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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 OSTI2023

Homo- and hetero-dimeric subunit interactions set affinity and efficacy in metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are dimeric class C G-protein–coupled receptors that operate in glia and neurons. Glutamate affinity and efficacy vary greatly between the eight mGluRs. The molecular basis of this diversity is not understood. We used single-molecule fluorescence energy transfer to monitor the structural rearrangements of activation in the mGluR ligand binding domain (LBD). In saturating glutamate, group II homodimers fully occupy the activated LBD conformation (full efficacy) but homodimers of group III mGluRs do not. Strikingly, the reduced efficacy of Group III homodimers does not arise from differences in the glutamate binding pocket but, instead, from interactions within the extracellular dimerization interface that impede active state occupancy. By contrast, the functionally boosted mGluR II/III heterodimers lack these interface ‘brakes’ to activation and heterodimer asymmetry in the flexibility of a disulfide loop connecting LBDs greatly favors occupancy of the activated conformation. Our results suggest that dimerization interface interactions generate substantial functional diversity by differentially stabilizing the activated conformation. This diversity may optimize mGluR responsiveness for the distinct spatio-temporal profiles of synaptic versus extrasynaptic glutamate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hyperbolic exciton polaritons in a van der Waals magnet

Abstract Exciton polaritons are quasiparticles of photons coupled strongly to bound electron-hole pairs, manifesting as an anti-crossing light dispersion near an exciton resonance. Highly anisotropic semiconductors with opposite-signed permittivities along different crystal axes are predicted to host exotic modes inside the anti-crossing called hyperbolic exciton polaritons (HEPs), which confine light subdiffractionally with enhanced density of states. Here, we show observational evidence of steady-state HEPs in the van der Waals magnet chromium sulfide bromide (CrSBr) using a cryogenic near-infrared near-field microscope. At low temperatures, in the magnetically-ordered state, anisotropic exciton resonances sharpen, driving the permittivity negative along one crystal axis and enabling HEP propagation. We characterize HEP momentum and losses in CrSBr, also demonstrating coupling to excitonic sidebands and enhancement by magnetic order: which boosts exciton spectral weight via wavefunction delocalization. Our findings open new pathways to nanoscale manipulation of excitons and light, including routes to magnetic, nonlocal, and quantum polaritonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Spin-EPR-pair separation by conveyor-mode single electron shuttling in Si/SiGe

Long-ranged coherent qubit coupling is a missing function block for scaling up spin qubit based quantum computing solutions. Spin-coherent conveyor-mode electron-shuttling could enable spin quantum-chips with scalable and sparse qubit-architecture. Its key feature is the operation by only few easily tuneable input terminals and compatibility with industrial gate-fabrication. Single electron shuttling in conveyor-mode in a 420 nm long quantum bus has been demonstrated previously. Here we investigate the spin coherence during conveyor-mode shuttling by separation and rejoining an Einstein-Podolsky-Rosen (EPR) spin-pair. Compared to previous work we boost the shuttle velocity by a factor of 10000. We observe a rising spin-qubit dephasing time with the longer shuttle distances due to motional narrowing and estimate the spin-shuttle infidelity due to dephasing to be 0.7% for a total shuttle distance of nominal 560 nm. Shuttling several loops up to an accumulated distance of 3.36 μm, spin-entanglement of the EPR pair is still detectable, giving good perspective for our approach of a shuttle-based scalable quantum computing architecture in silicon.

42 ENGINEERING↗
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Exciton engineering of 2D Ruddlesden–Popper perovskites by synergistically tuning the intra and interlayer structures

Designing two-dimensional halide perovskites for high-performance optoelectronic applications requires deep understanding of the structure-property relationship that governs their excitonic behaviors. However, a design framework that considers both intra and interlayer structures modified by the A-site and spacer cations, respectively, has not been developed. Here, we use pressure to synergistically tune the intra and interlayer structures and uncover the structural modulations that result in improved optoelectronic performance. Under applied pressure, (BA) 2 (GA)Pb 2 I 7 exhibits a 72-fold boost of photoluminescence and 10-fold increase of photoconductivity. Based on the observed structural change, we introduce a structural descriptor χ that describes both the intra and interlayer characteristics and establish a general quantitative relationship between χ and photoluminescence quantum yield: smaller χ correlates with minimized trapped excitons and more efficient emission from free excitons. Building on this principle, we design a perovskite (CMA) 2 (FA)Pb 2 I 7 that exhibits a small χ and an impressive photoluminescence quantum yield of 59.3%.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Operando Raman spectroscopy uncovers hydroxide and CO species enhance ethanol selectivity during pulsed CO 2 electroreduction

