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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The global energy footprint of information and communication technology electronics in connected Internet-of-Things devices

Information and communication technology (ICT) has been critical to the emergence of a new Internet-of-Things (IoT) era, as a rapidly increasing number and variety of IoT electronic devices in a wide range of end-use sectors require massive data communication in terms of volume, velocity/speed, and variety. Additionally, semiconductor technology advancements have played a key role in ICT and unlike the past ICT energy footprint analysis, which focused on the final electronic product, an attempt has been made to examine the global energy impacts for the period 2016–2025 based on the market forecasts of three major IoT IC types, i.e., sensors and actuators, connectivity, and processor ICs distributed among four major IoT device components.Total global IoT semiconductor primary energy demand is projected to increase from 2 EJ in 2016 to 35 EJ by 2025, resulting mainly from a substantial projected increase energy needed in the manufacturing of ICs, with growth in the use of sensors and energy-intensive next fab generation manufacturing. Unlike the trend in manufacturing energy, total global operational energy use is projected to decrease significantly with the development of smaller transistor size, low-power devices, and faster wireless data communication technology. Total operational energy is projected to significantly decrease from 118 TWh/year (1,2 EJ/year) to less than 1 TWh/year (0.003 EJ/year) in spite of a significantly higher anticipated growth in its market size during the 2016–2025 forecast period. Its annual share of total primary energy use is projected to reach an insignificant level of 0.01% by the end of 2025, consistent with the past limited electronic product life cycle analyses besides the analysis focus here limited to ICs in IoT devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗
DOE OSTI2026

Mechanism of Vapor-Phase Infiltration of Organometallic Hf in Poly(Methyl Methacrylate) for Hybrid Resist Applications

Inorganic–organic hybrid thin films synthesized by vapor-phase infiltration (VPI) of metal oxides into organic photoresists, such as poly(methyl methacrylate) (PMMA), have recently demonstrated their utility in extreme ultraviolet lithography, critical for angstrom-era semiconductor device miniaturization. Hafnium oxide infiltration has been reported recently for this purpose, but its detailed VPI mechanism has remained largely unexplored. In this study, we investigated the VPI characteristics and mechanisms of tetrakis(dimethylamido)hafnium (TDMAHf)─the hafnium precursor predominantly used for VPI in the field─into PMMA and examined its impact on electron-beam lithography (EBL) exposure behavior. VPI was performed at temperatures ranging from 85 to 150 °C, with chemical interactions characterized using infrared reflection-absorption spectroscopy, and resist patterning performance was evaluated through EBL dose-sensitivity assessments. The results indicate that TDMAHf forms a reversible adduct with PMMA at temperatures up to 120 °C, whereas at 150 °C, covalent bond formation occurs, most likely via dealkylation that leads to acetate formation. EBL studies reveal that resist sensitivity is influenced by both infiltration temperature and developer selection, with aqueous isopropyl alcohol development demonstrating enhanced sensitivity compared to organic solvent-based development. The optimized infiltration protocol at 120 °C ensures a uniform inorganic distribution without compromising resist dissolution. These findings not only help refine hybrid resist patterning performance but also offer insights potentially applicable to the VPI of other homoleptic metal-amide organometallic VPI precursors that include TDMA ligands.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cryo-electron Microscopy of Adeno-associated Virus

Adeno-associated virus (AAV) has a single-stranded DNA genome encapsidated in a small icosahedrally symmetric protein shell with 60 subunits. AAV is the leading delivery vector in emerging gene therapy treatments for inherited disorders, so its structure and molecular interactions with human hosts are of intense interest. A wide array of electron microscopic approaches have been used to visualize the virus and its complexes, depending on the scientific question, technology available, and amenability of the sample. Approaches range from subvolume tomographic analyses of complexes with large and flexible host proteins to detailed analysis of atomic interactions within the virus and with small ligands at resolutions as high as 1.6 Å. Analyses have led to the reclassification of glycan receptors as attachment factors, to structures with a new-found receptor protein, to identification of the epitopes of antibodies, and a new understanding of possible neutralization mechanisms. AAV is now well-enough characterized that it has also become a model system for EM methods development. Heralding a new era, cryo-EM is now also being deployed as an analytic tool in the process development and production quality control of high value pharmaceutical biologics, namely AAV vectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature’s own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and “smart” architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Furthermore, continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

