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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fast wide-field upconversion luminescence lifetime thermometry enabled by single-shot compressed ultrahigh-speed imaging

Photoluminescence lifetime imaging of upconverting nanoparticles is increasingly featured in recent progress in optical thermometry. Despite remarkable advances in photoluminescent temperature indicators, existing optical instruments lack the ability of wide-field photoluminescence lifetime imaging in real time, thus falling short in dynamic temperature mapping. Here, we report video-rate upconversion temperature sensing in wide field using single-shot photoluminescence lifetime imaging thermometry (SPLIT). Developed from a compressed-sensing ultrahigh-speed imaging paradigm, SPLIT first records wide-field luminescence intensity decay compressively in two views in a single exposure. Then, an algorithm, built upon the plug-and-play alternating direction method of multipliers, is used to reconstruct the video, from which the extracted lifetime distribution is converted to a temperature map. Using the core/shell NaGdF 4 :Er 3+ ,Yb 3+ /NaGdF 4 upconverting nanoparticles as the lifetime-based temperature indicators, we apply SPLIT in longitudinal wide-field temperature monitoring beneath a thin scattering medium. SPLIT also enables video-rate temperature mapping of a moving biological sample at single-cell resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity

Abstract Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m 2 /day, bringing the minimum biomass selling price down to approximately $281 per ton.

09 BIOMASS FUELS↗
DOE OSTI2022

A metal-supported single-atom catalytic site enables carbon dioxide hydrogenation

Nitrogen-doped graphene-supported single atoms convert CO 2 to CO, but fail to provide further hydrogenation to methane – a finding attributable to the weak adsorption of CO intermediates. To regulate the adsorption energy, here we investigate the metal-supported single atoms to enable CO 2 hydrogenation. We find a copper-supported iron-single-atom catalyst producing a high-rate methane. Density functional theory calculations and in-situ Raman spectroscopy show that the iron atoms attract surrounding intermediates and carry out hydrogenation to generate methane. The catalyst is realized by assembling iron phthalocyanine on the copper surface, followed by in-situ formation of single iron atoms during electrocatalysis, identified using operando X-ray absorption spectroscopy. The copper-supported iron-single-atom catalyst exhibits a CO 2 -to-methane Faradaic efficiency of 64% and a partial current density of 128 mA cm –2 , while the nitrogen-doped graphene-supported one produces only CO. The activity is 32 times higher than a pristine copper under the same conditions of electrolyte and bias.

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

Enabling high energy lithium metal batteries via single-crystal Ni-rich cathode material co-doping strategy

High-capacity Ni-rich layered oxides are promising cathode materials for secondary lithium-based battery systems. However, their structural instability detrimentally affects the battery performance during cell cycling. Here, we report an Al/Zr co-doped single-crystalline LiNi<:sub>0.88 Co 0.09 Mn 0.03 O 2 (SNCM) cathode material to circumvent the instability issue. We found that soluble Al ions are adequately incorporated in the SNCM lattice while the less soluble Zr ions are prone to aggregate in the outer SNCM surface layer. The synergistic effect of Al/Zr co-doping in SNCM lattice improve the Li-ion mobility, relief the internal strain, and suppress the Li/Ni cation mixing upon cycling at high cut-off voltage. These features improve the cathode rate capability and structural stabilization during prolonged cell cycling. In particular, the Zr-rich surface enables the formation of stable cathode-electrolyte interphase, which prevent SNCM from unwanted reactions with the non-aqueous fluorinated liquid electrolyte solution and avoid Ni dissolution. To prove the practical application of the Al/Zr co-doped SNCM, we assembled a 10.8 Ah pouch cell (using a 100 μm thick Li metal anode) capable of delivering initial specific energy of 504.5 Wh kg- 1 at 0.1 °C and 25 °C.

