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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗
NASA NTRS2006

Sensor Management for Applied Research Technologies (SMART)-On Demand Modeling (ODM) Project

NASA requires timely on-demand data and analysis capabilities to enable practical benefits of Earth science observations. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep learning curve associated with each sensor and data type. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output. A three year project, entitled Sensor Management for Applied Research Technologies (SMART) - On Demand Modeling (ODM), will develop and demonstrate the readiness of Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities that integrate both Earth observations and forecast model output into new data acquisition and assimilation strategies. The advancement of SWE-enabled systems (i.e., use of SensorML, sensor planning services - SPS, sensor observation services - SOS, sensor alert services - SAS and common observation model protocols) will have practical and efficient uses in the Earth science community for enhanced data set generation, real-time data assimilation with operational applications, and for autonomous sensor tasking for unique data collection.

Goodman, M.↗
NASA NTRS1989

Concept synthesis of an equipment manipulation and transportation system EMATS

The European Columbus Scenario is established. One of the Columbus Elements, the Man Tended Free Flyer will be designed for fully autonomous operation in order to provide the environment for micro gravity facilities. The Concept of an autonomous automation system which perform servicing of facilities and deals with related logistic tasks is discussed.

Depeuter, W.↗
DOE OSTI2022

Autonomous design of new chemical reactions using a variational autoencoder

Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.

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

Efficient and Selective Chemical Transformations in Highly Charged and Confined Nanodroplets

The acceleration of chemical reaction rates and the increased product selectivity in microdroplets compared to that in bulk solutions has become a topic of increasing interest that has been extensively characterized by electrospray ionization mass spectrometry (ESI-MS). However, the sources of this acceleration and the detailed relationships between droplet properties and resulting reaction rate acceleration are still under debate. Moreover, droplet properties are governed by multiple interrelated experimental parameters, i.e., electrospray voltage, solution flow rate, etc., which makes it difficult and time-consuming to explore this diverse parameter space using traditional manual experimental or computational approaches. In this work, we developed an automated experimental platform integrating reactions in controlled charged microdroplet environments with ESI-MS characterization and sequential hybrid Bayesian modeling, as well as an optimal experimental design framework, to achieve multidimensional parameter optimization for higher reaction turnover rates, based on a model reaction of tetraethylenepentamine (TEPA) with carbon dioxide. With the current platform, we have achieved automated scans with a range of electrospray voltages and solution flow rates, and determined and optimized parameter settings to achieve increased reaction turnovers. We have also linked this platform to the underlying properties of droplets via a hybrid model incorporating physics, high-level theoretical calculations, and machine learning (ML) approaches. The autonomous platform is broadly applicable to a range of chemical reactions relevant to DOE’s mission in chemical separations, catalysis, and materials synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2007

The Sensor Management for Applied Research Technologies (SMART) Project

NASA seeks on-demand data processing and analysis of Earth science observations to facilitate timely decision-making that can lead to the realization of the practical benefits of satellite instruments, airborne and surface remote sensing systems. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep "learning curve" associated with each sensor, data type, and associated products. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output.

Goodman, Michael↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Well-Tunable, 3D-printable, and Fast Autonomous Self-Healing Elastomers

Self-healing elastomers provide extended longevity of functional materials, due to their unique adaptability and durability. However, a major scientific challenge remains in developing materials with a rapid healing process combined with decent mechanical properties, that can be prepared by a relatively simple synthesis approach. Herein, we report a versatile design approach on self-healing elastomers by incorporating two different hydrogen bonding containing monomers, i.e., 2-[[(butylamino)carbonyl]oxy]ethyl acrylate (BCOE) and 2-ureido-4[1H]pyrimidinone (UPy) functionalized ethyl methacrylate. Poly(BCOE-r-UPy)s are synthesized by reversible addition-fragmentation chain-transfer (RAFT) polymerization, and controlling the ratio of two monomers enables well-tunable mechanical properties with tensile strength ranging from 0.04 to 6.3 MPa and tensile strain up to 3,000 %. The characteristic dissociation energy is calculated from a temperature dependence of terminal relaxation followed by subtracting the segmental relaxation. The rapid autonomous self-healing is achieved when the molar composition of Poly(BCOE-r-UPy) is tailored to BCOE/UPy = 99/1. The self-healing process is monitored in situ by a helium-ion microscope, and its macroscopic study using tensile tests indicates that Poly(BCOE-r-UPy1) with 1 % molar ratio of UPy recovers 70 % of its original toughness at ambient temperature within 10 mins. 3D printing of Poly(BCOE-r-UPy) affords a self-healable 3D structure, demonstrating the adaptability of Poly(BCOE-r-UPy) for on-demand fabrication. The simplicity of synthesis, well-tunable mechanical properties, unique self-healability, and 3D printing capability of Poly(BCOE-r-UPy)s indicate their potential for a range of applications.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Automation study for space station subsystems and mission ground support