Pulsed CO 2 electroreduction (CO 2 RR) has recently emerged as a facile way to in situ tune the product selectivity, in particular toward ethanol, without re-designing the catalytic system. However, in-depth mechanistic understanding requires comprehensive operando time-resolved studies to identify the kinetics and dynamics of the electrocatalytic interface. Here, we track the adsorbates and the catalyst state of pre-reduced Cu 2 O nanocubes (~30 nm) during pulsed CO 2 RR using sub-second time-resolved operando Raman spectroscopy. By screening a variety of product-steering pulse length conditions, we unravel the critical role of co-adsorbed OH and CO on the Cu surface next to the oxidative formation of Cu-O ad or CuO x /(OH) y species, impacting the kinetics of CO adsorption and boosting the ethanol selectivity. However, a too low OH ad coverage following the formation of bulk-like Cu 2 O induces a significant increase in the C 1 selectivity, while a too high OH ad coverage poisons the surface for C-C coupling. Thus, we unveil the importance of co-adsorbed OH on the alcohol formation under CO 2 RR conditions and thereby, pave the way for improved catalyst design and operating conditions.

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

ATP biosensor reveals microbial energetic dynamics and facilitates bioproduction

Adenosine-5’-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics’ impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Technological evolution of large-scale blue hydrogen production toward the U.S. Hydrogen Energy Earthshot

Hydrogen potentially has a crucial role in the U.S. transition to a net-zero emissions economy. Learning from large-scale hydrogen projects will boost technological evolution and innovation toward the U.S. Hydrogen Energy Earthshot. We apply experience curves to estimate the evolving costs of blue hydrogen production and to further examine the economic effect on technological evolution of the Inflation Reduction Act’s tax credits for carbon sequestration and clean hydrogen. Learning-by-doing alone can decrease the production cost of blue hydrogen. Without tax incentives, however, it is hard for blue hydrogen production to reach the cost target of $\$1$/kg H 2 . Here we show that the breakeven cumulative production capacity required for gas-based blue hydrogen to reach the $\$1$/kg H 2 target highly depends on tax credit, natural gas price, inflation rate, and learning rates. We make recommendations for hydrogen hub development and for accelerating technological progress toward the Hydrogen Energy Earthshot.

08 HYDROGEN↗
DOE OSTI2025

Transition metal vacancy and position engineering enables reversible anionic redox reaction for sodium storage

Triggering the anionic redox reaction is an effective approach to boost the capacity of layered transition metal (TM) oxides. However, the irreversible oxygen release and structural deterioration at high voltage remain conundrums. Herein, a strategy for Mg ion and vacancy dual doping with partial TM ions pinned in the Na layers is developed to improve both the reversibility of anionic redox reaction and structural stability of layered oxides. Both the Mg ions and vacancies (□) are contained in the TM layers, while partial Mn ions (~1.1%) occupy the Na-sites. The introduced Mg ions combined with vacancies not only create abundant nonbonding O 2p orbitals in favor of high oxygen redox capacity, but also suppress the voltage decay originated from Na–O–□ configuration. The Mn ions pinned in the Na layers act as “rivets” to restrain the slab gliding at extreme de-sodiated state and thereby inhibit the generation of cracks. The positive electrode, Na 0.67 Mn 0.011 [Mg 0.1 □ 0.07 Mn 0.83 ]O 2 , delivers an enhanced discharge capacity and decent cyclability. This study provides insights into the construction of stable layered oxide positive electrode with highly reversible anionic redox reaction for sodium storage.

Cai, Congcong [Wuhan Univ. of Technology (China)]↗
DOE OSTI2025

Ampere-level co-electrosynthesis of formate from CO 2 reduction paired with formaldehyde dehydrogenation reactions

Current catalysts face challenges with low formate selectivity at high current densities during the CO 2 electroreduction. Here, we showcase a versatile strategy to enhance the formate production on p-block metal-based catalysts by incorporating noble metal atoms on their surface, refining oxygen affinity, and tuning adsorption of the critical oxygen-bound *OCHO intermediate. The formate yield is observed to afford a volcano-like dependence on the *OCHO binding strength across a series of modified catalysts. The rhodium-dispersed indium oxide (Rh/In 2 O 3 ) catalyst exhibits impressive performances, achieving Faradaic efficiencies (FEs) of formate exceeding 90% across a broad current density range of 0.20 to 1.21 A cm −2 . In situ Raman spectroscopy and theoretical calculations reveal that the oxophilic Rh site facilitates *OCHO formation by optimizing its adsorption energy, placing Rh/In 2 O 3 near the volcano-shaped apex. A bipolar electrosynthesis system, coupling the CO 2 reduction at the cathode with the formaldehyde oxidative dehydrogenation at the anode, further boosts the FE of formate to nearly 190% with pure hydrogen generation under an ampere-level current density and a low cell voltage of 2.5 V in a membrane electrode assembly cell.