25 ENERGY STORAGE↗
DOE OSTI2022

Cell-free prototyping enables implementation of optimized reverse β-oxidation pathways in heterotrophic and autotrophic bacteria

Abstract Carbon-negative synthesis of biochemical products has the potential to mitigate global CO 2 emissions. An attractive route to do this is the reverse β-oxidation (r-BOX) pathway coupled to the Wood-Ljungdahl pathway. Here, we optimize and implement r-BOX for the synthesis of C4-C6 acids and alcohols. With a high-throughput in vitro prototyping workflow, we screen 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity. Implementation of these pathways into Escherichia coli generates designer strains for the selective production of butanoic acid (4.9 ± 0.1 gL −1 ), as well as hexanoic acid (3.06 ± 0.03 gL −1 ) and 1-hexanol (1.0 ± 0.1 gL −1 ) at the best performance reported to date in this bacterium. We also generate Clostridium autoethanogenum strains able to produce 1-hexanol from syngas, achieving a titer of 0.26 gL −1 in a 1.5 L continuous fermentation. Our strategy enables optimization of r-BOX derived products for biomanufacturing and industrial biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

RIViT-seq enables systematic identification of regulons of transcriptional machineries

Abstract Transcriptional regulation is a critical process to ensure expression of genes necessary for growth and survival in diverse environments. Transcription is mediated by multiple transcription factors including activators, repressors and sigma factors. Accurate computational prediction of the regulon of target genes for transcription factors is difficult and experimental identification is laborious and not scalable. Here, we demonstrate regulon identification by in vitro transcription-sequencing (RIViT-seq) that enables systematic identification of regulons of transcription factors by combining an in vitro transcription assay and RNA-sequencing. Using this technology, target genes of 11 sigma factors were identified in Streptomyces coelicolor A3(2). The RIViT-seq data expands the transcriptional regulatory network in this bacterium, discovering regulatory cascades and crosstalk between sigma factors. Implementation of RIViT-seq with other transcription factors and in other organisms will improve our understanding of transcriptional regulatory networks across biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A modular vaccine platform enabled by decoration of bacterial outer membrane vesicles with biotinylated antigens

Engineered outer membrane vesicles (OMVs) derived from Gram-negative bacteria are a promising technology for the creation of non-infectious, nanoparticle vaccines against diverse pathogens. However, antigen display on OMVs can be difficult to control and highly variable due to bottlenecks in protein expression and localization to the outer membrane of the host cell, especially for bulky and/or complex antigens. Here, we describe a universal approach for avidin-based vaccine antigen crosslinking (AvidVax) whereby biotinylated antigens are linked to the exterior of OMVs whose surfaces are remodeled with multiple copies of a synthetic antigen-binding protein (SNAP) comprised of an outer membrane scaffold protein fused to a biotin-binding protein. We show that SNAP-OMVs can be readily decorated with a molecularly diverse array of biotinylated subunit antigens, including globular and membrane proteins, glycans and glycoconjugates, haptens, lipids, and short peptides. When the resulting OMV formulations are injected in mice, strong antigen-specific antibody responses are observed that depend on the physical coupling between the antigen and SNAP-OMV delivery vehicle. Overall, these results demonstrate AvidVax as a modular platform that enables rapid and simplified assembly of antigen-studded OMVs for application as vaccines against pathogenic threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enabling selective zinc-ion intercalation by a eutectic electrolyte for practical anodeless zinc batteries

Abstract Two major challenges hinder the advance of aqueous zinc metal batteries for sustainable stationary storage: (1) achieving predominant Zn-ion (de)intercalation at the oxide cathode by suppressing adventitious proton co-intercalation and dissolution, and (2) simultaneously overcoming Zn dendrite growth at the anode that triggers parasitic electrolyte reactions. Here, we reveal the competition between Zn 2+ vs proton intercalation chemistry of a typical oxide cathode using ex-situ/ operando techniques, and alleviate side reactions by developing a cost-effective and non-flammable hybrid eutectic electrolyte. A fully hydrated Zn 2+ solvation structure facilitates fast charge transfer at the solid/electrolyte interface, enabling dendrite-free Zn plating/stripping with a remarkably high average coulombic efficiency of 99.8% at commercially relevant areal capacities of 4 mAh cm −2 and function up to 1600 h at 8 mAh cm −2 . By concurrently stabilizing Zn redox at both electrodes, we achieve a new benchmark in Zn-ion battery performance of 4 mAh cm −2 anode-free cells that retain 85% capacity over 100 cycles at 25 °C. Using this eutectic-design electrolyte, Zn | |Iodine full cells are further realized with 86% capacity retention over 2500 cycles. The approach represents a new avenue for long-duration energy storage.