An automation concept for the autonomous operation of space station subsystems, i.e., electric power, thermal control, and communications and tracking are discussed. To assure that functions essential for autonomous operations are not neglected, an operations function (systems monitoring and control) is included in the discussion. It is recommended that automated speech recognition and synthesis be considered a basic mode of man/machine interaction for space station command and control, and that the data management system (DMS) and other systems on the space station be designed to accommodate fully automated fault detection, isolation, and recovery within the system monitoring function of the DMS.

Source record↗
NASA NTRS2001

The Future of Instrument Technology for Space-based Remote Sensing

The current paradigm for space-based remote sensing relies upon frequency-specific measurements in pre-defined orbits with fixed or narrowly variable apertures. The suites of Earth observing instruments currently deployed or planned for deployment within the next 3 years typify this paradigm. To enable NASA's Earth Science vision, the future paradigm of remote sensing must employ frequency-agile measurements capable of multi-scene observations. Real-time, autonomous adaptive sensing and taskability will be critical. Advanced capabilities will include: (1) Miniaturized observatories. (2) Robust, compact instrument architectures. (3) Miniaturized/programmable components. (4) Aperture synthesis. (5) Deployable apertures. (6) Low cost production.

Peri, Frank↗
DOE OSTI2025

Ligand-induced self-assembly of twisted two-dimensional halide perovskites

Two-dimensional (2D) halide perovskites (HPs) exhibit intriguing optoelectronic functionalities. Conventionally, 2D HPs have been synthesized with linear and planar molecular spacers, resulting in nominal modifications of their optoelectronic properties. In contrast, lower-dimensional HPs (0D and 1D) have proved accommodating to the incorporation of bulky molecular spacers. Fundamental insights into the incorporation of bulky molecular spacers in 2D HP structures remains elusive. Here, in this study, by implementing a high-throughput autonomous exploration workflow, the crystallization behaviours of 2D HPs based on a bulky 3,3-diphenylpropylammonium (DPA) spacer are comprehensively explored. Counterintuitive to conventional HP chemistry, synthesis of 2D DPA 2 PbI 4 HPs is indeed feasible when the steric hindrance is mediated by minute incorporation of 3D HP precursors. Furthermore, a moiré superlattice is observed from the DPA 2 PbI 4 flakes, indicating the spontaneous formation of twisted stacks of 2D HPs.We hypothesize that the unconventional van der Waals surface of DPA 2 PbI 4 facilitates the self-assembly of the twisted stacks of 2D HPs. This work exemplifies how high-throughput experimentation can discover unconventional material systems in which the synthetic principle lies beyond conventional chemical intuition. Furthermore, these findings provide hints for how to chemically manipulate the twist stacking in 2D HPs, thus rendering a straightforward way for bespoke realization of functionalities in exotic materials systems via a bottom-up approach.

materials chemistry↗
DOE OSTI2022

On the frontiers of coupled extreme environments

Coupled extreme environments pose among the highest demands on materials, stressing the limits of temperature, pressure, electric and magnetic fields, atomic displacement rates, and chemical potentials that can be simultaneously sustained. Here, this issue highlights exciting new materials science methods that will enable rapid progress in what has traditionally been the most difficult materials arena. New opportunities in artificial intelligence, multi-physics simulations capabilities, the use of extreme conditions to synthesize novel materials, and our ability to interrogate materials under relevant conditions provide new avenues to understand and design new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Engineering PdAu/CeO 2 Alloy/Oxide Interfaces for Selective Methane‐to‐Methanol Conversion with Water