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

Deciphering Photoinduced Catalytic Reaction Mechanisms in Natural and Artificial Photosynthetic Systems on Multiple Temporal and Spatial Scales Using X-ray Probes

Utilization of renewable energies for catalytically generating value-added chemicals is highly desirable in this era of rising energy demands and climate change impacts. Artificial photosynthetic systems or photocatalysts utilize light to convert abundant CO 2 , H 2 O, and O 2 to fuels, such as carbohydrates and hydrogen, thus converting light energy to storable chemical resources. Here, the emergence of intense X-ray pulses from synchrotrons, ultrafast X-ray pulses from X-ray free electron lasers, and table-top laser-driven sources over the past decades opens new frontiers in deciphering photoinduced catalytic reaction mechanisms on the multiple temporal and spatial scales. Operando X-ray spectroscopic methods offer a new set of electronic transitions in probing the oxidation states, coordinating geometry, and spin states of the metal catalytic center and photosensitizers with unprecedented energy and time resolution. Operando X-ray scattering methods enable previously elusive reaction steps to be characterized on different length scales and time scales. The methodological progress and their application examples collected in this review will offer a glimpse into the accomplishments and current state in deciphering reaction mechanisms for both natural and synthetic systems. Looking forward, there are still many challenges and opportunities at the frontier of catalytic research that will require further advancement of the characterization techniques.

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

Localized Conduction Channels in Memristors

Since the early 2000s, the impending end of Moore’s scaling, as the physical limits to shrinking transistors have been approached, has fueled interest in improving the functionality and efficiency of integrated circuits by employing memristors or two-terminal resistive switches. Formation (or avoidance) of localized conducting channels in many memristors, often called “filaments”, has been established as the basis for their operation. While we understand some qualitative aspects of the physical and thermodynamic origins of conduction localization, there are not yet quantitative models that allow us to predict when they will form or how large they will be. Here, in this work, we compile observations and explanations of channel formation that have appeared in the literature since the 1930s, show how many of these seemingly unrelated pieces fit together, and outline what is needed to complete the puzzle. This understanding will be a necessary predictive component for the design and fabrication of post-Moore’s-era electronics.

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

Direct Air Capture Using Aqueous Amino Acid Solvents in a Crossflow Absorber

Carbon dioxide (CO 2 ) is the most abundant of all greenhouse gases (GHGs). CO 2 levels in the atmosphere are 50% higher than in the preindustrial era, trapping heat. CO 2 removal from the atmosphere by direct air capture (DAC) is needed to achieve the internationally agreed global temperature goals. The most common CO 2 capture technology is absorption by aminebased solvents in packed columns. Amino acid solutions have recently gained attention due to their advantages over traditional amine solvents. To be implemented effectively, DAC industrial processes need to handle large airflow rates in separation absorbers. The large air and solvent flow rates preclude the use of countercurrent columns due to high-pressure drops and the occurrence of flooding. Crossflow air−liquid absorbers are used to handle large air and liquid volumes due to their lower-pressure drop. The objective of this work is to study the influence of crossflow absorber geometric parameters and operating conditions on product formation and process efficiency. An already derived theoretical model for countercurrent absorbers has been modified to simulate the operation of a crossflow DAC absorber. The predictive model was implemented into a computer code that was used to study the efficiency of the processes as geometric equipment dimensions and operating parameters vary. Practical suggestions are made to design more efficient DAC processes.

Absorption↗
DOE OSTI2022

Quantum Simulation of Molecular Electronic States with a Transcorrelated Hamiltonian: Higher Accuracy with Fewer Qubits

