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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 991 records · Page 55

Battery Storage for Resilience

As the capital costs of battery storage systems are decreasing, new opportunities to cost-effectively deploy the technology, often paired with renewable energy technologies, are emerging. At the same time, the duration and frequency of natural disasters is increasing. As a result, a growing number of institutions are deploying battery storage systems as a resilient energy solution because traditional back-up power solutions, like diesel generators, are not always sufficient, especially during longer-duration and larger-scale disasters. This fact sheet describes how battery storage, along with additional generation sources, can be used both to provide cost savings while grid-connected and to provide backup power when the grid goes down.

25 ENERGY STORAGE↗

CFD Modeling of Concentration Polarization in High-Performance Flat-Sheet Modules

Membrane-based gas separation is a promising, regeneration-free route for industrial CO₂ capture, but its performance is often limited by concentration polarization (CP). Classical 1-D models neglect CP and thus overpredict fluxes, especially for today’s high-permeance, high-selectivity membranes. Therefore, we built a 3-D CFD model that resolves local species transport, boundary-layer resistance, permeate-side effects, and pressure drop in flat-sheet modules. Using the Concentration Polarization Coefficient (CPC) and the overall Effective Membrane CP Index (EAC), the model quantifies how geometry and operating conditions shape CP intensity. Simulations show CP becomes pronounced for advanced membranes, with feed-channel height emerging as the dominant geometric lever: shrinking the gap mitigates CP but raises pressure drop, forcing a trade-off between mass-transfer enhancement and energy cost. These insights emphasize the need for CFD-level analysis in next-generation module design, where 1-D treatments can severely underestimate required membrane area and misguide scale-up.

carbon capture↗

State-of-the-Art Organic- and Inorganic-Based Hollow Fiber Membranes in Liquid and Gas Applications: Looking Back and Beyond

The aggravation of environmental problems such as water scarcity and air pollution has called upon the need for a sustainable solution globally. Membrane technology, owing to its simplicity, sustainability, and cost-effectiveness, has emerged as one of the favorable technologies for water and air purification. Among all of the membrane configurations, hollow fiber membranes hold promise due to their outstanding packing density and ease of module assembly. Herein, this review systematically outlines the fundamentals of hollow fiber membranes, which comprise the structural analyses and phase inversion mechanism. Furthermore, illustrations of the latest advances in the fabrication of organic, inorganic, and composite hollow fiber membranes are presented. Key findings on the utilization of hollow fiber membranes in microfiltration (MF), nanofiltration (NF), reverse osmosis (RO), forward osmosis (FO), pervaporation, gas and vapor separation, membrane distillation, and membrane contactor are also reported. Moreover, the applications in nuclear waste treatment and biomedical fields such as hemodialysis and drug delivery are emphasized. Subsequently, the emerging R&D areas, precisely on green fabrication and modification techniques as well as sustainable materials for hollow fiber membranes, are highlighted. Last but not least, this review offers invigorating perspectives on the future directions for the design of next-generation hollow fiber membranes for various applications. As such, the comprehensive and critical insights gained in this review are anticipated to provide a new research doorway to stimulate the future development and optimization of hollow fiber membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Search for High-Pressure Silicon Phases: Reaching the Extreme Conditions with High-Intensity Laser Irradiation

Controlling electronic and optical properties of semiconducting materials could substantially expand their functionality and enable new pathways for the formation of ‘smart’ materials for emerging innovative applications. An emerging new path for the synthesis of exotic silicon (Si) polymorphs is irradiation of bulk diamond-cubic Si by ultrashort laser pulses at relativistic intensity above 1019 W/cm2. Exotic polymorphs are formed due to interaction with MeV-energy electrons generated in laser-produced high-temperature plasma state. The unusual Si structures are found in the form of 5–10 nm nanoparticles confined deep in the bulk of 500-μm-thick Si samples. Raman spectroscopy, electron microscopy and X-ray diffraction studies provide an unequivocal evidence of exotic Si phase formation. A key advantage of such relativistic irradiation lies thereby in the fact that large quantities of such exotic Si is synthesised embedded within the bulk diamond-cubic Si and that further the formed Si polymorphs are stable at ambient temperature and pressure. They are thus available for further studies for electronic applications in selective band gap engineering.

Rapp, Ludovic↗

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗

Rapid Generation of Hierarchically Porous Metal–Organic Frameworks through Laser Photolysis

Hierarchically porous metal–organic frameworks (HP-MOFs) facilitate mass transfer due to mesoporosity while preserving the advantage of microporosity. This unique feature endows HP-MOFs with remarkable application potential in multiple fields. Recently, new methods such as linker labilization for the construction of HP-MOFs have emerged. To further enrich the synthetic toolkit of MOFs, we report a controlled photolytic removal of linkers to create mesopores within microporous MOFs at tens of milliseconds. Ultraviolet (UV) laser has been applied to eliminate “photolabile” linkers without affecting the overall crystallinity and integrity of the original framework. Presumably, the creation of mesopores can be attributed to the missing-cluster defects, which can be tuned through varying the time of laser exposure and ratio of photolabile/robust linkers. Upon laser exposure, MOF crystals shrank while metal oxide nanoparticles formed giving rise to the HP-MOFs. In addition, photolysis can also be utilized for the fabrication of complicated patterns with high precision, paving the way towards MOF lithography, which has enormous potential in sensing and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Phenome‐to‐genome insights for evaluating root system architecture in field studies of maize

Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.

