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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Molecular Coatings Improve the Selectivity and Durability of CO 2 Reduction Chalcogenide Photocathodes

The quest for solar-driven conversion of carbon dioxide to chemicals and fuels hinges upon the identification of an efficient, durable, and selective photocathode. Chalcogenide p-type semiconductors exemplified by chalcopyrite Cu(In,Ga)Se 2 (CIGS) have been effectively deployed as photocathodes. However, selectivity toward CO 2 reduction and durability of the commonly used CdS adlayer remain primary challenges. Here, we demonstrate that for the wide band gap CuGa 3 Se 5 chalcopyrite absorber these challenges are well addressed by an organic coating generated in situ from an N,N'-(1,4-phenylene)bispyridinium ditriflate salt in the electrolyte. The molecular additive provides a 30-fold increase in selectivity toward CO2R products compared to the unmodified system and lowers Cd corrosion at least 10-fold. This dual functionality highlights the promise of hybrid solid-state-molecular photocathodes for enabling durable and efficient solar fuel systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Can Cobalt Be Eliminated from Lithium-Ion Batteries?

Following the discovery of LiCoO 2 (LCO) as a cathode in the 1980s, layered oxides have enabled lithium-ion batteries (LIBs) to power portable electronic devices that sparked the digital revolution of the 21st century. Since then, LiNi x Mn y Co z O 2 (NMC) and LiNi x Co y Al z O 2 (NCA) have emerged as the leading cathodes for LIBs in electric vehicle (EV) application and have become crucial components in the fight against global warming. However, the surging demand for LIBs has led to an extremely tight supply. The EV sector has already dominated the LIB market, even though EV sales were only 2–3% of total passenger vehicle sales in 2020. EV sales are expected to grow 10-fold by the end of this decade, and close to 90% of the total LIB demand will come from the EV sector. As a result, LIB manufacturers must aggressively ramp up cell production to keep pace with the immense growth of the EV market, a quest that brings scrutiny to the cost and sustainability of current LIB manufacturing practices. Here, cathodes are a critical component that largely determines the energy density and 40–50% of the total cell cost in LIBs. Rigorous consideration of the cathode performance and material cost is crucial in sustaining EV adoption. In this Viewpoint, we discuss why using cobalt in cathodes is unsustainable in the long run and highlight the features of cobalt-free cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Solution-Processed Ultrafast, Room-Temperature Single-Photon Source at 1550 nm

Single photons are cornerstones of quantum technologies. The need for an ultrafast, bright, and stable photon source emitting in the telecom-band at ∼1550 nm, ideally operating at room temperature, has resulted in a decades-long quest. However, to date, telecom sources are hampered by inherently long radiative lifetimes that severely limit brightness and speed, material instability, or the need for cryogenic operation. Here, in this study, stable colloidal PbS/CdS quantum dots emitting at 1550 nm (C-band) or 1350 nm (O-band) are embedded in a solution-synthesized nanoparticle-on-mirror cavity. Single cavity-coupled quantum dots experience extreme Purcell factors up to 10,700, resulting in ultrafast emission lifetimes of 65 ps, along with near-complete blinking suppression. As a result, 12 million single photons are emitted per second affording a single photon source at 1550 nm that is more than two orders of magnitude brighter than previously possible at room-temperature. These telecom-band single photon sources are solution-processable and lithography-free and may leverage mature colloidal fabrication technologies for future quantum applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unraveling Triplet Formation Mechanisms in Acenothiophene Chromophores

The evolution of molecular platforms for singlet fission (SF) chromophores has fueled the quest for new compounds capable of generating triplets quantitatively at fast time scales. As the exploration of molecular motifs for SF has diversified, a key challenge has emerged in identifying when the criteria for SF have been satisfied. Here, we show how covalently bound molecular dimers uniquely provide a set of characteristic optical markers that can be used to distinguish triplet pair formation from processes that generate an individual triplet. These markers are contained within (i) triplet charge-transfer excited state absorption features, (ii) kinetic signatures of triplet-triplet annihilation processes, and (iii) the modulation of triplet formation rates using bridging moieties between chromophores. Our assignments are verified by time-resolved electron paramagnetic resonance (EPR) measurements, which directly identify triplet pairs by their electron spin and polarization patterns. Here we apply these diagnostic criteria to dimers of acenothiophene derivatives in solution that were recently reported to undergo efficient intermolecular SF in condensed media. While the electronic structure of these heteroatom-containing chromophores can be broadly tuned, the effect of their enhanced spin-orbit coupling and low-energy nonbonding orbitals on their SF dynamics has not been fully determined. We find that SF is fast and efficient in tetracenothiophene but that anthradithiophene exhibits fast intersystem crossing due to modifications of the singlet and triplet excited state energies upon functionalization of the heterocycle. We conclude that it is not sufficient to assign SF based on comparisons of the triplet formation kinetics between monomer and multichromophore systems.