Simulation of electronic structure is one of the most promising applications on noisy intermediate-scale quantum (NISQ) era devices. However, NISQ devices suffer from a number of challenges like limited qubit connectivity, short coherence times, and sizable gate error rates. Thus, desired quantum algorithms should require shallow circuit depths and low qubit counts to take advantage of these devices. Here, we attempt to reduce quantum resource requirements for molecular simulations on a quantum computer while maintaining the desired accuracy with the help of classical quantum chemical theories of canonical transformation and explicit correlation. In this work, compact ab initio Hamiltonians are generated classically, in the second quantized form, through an approximate similarity transformation of the Hamiltonian with (a) an explicitly correlated two-body unitary operator with generalized pair excitations that remove the Coulombic electron–electron singularities from the Hamiltonian and (b) a unitary one-body operator to efficiently capture the orbital relaxation effects required for accurate description of the excited states. The resulting transcorrelated Hamiltonians are able to describe both the ground and the excited states of molecular systems in a balanced manner. Using the variational quantum eigensolver (VQE) method based on the unitary coupled cluster with singles and doubles (UCCSD) ansatz and only a minimal basis set (ANO-RCC-MB), we demonstrate that the transcorrelated Hamiltonians can produce ground state energies comparable to the reference CCSD energies with the much larger cc-pVTZ basis set. This leads to a reduction in the number of required CNOT gates by more than 3 orders of magnitude for the chemical species studied in this work. Furthermore, using the quantum equation of motion (qEOM) formalism in conjunction with the transcorrelated Hamiltonian, we are able to reduce the deviations in the excitation energies from the reference EOM-CCSD/cc-pVTZ values by an order of magnitude. In conclusion, the transcorrelated Hamiltonians developed here are Hermitian and contain only one- and two-body interaction terms and thus can be easily combined with any quantum algorithm for accurate electronic structure simulations.

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

Signature of Many-Body Localization of Phonons in Strongly Disordered Superlattices

Many-body localization (MBL) has attracted significant research attention due to its immunity to thermalization, role in logarithmic entanglement entropy growth, and opportunities to reach exotic quantum orders. However, experimental realization of MBL in solid-state systems has remained challenging. Here we report evidence of a possible phonon MBL phase in disordered GaAs/AlAs superlattices. Through grazing-incidence inelastic X-ray scattering, we observe a strong deviation of the phonon population from equilibrium in disordered samples doped with ErAs nanodots at low temperature, signaling a departure from thermalization. This behavior occurs within finite phonon energy and wavevector windows, suggesting a localization-thermalization crossover. We support our observation by proposing a theoretical model for the effective phonon Hamiltonian in disordered superlattices, and showing that it can be mapped exactly to a disordered 1D Bose-Hubbard model with a known MBL phase. The large on-site repulsion qualitatively explains the experimental findings. Furthermore, our work provides mode-resolved experimental evidence of phonon localization, potentially extending the scope of MBL to disordered solid-state systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Complementary Effects of Mg and Cu Incorporation in Stabilizing the Cobalt-Free LiNiO 2 Cathode for Lithium-Ion Batteries

Since the discovery of LiNiO 2 several decades ago, a new era of electric vehicles demanding high-energy-density lithium-ion batteries (LIBs) has recently rebooted the interest in this cathode material to eliminate the dependence on expensive and scarcely available cobalt. However, LiNiO 2 has been plagued by cycle instability, thermal instability, and air instability. We present here an exploration of the mutual interaction of magnesium and copper in stabilizing the cobalt-free LiNiO 2 cathode. Although Mg doping is beneficial for the robustness of the bulk structure of LiNiO 2 , surface characterization results of Mg-doped LiNiO 2 implies the need for further surface protection. To that end, we have incorporated Cu in addition to Mg in that Cu stabilizes the surface of Mg-doped LiNiO 2 by forming a protective stable surface layer without harming the bulk. Notable variations of the surface residual lithium composition (Li Li 2 CO 3 /Li LiOH ) along with the incorporation of stabilizers are also discussed. The harmony between Mg and Cu with as little as 0.5 atom % Mg and 0.3 atom % Cu significantly enhances the specific energy and cycle life of LiNiO 2 . Furthermore, this study demonstrates how the co-incorporation of optimal dopants can help stabilize both the bulk and surface and provides new insights toward developing cobalt-free layered oxide cathodes for high-energy-density LIBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Trailblazing Kr/Xe Separation: The Birth of the First Kr-Selective Material

