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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2024

Sustainable layered cathode with suppressed phase transition for long-life sodium-ion batteries

Sodium-ion batteries are among the most promising alternatives to lithium-based technologies for grid and other energy storage applications due to their cost benefits and sustainable resource supply. For the cathode—the component that largely determines the energy density of a sodium-ion battery cell—one major category of materials is P2-type layered oxides. Unfortunately, at high state-of-charge, such materials tend to undergo a phase transition with a very large volume change and consequent structural degradation during long-term cycling. Here we address this issue by introducing vacancies into the transition metal layer of P2-Na$_{0.7}$Fe$_{0.1}$Mn$_{0.75\square}$$_{0.15}$O2 (‘$_{\square}$’ represents a vacancy). The transition metal vacancy serves to suppress migration of neighbouring Na ions and therefore maintain structural and thermal stability in Na-depleted states. Moreover, the specific Na-O-$_{\square}$ configuration triggers a reversible anionic redox reaction and boosts the energy density. As a result, the cathode design here enables pouch cells with energy densities of 170 Wh kg -1 and 120 Wh kg -1 that can operate for over 600 and 1,000 cycles, respectively. In conclusion, our work not only suggests a feasible strategy for cathode design but also confirms the possibility of developing a battery chemistry that features a reduced need for critical raw materials.

25 ENERGY STORAGE↗
DOE OSTI2022

The role of alkali metal cations and platinum-surface hydroxyl in the alkaline hydrogen evolution reaction

The platinum-catalysed hydrogen evolution reaction (HER) generally shows poorer kinetics in alkaline electrolyte and represents a key challenge for alkaline water electrolysis. In the presence of alkali metal cations and hydroxyl anions, the electrode–electrolyte (platinum–water) interface in an alkaline electrolyte is far more complex than that in an acidic electrolyte. Here we combine electrochemical impedance spectroscopy and an electrical transport spectroscopy approach to probe and understand the fundamental role of different cations (Li + , Na + and K + ) in HER kinetics. Our integrated studies suggest that the alkali metal cations play an indirect role in modifying the HER kinetics, with the smaller cations being less destabilizing to the hydroxyl adsorbate (OH ad ) species in the HER potential window, which favours a higher coverage of OH ad on the platinum surface. Here, the surface OH ad species are highly polar and act as both electronically favoured proton acceptors and geometrically favoured proton donors to promote water dissociation in alkaline media, thus boosting the Volmer-step kinetics and the HER activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dynamic and reversible transformations of subnanometre-sized palladium on ceria for efficient methane removal

Reversibly adjusting the active structures of supported metal catalysts in response to dynamic working conditions has long been pursued. Here we report the reaction-environment-modulated transformations of subnanometre-sized Pd on CeO 2 for efficient methane removal, leveraging the reaction environments at different stages of automotive exhaust aftertreatment. During the cold start of vehicles, inactive Pd 1 single atoms are readily transformed into PdO $x$ subnanometre clusters by CO even at room temperature with excess O 2 , resulting in boosted low-temperature CH 4 oxidation. At elevated temperatures, dispersion of PdO $x$ cluster into Pd 1 against metal sintering renders outstanding hydrothermal stability to the catalyst, to be activated during the next vehicle start. Combined experimental and computational studies elucidate the dynamically evolved Pd speciation on CeO 2 at an atomic level. In conclusion, modulating the reversible nature of supported metals helps overcome the long-existing trade-off between low-temperature activity and high-temperature stability, also providing a new paradigm for designing intelligent catalysts that brings single-atom/cluster catalysts closer to real applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An automated liquid jet for fluorescence dosimetry and microsecond radiolytic labeling of proteins

X-ray radiolytic labeling uses broadband X-rays for in situ hydroxyl radical labeling to map protein interactions and conformation. High flux density beams are essential to overcome radical scavengers. However, conventional sample delivery environments, such as capillary flow, limit the use of a fully unattenuated focused broadband beam. An alternative is to use a liquid jet, and we have previously demonstrated that use of this form of sample delivery can increase labeling by tenfold at an unfocused X-ray source. Here we report the first use of a liquid jet for automated inline quantitative fluorescence dosage characterization and sample exposure at a high flux density microfocused synchrotron beamline. Our approach enables exposure times in single-digit microseconds while retaining a high level of side-chain labeling. This development significantly boosts the method’s overall effectiveness and efficiency, generates high-quality data, and opens up the arena for high throughput and ultrafast time-resolved in situ hydroxyl radical labeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN’s utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Chemical control of excited-state reactivity of the anionic green fluorescent protein chromophore

Controlling excited-state reactivity is a long-standing challenge in photochemistry, as a desired pathway may be inaccessible or compete with other unwanted channels. An important example is internal conversion of the anionic green fluorescent protein (GFP) chromophore where non-selective progress along two competing torsional modes (P: phenolate and I: imidazolinone) impairs and enables Z-to-E photoisomerization, respectively. Developing strategies to promote photoisomerization could drive new areas of applications of GFP-like proteins. Motivated by the charge-transfer dichotomy of the torsional modes, we explore chemical substitution on the P-ring of the chromophore as a way to control excited-state pathways and improve photoisomerization. As demonstrated by methoxylation, selective P-twisting appears difficult to achieve because the electron-donating potential effects of the substituents are counteracted by inertial effects that directly retard the motion. Conversely, these effects act in concert to promote I-twisting when introducing electron-withdrawing groups. Specifically, 2,3,5-trifluorination leads to both pathway selectivity and a more direct approach to the I-twisted intersection which, in turn, doubles the photoisomerization quantum yield. Our results suggest P-ring engineering as an effective approach to boost photoisomerization of the anionic GFP chromophore.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Photon acceleration of high-intensity vector vortex beams into the extreme ultraviolet

Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam–driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particlein- cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below 10 18 W/cm 2 to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding 10 20 W/cm 2 over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light–matter interactions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

08 HYDROGEN↗
DOE OSTI2025

Projected runoff declines from plant physiological effects on precipitation

Abstract The impact of plants on runoff under high atmospheric CO 2 is a major uncertainty for future water resources. Theory and Earth system models (ESMs) suggest that stricter plant stomatal regulation under high CO 2 will reduce transpiration, potentially boosting runoff. Yet, across a 12-member ensemble of idealized ESM simulations that isolate plant responses to CO 2 , we show that lower transpiration robustly enhances runoff over only 5% of modelled global land area. Precipitation changes are five times more important than transpiration changes in driving runoff responses and are a significant signal of CO 2 physiological forcing over 31–57% of land areas across models. Crucially, ESMs largely disagree on where physiologically forced precipitation changes occur but agree that plant responses in most locations are as likely to reduce runoff as increase it. These results imply that large model uncertainties in precipitation responses, rather than transpiration responses, explain why ESMs disagree on plant physiologically driven runoff changes.

Lesk, Corey S. (ORCID:0000000308684040)↗
DOE OSTI2024

Directing CO 2 electroreduction pathways for selective C 2 product formation using single-site doped copper catalysts

Manipulating the selectivity-determining step in post-C–C coupling is crucial for enhancing C 2 product specificity during electrocatalytic CO 2 reduction, complementing efforts to boost rate-determining step kinetics. Here we highlight the role of single-site noble metal dopants on Cu surfaces in influencing C–O bond dissociation in an oxygen-bound selectivity-determining intermediate, steering post-C–C coupling toward ethylene versus ethanol. Integrating theoretical and experimental analyses, we demonstrate that the oxygen binding strength of the Cu surface controls the favorability of C–O bond scission, thus tuning the selectivity ratio of ethylene-to-ethanol. The Rh-doped Cu catalyst with optimal oxygen binding energy achieves a Faradaic efficiency toward ethylene of 61.2% and an ethylene-to-ethanol Faradaic efficiency ratio of 4.51 at –0.66 V versus RHE (reversible hydrogen electrode). Integrating control of both rate-determining and selectivity-determining steps further raises ethylene Faradaic efficiency to 68.8% at 1.47 A cm -2 in a tandem electrode. Our insights guide the rational design of Cu-based catalysts for selective CO 2 electroreduction to a single C 2 product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2021

Hybrid magnetic nanoparticles as efficient nanoheaters in biomedical applications

Heating at the nanoscale is the basis of several biomedical applications, including magnetic hyperthermia therapies and heat-triggered drug delivery. The combination of multiple inorganic materials in hybrid magnetic nanoparticles provides versatile platforms to achieve an efficient heat delivery upon different external stimuli or to get an optical feedback during the process. However, the successful design and application of these nanomaterials usually require intricate synthesis routes and their magnetic response is still not fully understood. In this review we give an overview of the novel systems reported in the last few years, which have been mostly obtained by organic phase-based synthesis and epitaxial growth processes. Since the heating efficiency of hybrid magnetic nanoparticles often relies on the exchange-interaction between their components, we discuss various interface-phenomena that are responsible for their magnetic properties. Finally, followed by a brief comment on future directions in the field, we outline recent advances on multifunctional nanoparticles that can boost the heating power with light and combine heating and temperature sensing in a single nanomaterial.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding of A-site deficiency in layered perovskites: promotion of dual reaction kinetics for water oxidation and oxygen reduction in protonic ceramic electrochemical cells

Protonic ceramic electrochemical cells (PCECs) are promising solid-state energy conversion devices which enable the conversion of energy between electricity and hydrogen at intermediate temperatures. Rapid conversion between chemical and electrical energy via PCEC technology will assist in overcoming grand challenges in energy storage. To achieve highly efficient reversible operation between hydrogen production and electricity generation, boosting water-oxidation and oxygen reduction activities of the oxygen electrode while maintaining the durable operation is one of the early-stage technical opportunities. In this study, an A-site deficient layered perovskite (PrBa 0.8 Ca 0.2 ) 0.95 Co 2 O 6–δ has been developed as an oxygen electrode for a PCEC which presents superior electrochemical performances. The electrolysis current density reached as high as –0.72 A cm –2 at 1.3 V, and a peak power density of 0.540 W cm –2 was obtained at 600 °C in electrolysis and fuel cell mode, respectively. The PCEC with the new electrode shows good durability under practical operating conditions for 160 hours in both operating modes with no observable degradation. Here, the reversibility between the electrolysis and fuel cell mode is also successfully demonstrated.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

The high-yield direct synthesis of dimethyl ether from CO 2 and H 2 in a dry reaction environment

Direct dimethyl ether (DME) synthesis from CO 2 and H 2 is considered a promising production route. However, this process is thermodynamically and kinetically restricted by the presence of water as a byproduct. In this study, a water-conduction membrane was incorporated into the direct DME synthesis reactor to generate a dry reaction environment. In the absence of water, the activities of the CO 2 hydrogenation catalyst (CZA) and methanol dehydration catalyst (HZSM-5) were boosted 4- and 10-fold, respectively, and single-pass CO 2 conversion of up to 73.4%, far beyond equilibrium, and DME yields of up to 54.5% were obtained. Moreover, the good protection of the catalysts against fast deactivation was demonstrated.

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