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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ammonium cations with high p K a in perovskite solar cells for improved high-temperature photostability

Phenethylammonium (PEA + ) and butylammonium (BA + ) are widely used in three-dimensional (3D) perovskites to form two-dimensional (2D) perovskites at film surfaces and grain boundaries (GBs) for defect passivation and performance enhancement. Here, we show that these cations are unstable with 3D formamidinium (FA + )-containing perovskites under high-temperature light soaking. PEA + and BA + are found to deprotonate to amines, which then react with FA + to produce (phenethylamino)methaniminium (PEAMA + ) and (butylamino)methaniminium (BAMA + ), respectively, severely limiting device high-temperature photostability. Furthermore, removing these cations greatly improves the photostability but compromises device efficiency by leaving non-fully passivated surfaces and GBs. Ammonium cations with a high acid dissociation constant (pK a ), including PEAMA + (pK a =12.0) and BAMA + (pK a =12.0), can replace PEA + or BA + for passivation and are stable with FA-based perovskites due to their resistance to further deprotonation. P-i-n structure solar cells with PEAMA + additive maintained over 90% of their initial efficiency after light soaking at open circuit and 90 °C for 1500 hours.

14 SOLAR ENERGY↗
DOE OSTI2025

Using chiral-induced spin selectivity as a tool to improve materials and processes for energy science

Studies relating to electrical energy conversion, storage, and generation, date back to the 19th century, however only in recent years have scientists begun to investigate the impact of the electron spin on these processes. Control and manipulation of the electron spin, an intrinsically quantum property of matter, opens new approaches to addressing energy science challenges. Furthermore, the chiral-induced spin selectivity (CISS) effect is enabling for this effort, because of the control over the transport and generation of both pure spin currents and spin-polarized charge currents. In this review article, we begin with a brief introduction on design strategies for the implementation of CISS in materials and then describe recent studies that demonstrate how CISS can be used to improve electrocatalysis and spintronics. Lastly, we conclude with forward-looking thoughts on the next steps for leveraging CISS in energy science.

Batteries↗
DOE OSTI2026

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗
DOE OSTI2020

Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement

Abstract Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N 6 -methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A low power flexible dielectric barrier discharge disinfects surfaces and improves the action of hydrogen peroxide

There is an urgent need for disinfection and sterilization devices accessible to the public that can be fulfilled by innovative strategies for using cold atmospheric pressure plasmas. Here, we demonstrate a successful novel combination of a flexible printed circuit design of a dielectric barrier discharge (flex-DBD) with an environmentally safe chemical reagent for surface decontamination from bacterial contaminants. Flex-DBD operates in ambient air, atmospheric pressure, and room temperature without any additional gas flow at a power density not exceeding 0.5 W/cm 2 . The flex-DBD activation of a 3% hydrogen peroxide solution results in the reduction in the bacterial load of a surface contaminant of > 6log 10 in 90 s, about 3log 10 and 2log 10 better than hydrogen peroxide alone or the flex-DBD alone, respectively, for the same treatment time. We propose that the synergy between plasma and hydrogen peroxide is based on the combined action of plasma-generated OH · radicals in the hydrogen peroxide solution and the reactive nitrogen species supplied by the plasma effluent. A scavenger method verified a significant increase in OH · concentration due to plasma treatment. Novel in-situ FTIR absorption spectra show the presence of O 3 , NO 2 , N 2 O, and other nitrogen species. Ozone dissolving in the H 2 O 2 solution can effectively generate OH · through a peroxone process. The addition of the reactive nitrogen species increases the disinfection efficiency of the hydroxyl radicals and other oxygen species. Hence, plasma activation of a low concentration hydrogen peroxide solution, using a hand-held flexible DBD device results in a dramatic improvement in disinfection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2023

Improving deep learning model performance under parametric constraints for materials informatics applications

Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Helium-electrospray improves sample delivery in X-ray single-particle imaging experiments

Abstract Imaging the structure and observing the dynamics of isolated proteins using single-particle X-ray diffractive imaging (SPI) is one of the potential applications of X-ray free-electron lasers (XFELs). Currently, SPI experiments on isolated proteins are limited by three factors: low signal strength, limited data and high background from gas scattering. The last two factors are largely due to the shortcomings of the aerosol sample delivery methods in use. Here we present our modified electrospray ionization (ESI) source, which we dubbed helium-ESI (He-ESI). With it, we increased particle delivery into the interaction region by a factor of 10, for 26 nm-sized biological particles, and decreased the gas load in the interaction chamber corresponding to an 80% reduction in gas scattering when compared to the original ESI. These improvements have the potential to significantly increase the quality and quantity of SPI diffraction patterns in future experiments using He-ESI, resulting in higher-resolution structures.

Science & Technology - Other Topics↗
DOE OSTI2025

Improved robustness of sequentially deposited potassium cesium antimonide photocathodes achieved by increasing the potassium content towards theoretical stoichiometry

Alkali antimonide semiconductor photocathodes are promising candidates for high-brightness electron sources for advanced accelerators, including free-electron lasers (FEL), due to their high quantum efficiency (QE), low emittance, and high temporal resolution. Two challenges with these photocathodes are (1) the lack of a universal deposition recipe to achieve crystal stoichiometries and (2) their high susceptibility to vacuum contamination, which restricts their operation pressure to ultrahigh vacuums and leads to a short lifetime and low extraction charge. To resolve these issues, it is essential to understand the elemental compositions of deposited photocathodes and correlate them to robustness. Here, we report depth profiles for potassium cesium antimonide photocathodes, which were investigated using synchrotron radiation x-ray photoelectron spectroscopy, and the robustness of those photocathodes. We prepared two types of photocathodes with different potassium contents via sequential thermal evaporation. Depth profiles revealed that the photocathodes with a potassium deficit had excess cesium at the surface, while the ratio of potassium and cesium to antimony decreased rapidly within the film. In contrast, the photocathodes with sufficient potassium had close to the theoretical stoichiometry of K 2 CsSb at the surface and maintained that stoichiometry for over half the entire film thickness. Both photocathode types had a similar maximum QE at 532 nm; however, exposure to oxygen revealed that the photocathode with a crystalline stoichiometry of K 2 CsSb maintained QE at one order of magnitude higher pressure compared to its potassium-deficit counterpart. These results highlight the importance of synthesizing potassium cesium antimonide photocathodes with sufficient potassium to achieve the theoretical crystalline stoichiometry for both high QE and improved robustness.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