Efficient separation of Kr from Kr/Xe mixtures is pivotal in nuclear waste management and dark matter research. Thus far, scientists have encountered a formidable challenge: the absence of a material with the ability to selectively adsorb Kr over Xe at room temperature. This study presents a groundbreaking transformation of the renowned metal–organic framework (MOF) CuBTC, previously acknowledged for its Xe adsorption affinity, into an unparalleled Kr-selective adsorbent. This achievement stems from an innovative densification approach involving systematic compression of the MOF, where the crystal size, interparticle interaction, defects, and evacuation conditions are synergistically modulated. The resultant densified CuBTC phase exhibits exceptional mechanical resilience, radiation tolerance, and notably an unprecedented selectivity for Kr over Xe at room temperature. Simulation and experimental kinetic diffusion studies confirm reduced gas diffusion in the densified MOF, attributed to its small pore window and minimal interparticle voids. The lighter Kr element demonstrates facile surface passage and higher diffusivity within the material, while the heavier Xe encounters increased difficulty entering the material and lower diffusivity. This Kr-selective MOF not only represents a significant breakthrough in Kr separation but also demonstrates remarkable processability and scalability to kilogram levels. Furthermore, the findings presented herein underscore the transformative potential of engineered MOFs in addressing complex challenges, heralding a new era of Kr separation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of Electro-Sprayed Porous Electrodes on the Performance and Stability of Water Electrolysis

The efficiency of proton exchange membrane water electrolysis (PEMWE) is a critical issue in realizing the production of green hydrogen. Here, the coexistence of three phases in the catalyst layer of PEMWE causes the mass transport limitation at the interfaces between them. In particular, the vigorous production of gaseous hydrogen and oxygen derived from liquid water is generated in the form of bubbles that seriously deactivate the membrane-electrode assembly (MEA). In this study, we investigated the effect of porous structure in the electrode on the efficiency of hydrogen production at high current density, which is highly related to the mass transport limitation. A widely used commercial catalyst (IrO 2 ) were directly coated on the membrane by the electro-spray method. The porous electrodes on the membrane were formed by the charged catalyst particles that repulsed each other due to the electrostatic forces of the particles. Our membrane electrode assembly (MEA) exhibited outstanding electrolysis performances such as 5.3 A cm -2 and 3.2 A cm -2 at 2.0 V and 1.8 V, respectively, which are the highest values compared with the results published in the current studies. In addition to the porosity, it was confirmed that optimum binder contents positively affect the hydrophobicity and contact resistance of MEA. Through a simple porosity-controlled technique, the performance of PEMWE, in which three phases coexist, can be improved by more than 60 %. Accordingly, we expect that our systematic study on the role of porosity in the electrodes opens a new era to efficiently produce green hydrogen.

catalysts↗
DOE OSTI2025

Effect of Networking Density on the Patterning Performance of Molecular Layer Deposited Alucone Electron Beam/EUV Resists

One of the main barriers to continued device scaling in the era of extreme ultraviolet (EUV) lithography is the need for improved photoresist chemistries to address challenges such as poor EUV sensitivity, inadequate etch resistance, and pattern collapse. Metal-organic photoresists are a promising class of materials that can address many of these challenges, and among them, resists deposited via hybrid molecular layer deposition (MLD) have attracted interest for their unique advantages in thickness control, chemical homogeneity, and compatibility with vacuum processing. However, despite many successful demonstrations of patterning, little is known about how the molecular design of hybrid MLD resists affects their lithographic performance. In this work, we study the effect of the network structure, a common feature among all hybrid MLD resists, via a series of aluminum alkoxide ("alucone") negative tone resists with varying networking density. Their patterning mechanism is investigated via electron beam lithography (EBL)─a common proxy for EUV─and compared to their EUV-induced reactions studied via flood exposure and in situ characterization. In conclusion, we show that the resist with the least networking density demonstrates the best sensitivity and resolution, with the ability to resolve dense line/space gratings as small as 14 nm half pitch via EBL.

electron beam lithography↗
DOE OSTI2020

Successes and Opportunities for Discovery of Metal Oxide Photoanodes for Solar Fuels Generators

We report that the importance of metal oxide photoanodes in solar fuels technology has garnered concerted efforts in photoanode discovery in recent decades, which complement parallel efforts in development of analytical techniques and optimization strategies using standard photoanodes such as TiO 2 , Fe 2 O 3 and BiVO 4 . Theoretical guidance of high-throughput experiments has been particularly effective in dramatically increasing the portfolio of metal oxide photoanodes, motivating a new era of photoanode development where the characterization and optimization techniques developed on traditional materials are applied to nascent photoanodes that exhibit visible light photoresponse. The compendium of metal oxide photoanodes presented in the present work can also serve as the basis for further technique development, with a primary goal to establish workflows for discovery of materials that perform better against the critical criteria of operational stability, visible light photoresponse, and photovoltage suitable for tandem absorber architectures.

14 SOLAR ENERGY↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2024

Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics

The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.

36 MATERIALS SCIENCE↗