25 ENERGY STORAGE↗
DOE OSTI2024

Vapor-induced phase-separation-enabled versatile direct ink writing

Abstract Versatile printing of polymers, metals, and composites always calls for simple, economic approaches. Here we present an approach to three-dimensional (3D) printing of polymeric, metallic, and composite materials at room conditions, based on the polymeric vapor-induced phase separation (VIPS) process. During VIPS 3D printing (VIPS-3DP), a dissolved polymer-based ink is deposited in an environment where nebulized non-solvent is present, inducing the low-volatility solvent to be extracted from the filament in a controllable manner due to its higher chemical affinity with the non-solvent used. The polymeric phase is hardened in situ as a result of the induced phase separation process. The low volatility of the solvent enables its reclamation after the printing process, significantly reducing its environmental footprint. We first demonstrate the use of VIPS-3DP for polymer printing, showcasing its potential in printing intricate structures. We further extend VIPS-3DP to the deposition of polymer-based metallic inks or composite powder-laden polymeric inks, which become metallic parts or composites after a thermal cycle is applied. Furthermore, spatially tunable porous structures and functionally graded parts are printed by using the printing path to set the inter-filament porosity as well as an inorganic space-holder as an intra-filament porogen.

42 ENGINEERING↗
DOE OSTI2024

Local ionic transport enables selective PGM-free bipolar membrane electrode assembly

Bipolar membranes in electrochemical CO 2 conversion cells enable different reaction environments in the CO 2 -reduction and O 2 -evolution compartments. Under ideal conditions, water-splitting in the bipolar membrane allows for platinum-group-metal-free anode materials and high CO 2 utilizations. In practice, however, even minor unwanted ion crossover limits stability to short time periods. Here we report the vital role of managing ionic species to improve CO 2 conversion efficiency while preventing acidification of the anodic compartment. Through transport modelling, we identify that an anion-exchange ionomer in the catalyst layer improves local bicarbonate availability and increasing the proton transference number in the bipolar membranes increases CO 2 regeneration and limits K + concentration in the cathode region. Through experiments, we show that a uniform local distribution of bicarbonate ions increases the accessibility of reverted CO 2 to the catalyst surface, improving Faradaic efficiency and limiting current densities by twofold. Using these insights, we demonstrate a fully platinum-group-metal-free bipolar membrane electrode assembly CO 2 conversion system exhibiting <1% CO 2 /cation crossover rates and 80-90% CO 2 -to-CO utilization efficiency over 150 h operation at 100 mA cm -2 without anolyte replenishment.

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

Modulating physicochemical interfaces enables li-rich oxides based ceramic solid-state li batteries under ambient conditions

Li-rich layered oxides exhibit promising potential applications in high-energy-density solid-state lithium metal batteries. Nevertheless, the strong oxidative oxygen species generate at high voltage, which poses great challenges to positive electrode-side interface stability. Herein, a robust in-situ polymerization gel polymer electrolyte with bifunctional additives is designed for interface modification. These additives, include lithium difluoro(oxalate) borate and LiPO 2 F 2 , regulate the Li + chemical environment in gel polymer electrolyte to enhance crosslink density without residual oligomer, which reduce gas generation and suppress contact loss, thus avoiding interfacial impedance divergence. Concurrently, the designed gel polymer electrolyte enables a wide electrochemical stability window (up to 4.7 V) and a high Li + transference number (0.82). Additionally, the additives induced F- and B-rich inorganic cathode-electrolyte interphase inhibits side reactions and oxygen/transition metal loss effectively, stabilizing the chemical interface. The as-constructed Li-rich layered oxides-based ceramic solid-state lithium metal batteries with gel polymer electrolyte interface modification exert a high discharge capacity of 276.5 mAh g -1 at 30 °C without external pressure, delivering a retention of 81.7% after 100 cycles at 25 mA g -1 during 2.0-4.7 V. This work provides a guideline for developing high-voltage solid-state lithium metal batteries via interfacial design.

