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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2021

Flexible and high-performance electrochromic devices enabled by self-assembled 2D TiO 2 /MXene heterostructures

Transition metal oxides (TMOs) are promising electrochromic (EC) materials for applications such as smart windows and displays, yet the challenge still exists to achieve good flexibility, high coloration efficiency and fast response simultaneously. MXenes (e.g. Ti 3 C 2 T x ) and their derived TMOs (e.g. 2D TiO 2 ) are good candidates for high-performance and flexible EC devices because of their 2D nature and the possibility of assembling them into loosely networked structures. Here we demonstrate flexible, fast, and high-coloration-efficiency EC devices based on self-assembled 2D TiO 2 /Ti 3 C 2 T x heterostructures, with the Ti 3 C 2 T x layer as the transparent electrode, and the 2D TiO 2 layer as the EC layer. Benefiting from the well-balanced porosity and connectivity of these assembled nanometer-thick heterostructures, they present fast and efficient ion and electron transport, as well as superior mechanical and electrochemical stability. We further demonstrate large-area flexible devices which could potentially be integrated onto curved and flexible surfaces for future ubiquitous electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Iridium metallene oxide for acidic oxygen evolution catalysis

Exploring new materials is essential in the field of material science. Especially, searching for optimal materials with utmost atomic utilization, ideal activities and desirable stability for catalytic applications requires smart design of materials’ structures. Herein, we report iridium metallene oxide: 1 T phase-iridium dioxide (IrO 2 ) by a synthetic strategy combining mechanochemistry and thermal treatment in a strong alkaline medium. This material demonstrates high activity for oxygen evolution reaction with a low overpotential of 197 millivolt in acidic electrolyte at 10 milliamperes per geometric square centimeter (mA cm geo –2 ). Together, it achieves high turnover frequencies of 4.2 s UPD –1 (3.0 s BET –1 ) at 1.50 V vs. reversible hydrogen electrode. Furthermore, 1T-IrO 2 also shows little degradation after 126 hours chronopotentiometry measurement under the high current density of 250 mA cm geo –2 in proton exchange membrane device. Theoretical calculations reveal that the active site of Ir in 1T-IrO 2 provides an optimal free energy uphill in *OH formation, leading to the enhanced performance. The discovery of this 1T-metallene oxide material will provide new opportunities for catalysis and other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Environmental trade-offs of direct air capture technologies in climate change mitigation toward 2100

Abstract Direct air capture (DAC) is critical for achieving stringent climate targets, yet the environmental implications of its large-scale deployment have not been evaluated in this context. Performing a prospective life cycle assessment for two promising technologies in a series of climate change mitigation scenarios, we find that electricity sector decarbonization and DAC technology improvements are both indispensable to avoid environmental problem-shifting. Decarbonizing the electricity sector improves the sequestration efficiency, but also increases the terrestrial ecotoxicity and metal depletion levels per tonne of CO 2 sequestered via DAC. These increases can be reduced by improvements in DAC material and energy use efficiencies. DAC exhibits regional environmental impact variations, highlighting the importance of smart siting related to energy system planning and integration. DAC deployment aids the achievement of long-term climate targets, its environmental and climate performance however depend on sectoral mitigation actions, and thus should not suggest a relaxation of sectoral decarbonization targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Multi-scale computer-aided design and photo-controlled macromolecular synthesis boosting uranium harvesting from seawater

By integrating multi-scale computational simulation with photo-regulated macromolecular synthesis, this study presents a new paradigm for smart design while customizing polymeric adsorbents for uranium harvesting from seawater. A dissipative particle dynamics (DPD) approach, combined with a molecular dynamics (MD) study, is performed to simulate the conformational dynamics and adsorption process of a model uranium grabber, i.e., PAO m -b-PPEGMA n , suggesting that the maximum adsorption capacity with atomic economy can be achieved with a preferred block ratio of 0.18. The designed polymers are synthesized using the PET-RAFT polymerization in a microfluidic platform, exhibiting a record high adsorption capacity of uranium (11.4 ± 1.2 mg/g) in real seawater within 28 days. This study offers an integrated perspective to quantitatively assess adsorption phenomena of polymers, bridging metal-ligand interactions at the molecular level with their spatial conformations at the mesoscopic level. The established protocol is generally adaptable for target-oriented development of more advanced polymers for broadened applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Does provable absence of barren plateaus imply classical simulability?