The value of improving manure management

Manure management and land application can improve soil health and nutrient recycling but can also be a major source of air pollutant emissions and regional eutrophication. A high-resolution, regional framework can now facilitate assessment of current and future manure management policies in a representative livestock-dense region of Europe.

Harrison, Brendan P↗
DOE OSTI2021

Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols

Abstract Uncertainty in desert dust composition poses a big challenge to understanding Earth’s climate across different epochs. Of particular concern is hematite, an iron-oxide mineral dominating the solar absorption by dust particles, for which current estimates of absorption capacity vary by over two orders of magnitude. Here, we show that laboratory measurements of dust composition, absorption, and scattering provide valuable constraints on the absorption potential of hematite, substantially narrowing its range of plausible values. The success of this constraint is supported by results from an atmospheric transport model compared with station-based measurements. Additionally, we identify substantial bias in simulating hematite abundance in dust aerosols with current soil mineralogy descriptions, underscoring the necessity for improved data sources. Encouragingly, the next-generation imaging spectroscopy remote sensing data hold promise for capturing the spatial variability of hematite. These insights have implications for enhancing dust modeling, thus contributing to efforts in climate change mitigation and adaptation.

Environmental Sciences & Ecology↗
DOE OSTI2020

Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement

The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M–N–C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M–N–C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MN x sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M–N–C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M–N–C catalysts to MEA performance is limited. Rationally designing morphologic features of M–N–C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M–N–C catalysts in MEAs. The most considerable challenge of current M–N–C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M–N–C catalysts viable in PEMFC technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2022

Atomic layer deposition of lithium zirconium oxides for the improved performance of lithium-ion batteries

Recently there has been increasing interest to develop lithium-containing films as solid-state electrolytes or surface coatings for lithium-ion batteries (LIBs) and related systems. Here, in this study, we for the first time investigated the thin film growth of lithium zirconium oxides (Li x Zr y O or LZOs) through combining two individual atomic layer deposition (ALD) processes of ZrO 2 and LiOH, i.e., sub-ALD of ZrO 2 and LiOH. We revealed that the hygroscopic nature of the LiOH component has a big impact on the growth of LZOs. We found that an increased temperature to 225 °C was more effective than an elongated purge to mitigate the adverse effects of physisorbed H 2 O. We further discovered that, during the resultant LZO super-ALD processes, the growth of sub-ALD LiOH has been promoted while the growth of sub-ALD ZrO 2 has been inhibited. In this study, a suite of instruments has been applied to characterize the LZO super-ALD processes and the resultant LZO films, including in situ quartz crystal microbalance (QCM), scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), atomic force microscopy (AFM), synchrotron-based X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Furthermore, we applied the resulting LZO films over LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) cathodes in LIBs and demonstrated that the LZO coating films could evidently improve the lithium-ion insertion and extraction rates of the NMC622 electrodes up to 3.4 and 2.6 times, respectively. The LZO-coated NMC622 cathodes exhibited much better performance than the uncoated NMC622 ones.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Atomically dispersed single iron sites for promoting Pt and Pt 3 Co fuel cell catalysts: performance and durability improvements

Significantly reducing platinum group metal (PGM) loading while improving catalytic performance and durability is critical to accelerating proton-exchange membrane fuel cells (PEMFCs) for transportation. In this study, we report an effective strategy to boost PGM catalysts through integrating PGM-free atomically-dispersed single metal active sites in the carbon support toward the cathode oxygen reduction reaction (ORR). We achieved uniform and fine Pt nanoparticle (NP) (~2 nm) dispersion on an already highly ORR-active FeN 4 site-rich carbon (FeN 4 –C). Furthermore, we developed an effective approach to preparing a well-dispersed and highly ordered L1 2 Pt 3 Co intermetallic nanoparticle catalyst on the FeN 4 –C support. DFT calculations predicted a synergistic interaction between Pt clusters and surrounding FeN 4 sites through weakening O 2 adsorption by 0.15 eV on Pt sites and reducing activation energy to break O–O bonds, thereby enhancing the intrinsic activity of Pt. Experimentally, we verified the synergistic effect between Pt or Pt 3 Co NPs and FeN 4 sites, leading to significantly enhanced ORR activity and stability. Especially in a membrane electrode assembly (MEA) with a low cathode Pt loading (0.1 mg Pt cm –2 ), the Pt/FeN 4 –C catalyst achieved a mass activity of 0.451 A mg Pt –1 and retained 80% of the initial values after 30 000 voltage cycles (0.60 to 0.95 V), exceeding DOE 2020 targets. Furthermore, the Pt 3 Co/FeN 4 catalyst achieved significantly enhanced performance and durability concerning initial mass activity (0.72 A mg Pt –1 ), power density (824 mW cm –2 at 0.67 V), and stability (23 mV loss at 1.0 A cm –2 ). The approach to exploring the synergy between PGM and PGM-free Fe–N–C catalysts provides a new direction to design advanced catalysts for hydrogen fuel cells and various electrocatalysis processes.

25 ENERGY STORAGE↗