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

Bringing weak transitions to light

Abstract Weak transitions between quantum states are of fundamental importance for a broad range of phenomena from analytical biochemistry to precision physics, but generally challenge experimental detection. Due to their small cross sections scaling with the absolute square of their transition matrix elements, spectroscopic measurements often fail in particular in the presence of competing background processes. Here we introduce a general concept to break this scaling law and enhance the transition probability by exploiting a stronger laser-coupled pathway to the same excited state. We demonstrate the concept experimentally by attosecond transient absorption spectroscopy in helium atoms. The quasi-forbidden transitions from the ground state 1s 2 to the weakly coupled doubly excited 2p3dandsp 2,4− states are boosted by an order of magnitude. Enhancing single-photon-suppressed transitions can find widespread applicability, from spectral diagnostics of complex molecules in life and chemical sciences to precision spectroscopy of weak transitions in metastable atomic nuclei in the search for new physics.

Science & Technology - Other Topics↗
DOE OSTI2025

Sub-10 nm upconversion nanocrystals for long-term single-particle tracking

Lanthanide-doped upconversion nanoparticles are attractive single-molecule imaging probes due to their high photostability and anti-Stokes luminescence. However, achieving both small particle size and strong brightness has remained a major challenge, as reducing size often leads to dimmer emission. Herein, we fabricate a sub-10 nm cascade actively protected upconversion nanoparticles, which shows a 33-fold enhanced upconversion efficiency at the single-particle level compared to larger ~19 nm conventional nanoparticles. Theoretical modeling and time-resolved measurements show that emission loss mainly comes from energy leakage of Er 3+ ions to surface defects. By introducing a NaYbF 4 layer as photon-harvesting and protective intermediate layer, we minimize this energy loss and significantly boost brightness. A monolayer of inert NaLuF 4 can effectively suppress the surface quenching to Yb 3+ . Using these ultra-small bright probes, we successfully tracked single epidermal growth factor receptor molecules on live cells for up to one hour, revealing dynamic switching between different diffusion modes.

Qiu, Xiaochen [Fudan Univ., Shanghai (China); Shen↗
DOE OSTI2025

Exploring single-cell biosynthetic noise and dynamics for enhanced betaxanthin production in Escherichia coli

Cell-to-cell variability often limits the efficiency of microbial bioproduction, yet how individual cells fluctuate over time and how these fluctuations shape population-level output remain unclear. To address this issue, we tracked a heterologous betaxanthin pathway in Escherichia coli using microfluidics-assisted time-lapse microscopy, allowing simultaneous measurement of fluctuations in betaxanthin, its biosynthetic enzyme DOD and growth across generations. Here we show that over 50% of high betaxanthin producers become medium or low producers after two divisions. Betaxanthin variation primarily originates from DOD noise, with a smaller contribution from growth rate fluctuations. We further develop a stochastic model to explore various control circuits and find that pathway enzyme or metabolite-based growth selection strategies are most effective in enhancing production. We experimentally validate the model by coupling enzyme expression to nutrient availability, which enriches high producers and boosts titer by 4.4-fold. Our results highlight key sources of metabolic heterogeneity and provide a framework for designing robust microbial processes.

Bacterial systems biology↗
DOE OSTI2026

Acid-base bifunctional porous liquids

Basic solutions are essential for gas-involved catalytic transformations, yet their ultralow free volume limits gas solubility and diffusivity. Engineering permanent porosity in such reactive liquids has remained a longstanding challenge. Here, we introduce a surface-sacrifice strategy that incorporates Brønsted-acidic zeolite nanocrystals into strong organic bases to create permanently porous reactive liquids. Stereochemically controlled acid–base neutralization at the external zeolite surface forms a thin ionic solvation shell that stabilizes the dispersion while fully preserving the internal microporosity and crystallinity of the zeolite framework. The resulting basic media exhibit persistent microporosity, confirmed by inert-gas sorption, 129Xe nuclear magnetic resonance, neutron scattering, and theoretical simulations. These porous liquids show significantly enhanced catalytic performance in CO2 conversion and hydrogenation reactions. The coexisting acidic and basic sites further enable antagonistic cascade catalysis within a single liquid phase. This surface-sacrifice approach provides a general route to introduce permanent porosity into reactive media, enabling boosted gas transformations.

Qiu, Liqi [University of Tennessee, Knoxville (UTK↗
DOE OSTI2020

Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data

Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging approach. Here, we developed an approach that combines two MSI techniques, matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The known relationships between the two MSI channels of information are enforced via a physically constrained machine-learning approach and directly incorporated in the data processing. We demonstrate the robustness of this method by generating intact molecular MALDI-type spectra and chemical maps at ToF-SIMS resolution when imaging mouse brain thin tissue sections. This approach can be readily adopted for other types of bioimaging where physical relationships between methods have to be considered to boost the confidence in the reconstruction product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Optimal band structure for thermoelectrics with realistic scattering and bands

Abstract Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost z T beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes z T > 3.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Predicting solid state material platforms for quantum technologies

Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