Hu, Xinchao [Xiamen University (China)]↗
DOE OSTI2025

Anisotropic surface potentials induced by competitive ion adsorption enable the synthesis of branched cubic Pt mesocrystals

Creation of complex nanostructured materials through oriented attachment (OA) requires the manipulation of interparticle forces, including electrostatic repulsion, which depends strongly on surface potentials and can be modified through the effect of solution environment on interfacial chemistry. Here we show that time-dependent anisotropies in surface potential driven by competitive ion adsorption can alter facet-selectivity during OA. This phenomenon enables the synthesis of branched cubic Pt mesocrystals. Initially, Pt nanoparticles attach preferentially at their {100} facets to form a well-defined cubic core. Over time, changes in ion adsorption shift the attachment preference to the {111} facets, promoting branch formation. In both stages, anisotropic surface potentials generate electrostatic torques that align the particles prior to attachment. These findings demonstrate a generalizable strategy for directing the architecture of nanomaterials through time-resolved control of interfacial chemistry during OA, offering new pathways for the design of complex mesoscale structures.

Bae, Yuna [Pacific Northwest National Laboratory (↗
DOE OSTI2025

Enabling ambient stability and quantum integration of organometallic magnonic ferrimagnets via atomic layer encapsulation

Magnons, the quanta of spin waves in magnetic materials, are promising for hybrid quantum systems by bridging electromagnetic and spin degrees of freedom. The organometallic ferrimagnet vanadium tetracyanoethylene (V[TCNE] x , x ≈ 2) is especially well-suited for quantum magnonics due to its low Gilbert damping and substrate versatility. However, its rapid chemical degradation in ambient conditions hinders practical applications. Incumbent encapsulation methods provide some protection, but are bulky, obscure intrinsic properties of V[TCNE] x , introduce thermal stress at cryogenic temperatures, and complicate microwave device integration. Here, we demonstrate that ultrathin alumina films deposited via low-temperature atomic layer deposition effectively protect V[TCNE] x by preserving its magnetic and magnonic properties following ambient exposure. The sub-100 nm transparent films also enable advanced spectroscopy, magnetometry, and cavity magnonic measurements that reveal the intrinsic properties of V[TCNE] x . This encapsulation strategy advances molecule-based quantum information science by providing a robust route toward scalable, monolithic integration in hybrid quantum technologies.

magnetic materials↗
DOE OSTI2026

Active site design enables industrial scale H 2 O 2 electrosynthesis with metal-free catalysts

The electrosynthesis of hydrogen peroxide (H 2 O 2 ) via a two-electron oxygen reduction reaction enables decentralized H 2 O 2 production. While metal-free carbon catalysts are sustainable and low-cost, their performance is hindered by poorly defined active sites and uncontrolled defect states. Here, we resolve these challenges through active site design and catalyst screening using fluorine (F) and nitrogen (N) codoped carbons as model materials. Statistical analysis combined with density functional theoretical calculations reveals that F-induced structural modification and defect passivation optimize OOH* binding, with F-doping and adjacent F atoms predominantly lowering abs ΔG(OOH*). Experimental results confirm that semi-ionic C–F bonds passivate defects in nitrogen-doped carbon, enhancing catalytic activity and durability. The resulting (N, F)-codoped carbon achieves nearly 100% H 2 O 2 selectivity at 0.5–0.65 V versus the reversible hydrogen electrode and maintains > 95% across 0.01–0.65 V versus the reversible hydrogen electrode. In an electrolyzer, (N, F)-codoped carbon exhibits an H 2 O 2 yield rate of 74.35 mol g cat. −1 h -1 and sustains 300 mA cm -2 for 105 hours with ~95% faradaic efficiency. Coupling the two-electron oxygen reduction reaction with methanol oxidation further reduces cell voltage and enhances productivity. This work provides a means to design efficient catalysts for industrial H 2 O 2 electrosynthesis.

H2O2 electrosynthesis↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Differential programming enabled functional imaging with Lorentz transmission electron microscopy

Abstract Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