A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We collect evidence-on a case-by-case basis-that many commonly used models whose loss landscapes avoid barren plateaus can also admit classical simulation, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing that quantum computers can be essential for collecting data, our analysis sheds doubt on the information processing capabilities of many parametrized quantum circuits with provably barren plateau-free landscapes. We end by discussing the (many) caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Unprecedented non-hysteretic superelasticity of [001]-oriented NiCoFeGa single crystals

Superelasticity associated with the martensitic transformation has found a broad range of engineering applications. However, the intrinsic hysteresis and temperature sensitivity of the first-order phase transformation significantly hinder the usage of smart metallic components in many critical areas. In this paper, we report a large superelasticity up to 15.2% strain in [001]-oriented NiCoFeGa single crystals, exhibiting non-hysteretic mechanical responses, a small temperature dependence and high-energy-storage capability and cyclic stability over a wide temperature and composition range. In situ synchrotron X-ray diffraction measurements show that the superelasticity is correlated with a stress-induced continuous variation of lattice parameter accompanied by structural fluctuation. Neutron diffraction and electron microscopy observations reveal an unprecedented microstructure consisting of atomic-level entanglement of ordered and disordered crystal structures, which can be manipulated to tune the superelasticity. The discovery of the large elasticity related to the entangled structure paves the way for exploiting elastic strain engineering and development of related functional materials. NiCoFeGa single crystals exhibit large non-hysteretic superelasticity over broad temperature and composition ranges. It is attributed to the continuous phase transition with applied stress, which is related to the fluctuation of entangled ordered and disordered crystal structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of carrier behavior in photonically excited 6H silicon carbide exhibiting fast, high voltage, bulk transconductance properties

Abstract Unabated, worldwide trends in CO 2 production project growth to > 43-BMT per year over the next two decades. Efficient power electronics are crucial to fully realizing the CO 2 mitigating benefits of a worldwide smart grid (~ 18% reduction for the United States alone). Even state-of-the-art SiC high voltage junction devices are inefficient because of slow transition times (~ 0.5-μs) and limited switching rates at high voltage (~ 20-kHz at ≥ 15-kV) resulting from the intrinsically limited charge carrier drift speed (< 2 × 10 7 -cm-s −1 ). Slow transition times and limited switch rates waste energy through transition loss and hysteresis loss in external magnetic components. Bulk conduction devices, where carriers are generated and controlled nearly simultaneously throughout the device volume, minimize this loss. Such devices are possible using below bandgap excitation of semi-insulating (SI) SiC single crystals. We explored carrier dynamics with a 75-fs single wavelength pump/supercontinuum probe and a modified transient spectroscopy technique and also demonstrated a new class of efficient, high-speed, high-gain, bi-directional, optically-controlled transistor-like power device. At a performance level six times that of existing devices, for the first time we demonstrated prototype operation at multi-10s of kW and 20-kV, 125-kHz in a bulk conduction transistor-like device using direct photon-carrier excitation with below bandgap light.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Manipulating polymer composition to create low-cost, high-fidelity sensors for indoor CO2 monitoring

Abstract Carbon dioxide (CO 2 ) has been linked to many deleterious health effects, and it has also been used as a proxy for building occupancy measurements. These applications have created a need for low-cost and low-power CO 2 sensors that can be seamlessly incorporated into existing buildings. We report a resonant mass sensor coated with a solution-processable polymer blend of poly(ethylene oxide) (PEO) and poly(ethyleneimine) (PEI) for the detection of CO 2 across multiple use conditions. Controlling the polymer blend composition and nanostructure enabled better transport of the analyte gas into the sensing layer, which allowed for significantly enhanced CO 2 sensing relative to the state of the art. Moreover, the hydrophilic nature of PEO resulted in water uptake, which provided for higher sensing sensitivity at elevated humidity conditions. Therefore, this key integration of materials and resonant sensor platform could be a potential solution in the future for CO 2 monitoring in smart infrastructure.

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

Multiscale hierarchical and heterogeneous mechanical response of additively manufactured novel Al alloy investigated by high-resolution nanoindentation mapping

Smart alloying and microstructural engineering mitigate challenges associated with laser-powder bed fusion additive manufacturing (L-PBFAM). A novel Al–Ni–Ti–Zr alloy utilized grain refinement by heterogeneous nucleation and eutectic solidification to achieve superior performance-printability synergy. Conventional mechanical testing cannot delineate complex micromechanics of such alloys. This study combined multiscale nanomechanical and microstructural mapping to illustrate mechanical signatures associated with hierarchical heat distribution and rapid solidification of L-PBFAM. The disproportionate hardening effect imparted by Al 3 (Ti,Zr) precipitates in the pool boundaries and the semi-solid zone was successfully demonstrated. Nanomechanical response associated with heterogeneity in particle volume fraction and coherency across melt pool was interpreted from nanoindentation force–displacement curves. The hardness map effectively delineated the weakest and strongest sections in the pool with microscopic accuracy. The presented approach serves as a high throughput methodology to establish the chemistry-processing-microstructure-properties correlation of newly designed alloys for L-PBFAM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

All-atmospheric fabrication of Ag–Cu core–shell nanowire transparent electrodes with Haacke figure of merit >600 × 10–3 Ω−1