The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO 2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd 0.3 Au 0.7 /CeO 2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methane-to-water ratio. While Pd/CeO 2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd with Au on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO 2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO 2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1978

Controlled cellular energy conversion in brown adipose tissue thermogenesis

Brown adipose tissue serves as a model system for nonshivering thermogenesis (NST) since a) it has as a primary physiological function the conversion of chemical energy to heat; and b) preliminary data from other tissues involved in NST (e.g., muscle) indicate that parallel mechanisms may be involved. Now that biochemical pathways have been proposed for brown fat thermogenesis, cellular models consistent with a thermodynamic representation can be formulated. Stated concisely, the thermogenic mechanism in a brown fat cell can be considered as an energy converter involving a sequence of cellular events controlled by signals over the autonomic nervous system. A thermodynamic description for NST is developed in terms of a nonisothermal system under steady-state conditions using network thermodynamics. Pathways simulated include mitochondrial ATP synthesis, a Na+/K+ membrane pump, and ionic diffusion through the adipocyte membrane.

Horowitz, J. M.↗
NASA NTRS2002

A New Look at NASA: Strategic Research In Information Technology

This viewgraph presentation provides information on research undertaken by NASA to facilitate the development of information technologies. Specific ideas covered here include: 1) Bio/nano technologies: biomolecular and nanoscale systems and tools for assembly and computing; 2) Evolvable hardware: autonomous self-improving, self-repairing hardware and software for survivable space systems in extreme environments; 3) High Confidence Software Technologies: formal methods, high-assurance software design, and program synthesis; 4) Intelligent Controls and Diagnostics: Next generation machine learning, adaptive control, and health management technologies; 5) Revolutionary computing: New computational models to increase capability and robustness to enable future NASA space missions.

Alfano, David↗
NASA NTRS2005

Bio-Nanobattery Development and Characterization

A bio-nanobattery is an electrical energy storage device that utilizes organic materials and processes on an atomic, or nanometer-scale. The bio-nanobattery under development at NASA s Langley Research Center provides new capabilities for electrical power generation, storage, and distribution as compared to conventional power storage systems. Most currently available electronic systems and devices rely on a single, centralized power source to supply electrical power to a specified location in the circuit. As electronic devices and associated components continue to shrink in size towards the nanometer-scale, a single centralized power source becomes impractical. Small systems, such as these, will require distributed power elements to reduce Joule heating, to minimize wiring quantities, and to allow autonomous operation of the various functions performed by the circuit. Our research involves the development and characterization of a bio-nanobattery using ferritins reconstituted with both an iron core (Fe-ferritin) and a cobalt core (Co-ferritin). Synthesis and characterization of the Co-ferritin and Fe-ferritin electrodes were performed, including reducing capability and the half-cell electrical potentials. Electrical output of nearly 0.5 V for the battery cell was measured. Ferritin utilizing other metallic cores were also considered to increase the overall electrical output. Two dimensional ferritin arrays were produced on various substrates to demonstrate the feasibility of a thin-film nano-scaled power storage system for distributed power storage applications. The bio-nanobattery will be ideal for nanometerscaled electronic applications, due to the small size, high energy density, and flexible thin-film structure. A five-cell demonstration article was produced for concept verification and bio-nanobattery characterization. Challenges to be addressed include the development of a multi-layered thin-film, increasing the energy density, dry-cell bionanobattery development, and selection of ferritin core materials to allow the broadest range of applications. The potential applications for the distributed power system include autonomously-operating intelligent chips, flexible thin-film electronic circuits, nanoelectromechanical systems (NEMS), ultra-high density data storage devices, nanoelectromagnetics, quantum electronic devices, biochips, nanorobots for medical applications and mechanical nano-fabrication, etc.

King, Glen C.↗
DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