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

Direct Evidence for Excited Ligand Field State-based Oxidative Photoredox Chemistry of a Cobalt(III) Polypyridyl Photosensitizer

Increasing interest in sustainable chemistry coupled with the quest to explore new reactivity has spurred research on first-row transition metal complexes for potential applications in a variety of settings. One of the more active areas of research is photoredox catalysis, where the synthetically tunable nature of their electronic structures provides a rich palette of options for tailoring their reactivity to a desired chemical transformation. Understanding the mechanism of excited-state reactivity is critical for the informed development of next-generation catalysts, which in turn requires information concerning the propensity of their electronic excited states to engage in the desired electron or energy transfer processes. Herein we provide direct evidence of the highly oxidizing nature of the lowest-energy ligand-field (LF) excited state of a first-row d 6 -low-spin Co(III) photosensitizer [Co(4,4'-Br 2 bpy) 3 ] 3+ (where 4,4'-Br 2 bpy is 4,4'-dibromo-2,2'-bipyridine). The redox potential associated with the LF excited state of the Co(III) complex was bracketed by performing bimolecular quenching studies by using a series of simple organic electron donors. Time-resolved absorption spectroscopy confirmed a dynamic quenching process attributed to reductive quenching of the lowest-energy ligand-field excited state of the Co(III) chromophore. Analysis of the Stern–Volmer plots for each chromophore-quencher pair revealed a limiting value of E red * ~ 1.25 V vs Fc/Fc + for the metal-centered excited state, which is significantly stronger than that of more commonly employed transition metal-based photoredox agents such as [Ru(bpy) 3 ] 2+ (E red * = 0.32 V vs Fc/Fc + ) and [Ir(ppy) 2 (bpy)] + (E red * = 0.27 V vs Fc/Fc + ). Finally, these results suggest that this class of chromophores could find utility in applications requiring the activation of oxidatively resistant organic substrates for photoredox catalysis.

14 SOLAR ENERGY↗
DOE OSTI2025

Decoding the Oxygen Activity with Iron through Ligand-to-Metal Charge Transfer in Li-Rich Layered Cathodes

The quest for high-energy-density and cost-effective cathode materials has revitalized lithium-rich iron-based oxides, where Fe 3+ ions with their stable half-filled d 5 electronic configuration demonstrate unique potentials in regulating oxygen redox activity through ligand-to-metal charge transfer. Using operando 57 Fe Mössbauer spectroscopy, synchrotron X-ray techniques, and density functional theory calculations, we unravel the enhanced Fe–O redox activity through the LMCT process and the concomitant evolution of local structure distortion in the Li-rich layered system Li 1.2 MO 2 (M = Ni, Mn, Fe). The metastable Fe 4+ intermediates facilitate early activation of oxygen redox through LMCT along with increased delithiation at a lowered potential (below 4.5 V). Here, this process inherently suppresses Jahn–Teller distortion of Fe 4+ and exhibits Fe–O redox dominance over conventional Fe 3+ /Fe 4+ redox in Li-rich oxides. The Fe–O coupling redox exhibits improved initial Coulombic efficiency and 95% capacity retention over 200 cycles at 4.4 V. However, the LMCT process is accompanied by aggravated cation migration and voltage fade at low cutoff potentials, which requires further mitigation for the utilization of Fe–O redox. Overall, these findings demonstrate the regulating mechanism of iron for oxygen redox through LMCT and provide fundamental insights into the design principles for high-performance, cost-effective iron-based cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗
DOE OSTI2022

High field magnetometry with hyperpolarized nuclear spins

Abstract Quantum sensors have attracted broad interest in the quest towards sub-micronscale NMR spectroscopy. Such sensors predominantly operate at low magnetic fields. Instead, however, for high resolution spectroscopy, the high-field regime is naturally advantageous because it allows high absolute chemical shift discrimination. Here we demonstrate a high-field spin magnetometer constructed from an ensemble of hyperpolarized 13 C nuclear spins in diamond. They are initialized by Nitrogen Vacancy (NV) centers and protected along a transverse Bloch sphere axis for minute-long periods. When exposed to a time-varying (AC) magnetic field, they undergo secondary precessions that carry an imprint of its frequency and amplitude. For quantum sensing at 7T, we demonstrate detection bandwidth up to 7 kHz, a spectral resolution < 100mHz, and single-shot sensitivity of 410pT $$/\sqrt{{{{{{{{\rm{Hz}}}}}}}}}$$ / Hz . This work anticipates opportunities for microscale NMR chemical sensors constructed from hyperpolarized nanodiamonds and suggests applications of dynamic nuclear polarization (DNP) in quantum sensing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

From an antiferromagnetic insulator to a strongly correlated metal in square-lattice MCl 2 (pyrazine) 2 coordination solids

Electronic synergy between metal ions and organic linkers is a key to engineering molecule-based materials with a high electrical conductivity and, ultimately, metallicity. To enhance conductivity in metal-organic solids, chemists aim to bring the electrochemical potentials of the constituent metal ions and bridging organic ligands closer in a quest to obtain metal- d and ligand- π admixed frontier bands. Herein, we demonstrate the critical role of the metal ion in tuning the electronic ground state of such materials. While VCl 2 (pyrazine) 2 is an electrical insulator, TiCl 2 (pyrazine) 2 displays the highest room-temperature electronic conductivity (5.3 S cm –1 ) for any metal-organic solid involving octahedrally coordinated metal ions. Notably, TiCl 2 (pyrazine) 2 exhibits Pauli paramagnetism consistent with the specific heat, supporting the existence of a Fermi liquid state (i.e., a correlated metal). This result widens perspectives for designing molecule-based systems with strong metal-ligand covalency and electronic correlations.