Abstract Transparent conducting electrodes (TCEs) are essential components in devices such as touch screens, smart windows, and photovoltaics. Metal nanowire networks are promising next-generation TCEs, but best-performing examples rely on expensive metal catalysts (palladium or platinum), vacuum processing, or transfer processes that cannot be scaled. This work demonstrates a metal nanowire TCE fabrication process that focuses on high performance and simple fabrication. Here we combined direct and plating metallization processes on electrospun nanowires. We first directly metallize silver nanowires using reactive silver ink. The silver catalyzes subsequent copper plating to produce Ag–Cu core–shell nanowires and eliminates nanowire junction resistances. The process allows for tunable transmission and sheet resistance properties by adjusting electrospinning and plating time. We demonstrate state-of-the-art, low-haze TCEs using an all-atmospheric process with sheet resistances of 0.33 Ω sq −1 and visible light transmittances of 86% (including the substrate), leading to a Haacke figure of merit of 652 × 10 –3 Ω −1 . The core–shell nanowire electrode also demonstrates high chemical and bending durability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2020

Uncovering phase transformation, morphological evolution, and nanoscale color heterogeneity in tungsten oxide electrochromic materials

Controlling the electrochemical interfacial processes that govern the durability of electrochromic devices represents a key challenge in developing sustainable and cost-effective smart windows. Here, we revisit the classical tungsten trioxide (WO 3 ) materials as a platform to uncover the previously unknown interrelationship between phase transformation, morphological evolution, and nanoscale color heterogeneity in these materials. Through synchrotron/electron spectroscopic and imaging analyses, we report that the WO 3 –electrolyte interface is influenced by the tungsten dissolution and redeposition during the electrochemical cycling. The tungsten redeposition provokes the in situ crystal growth, which ultimately leads to the phase transformation from the semicrystalline WO 3 to a nanoflake-shaped, proton-trapped tungsten trioxide dihydrate (H x WO 3 · 2 H 2 O). The multidimensional quantification of the electronic structure reveals that the tungsten reduction caused by the proton trapping is heterogeneous at the nanometric scale and is responsible for the nanoscale color heterogeneity. The interplay between phase transformation, morphological evolution, and nanoscale color heterogeneity results in degraded optical modulation, switching kinetics, coulombic efficiency, and bleached-state transparency. Our results highlight that the high interfacial reactivity near the electrode surface may be the underlying mechanism for undesired bulk structural changes of electrochromic materials, and calls for fundamental studies in probing and controlling the electrode–electrolyte interfacial processes in electrochromic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle-nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle x-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide ligands. Lastly, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

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

Local chemical fluctuation mediated ultra-sluggish martensitic transformation in high-entropy intermetallics

Superelasticity associated with martensitic transformation has found a broad range of engineering applications, such as in low-temperature devices in the aerospace industry. Nevertheless, the narrow working temperature range and strong temperature sensitivity of the first-order phase transformation significantly hinder the usage of smart metallic components in many critical areas. Here, we scrutinized the phase transformation behavior and mechanical properties of multicomponent B2-structured intermetallic compounds. Strikingly, the (TiZrHfCuNi) 83.3 Co 16.7 high-entropy intermetallics (HEIs) show superelasticity with high critical stress over 500 MPa, high fracture strength of over 2700 MPa, and small temperature sensitivity in a wide range of temperatures over 220 K. The complex sublattice occupation in these HEIs facilitates formation of nano-scaled local chemical fluctuation and then elastic confinement, which leads to an ultra-sluggish martensitic transformation. The thermal activation of the martensitic transformation was fully suppressed while the stress activation is severely retarded with an enhanced threshold stress over a wide temperature range. Moreover, the high configurational entropy also results in a small entropy change during phase transformation, consequently giving rise to the low temperature sensitivity of the superelasticity stress. Furthermore, our findings may provide a new paradigm for the development of advanced superelastic alloys, and shed new insights into understanding of martensitic transformation in general.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Morphodynamics of dendrite growth in alumina based all solid-state sodium metal batteries

All solid-state batteries (ASSBs) with ceramic electrolytes and alkali metal anodes are a potential future energy storage technology for vehicle electrification and smart grids. However, uncontrollable dendrite growth toward ultimate short circuiting in solid electrolytes (SEs) has become a serious concern in the design of long-cycle, safe ASSBs, and the underlying mechanism has remained unclear. Here through multiscale imaging and morphodynamic tracking we show that Na dendrites grow in β''-Al 2 O 3 SEs through an alternating sequence of Na deposition and crack propagation. Atomic-scale imaging evidenced that electrochemical cycling causes massive delamination cracking along the Na + conduction planes, accompanied by the closure of neighboring conduction channels. In situ SEM observations revealed a dynamic interplay between Na deposition and crack propagation: Na deposition accumulates mechanical stress that induces cracking; cracking releases the local stress, which promotes further Na deposition. Thus, Na deposition and cracking alternatingly proceed until short circuits take place. A multiscale phase-field model is developed to recapitulate the morphodynamics of Na dendrite growth, predicting the tree-like fractal morphology of the growing dendrites. Importantly, our findings suggest that decoupling between Na deposition and cracking represents an important route to mitigate uncontrollable dendrite growth in ASSBs.

25 ENERGY STORAGE↗