CEs. Additionally, we demonstrate that in the ANS method, the critical exponents 𝛽 and 𝛾 should be calculated separately using data for 𝑇 ⩽ 𝑇 c and 𝑇 ⩾ 𝑇 c , respectively. In conclusion, our findings underscore the need for a revised theoretical framework to accurately describe second-order magnetoelastic transitions.

Ferrimagnets↗
DOE OSTI2026

Impact of absorption due to zero-field splitting on loss in dielectrics: A case study in sapphire

The coherence times of superconducting qubits are limited by loss mechanisms, whose microscopic origins have remained elusive. We propose a mechanism caused by transitions between zero-field-split states of paramagnetic impurities or defects. We derive the absorption cross section for a magnetic dipole transition and apply it to calculate the bulk loss tangent. For Cr, Fe, and V impurities in sapphire, we find loss tangents at 4.5 GHz in the range of 10 −9 –10 −8 , comparable to the loss measured in experiments. This value suggests that magnetic loss may be a limiting factor in the coherence times of superconducting qubits.

36 MATERIALS SCIENCE↗