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

A quinary WTaCrVHf nanocrystalline refractory high-entropy alloy withholding extreme irradiation environments

In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission & fusion reactors, space applications, etc.), design, prediction and control of advanced materials beyond current material designs become paramount. Here, through a combined experimental and simulation methodology, we design a nanocrystalline refractory high entropy alloy (RHEA) system. Compositions assessed under extreme environments and in situ electron-microscopy reveal both high thermal stability and radiation resistance. We observe grain refinement under heavy ion irradiation and resistance to dual-beam irradiation and helium implantation in the form of low defect generation and evolution, as well as no detectable grain growth. The experimental and modeling results—showing a good agreement—can be applied to design and rapidly assess other alloys subjected to extreme environmental conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Layered metals as polarized transparent conductors

The quest to improve transparent conductors balances two key goals: increasing electrical conductivity and increasing optical transparency. To improve both simultaneously is hindered by the physical limitation that good metals with high electrical conductivity have large carrier densities that push the plasma edge into the ultra-violet range. Technological solutions reflect this trade-off, achieving the desired transparencies only by reducing the conductor thickness or carrier density at the expense of a lower conductance. Here we demonstrate that highly anisotropic crystalline conductors offer an alternative solution, avoiding this compromise by separating the directions of conduction and transmission. We demonstrate that slabs of the layered oxides Sr 2 RuO 4 and Tl 2 Ba 2 CuO 6+δ are optically transparent even at macroscopic thicknesses >2 μm for c-axis polarized light. Underlying this observation is the fabrication of out-of-plane slabs by focused ion beam milling. This work provides a glimpse into future technologies, such as highly polarized and addressable optical screens.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nanoscale polarization transient gratings

Light manipulation at the nanoscale is essential both for fundamental science and modern technology. The quest to shorter lengthscales, however, requires the use of light wavelengths beyond the visible. In particular, in the extreme ultraviolet regime these manipulation capabilities are hampered by the lack of efficient optics, especially for polarization control. Here, we present a method to create periodic, polarization modulations at the nanoscale using a tailored configuration of the FERMI free electron laser and demonstrate its capabilities by comparing the dynamics induced by this polarization transient grating with those driven by a conventional intensity grating on a thin ferrimagnetic alloy. While the intensity grating signal is dominated by the thermoelastic response, the polarization grating excitation minimizes it, uncovering helicity-dependent responses previously undetected. We anticipate nanoscale polarization transient gratings to become useful for the study of physical, chemical and biological systems possessing chiral symmetry.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ultrahigh-power-density flexible piezoelectric energy harvester based on freestanding ferroelectric oxide thin films

Flexible piezoelectric nanogenerators are emerging as a promising solution for powering next-generation flexible electronics by converting mechanical energy into electrical energy. However, traditional ferroelectric ceramics, despite their excellent piezoelectric properties, lack flexibility; while piezoelectric polymers, although highly flexible, have low piezoelectricity. The quest to develop materials that combine high piezoelectricity with exceptional flexibility has thus become a research focus. Herein, we present a breakthrough in this field with the fabrication of freestanding (111)-oriented PbZr 0.52 Ti 0.48 O 3 single crystalline thin films, which exhibit remarkable flexibility and a high converse piezoelectric coefficient (~585 pm/V). This is achieved through water-soluble sacrificial layer to relieve substrate clamping and controlling the crystal orientation to further enhance the piezoelectric response. Our nanogenerators, constructed using these freestanding nanoscale membranes, demonstrate a record-high output power density (~63.5 mW/cm 3 ), excellent flexibility (with a strain tolerance >3.4%), and superior mechanical stability in cycling tests (>60,000 cycles). These advancements pave the way for high-performance, flexible electronic devices utilizing ferroelectric oxide thin films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Spin excitations and flat electronic bands in a Cr-based kagome superconductor

In the quest for topology- and correlation-driven quantum states, kagome lattice materials have garnered significant interest for their band structures, featuring flat bands (FBs) from the quantum destructive interference of the electronic wavefunction. Tuning an FB to the chemical potential could induce electronic instabilities and emergent orders. Despite extensive studies, direct evidence of FBs tuned to the chemical potential and their role in emergent orders in bulk materials remains lacking. Using angle-resolved photoemission spectroscopy, resonant inelastic X-ray scattering, and density functional theory, we show that the low-energy structure of the Cr-based kagome metal superconductor CsCr 3 Sb 5 is dominated by FBs at the Fermi level. We also observe low-energy magnetic excitations evolving across the low-temperature transition, largely consistent with the FB shift. Our results suggest that the low-temperature order contains a magnetic origin and that the kagome FBs may play a role in the emergence of this order.

electronic properties and materials↗
DOE OSTI2021

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2025

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Neural Network↗