Genetics & Heredity↗

Physics of the Inverted Harmonic Oscillator: From the lowest Landau level to event horizons

In this work, we present the inverted harmonic oscillator (IHO) Hamiltonian as a paradigm to understand the quantum mechanics of scattering and time-decay in a diverse set of physical systems. As one of the generators of area preserving transformations, the IHO Hamiltonian can be studied as a dilatation generator, squeeze generator, a Lorentz boost generator, or a scattering potential. In establishing these different forms, we demonstrate the physics of the IHO that underlies phenomena as disparate as the Hawking–Unruh effect and scattering in the lowest Landau level (LLL) in quantum Hall systems. We derive the emergence of the IHO Hamiltonian in the LLL in a gauge invariant way and show its exact parallels with the Rindler Hamiltonian that describes quantum mechanics near event horizons. This approach of studying distinct physical systems with symmetries described by isomorphic Lie algebras through the emergent IHO Hamiltonian enables us to reinterpret geometric response in the lowest Landau level in terms of relativistic effects such as Wigner rotation. Further, the analytic scattering matrix of the IHO points to the existence of quasinormal modes (QNMs) in the spectrum, which have quantized time-decay rates. We present a way to access these QNMs through wave packet scattering, thus proposing a novel effect in quantum Hall point contact geometries that parallels those found in black holes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗

Maximizing Marine Carbon Removal by Coupling Electrochemical and Biological Methods

Integrated development of emerging marine decarbonization strategies offers the possibility of lowering CO2 removal costs and enabling their widespread deployment. In this study we examine the feasibility and benefits of coupling electrochemical and biological marine carbon removal strategies. Bipolar membrane electrodialysis (BPMED) is used to generate acid and alkalinity from seawater and electricity, and the alkalinity is returned to the ocean for indirect CO2 removal from the atmosphere, but the acid stream is a waste product. Considering the large-scale of CO2 removal necessary, the acid storage, neutralization, and disposal have prohibitive costs and carbon footprint. Here we investigate the feasibility to valorize the acid stream to enhance the growth and CO2 uptake through photosynthesis in the fast-growing marine phytoplankter Picochlorum celeri. When added to active algae cultures, the BPMED-generated acidified seawater alters the carbonate-bicarbonate equilibrium thereby increasing the bioavailability of CO2 and the observed growth rates. Additions of up to 2 mM H+ from BPMED effluent streams increased algal productivity up to 3-fold. A high-level analysis conducted based on experimental data to estimate the potential of sequestered CO2 emissions when compared to conventional commercial means of acid utilization or disposal, is estimated to be ~30 kgCO2 / kgacid. Through further development and optimization in terms of choice of algal species, growth conditions, acid addition rates, etc. the combined electrochemical-biological approach has the potential to achieve higher net CO2 removal.

carbon dioxide, marine, marine algae↗

Advances in Colloidal InP-Based Quantum Dots for Photocatalytic Hydrogen Evolution

Photocatalytic materials for hydrogen generation are typically categorized into UV-absorbing metal oxides and visible-range semiconductors such as chalcogenides or perovskites, which often incorporate toxic elements. To address environmental concerns of the latter group, indium phosphide (InP)-based quantum dots (QDs) have recently emerged as a less toxic alternative. These nanocrystals (NCs) benefit from a broadband and tunable absorption spectrum, which is well matched for solar photochemistry, and offer suitable electronic characteristics to drive photoinduced charge separation. This perspective provides a comprehensive summary of the recent advancements in developing InP-based NCs with a focus on photocatalytic hydrogen production. We discuss synthetic strategies that enhance the catalytic activity of these materials and highlight key challenges that must be addressed to enhance their performance. Finally, we explore future research directions aimed at improving photocatalytic efficiency and integrating InP-based QDs into practical solar-to-fuel conversion systems.

Hydrogen↗

Fourier Analyses of High-Order Continuous and Discontinuous Galerkin Methods

In this paper, we present a Fourier analysis of wave propagation problems subject to a class of continuous and discontinuous discretizations using high-degree Lagrange polynomials. This allows us to obtain explicit analytical formulas for the dispersion relation and group velocity and, for the first time to our knowledge, characterize analytically the emergence of gaps in the dispersion relation at specific wavenumbers, when they exist, and compute their specific locations. Wave packets with energy at these wavenumbers will fail to propagate correctly, leading to significant numerical dispersion. We also show that the Fourier analysis generates mathematical artifacts, and we explain how to remove them through a branch selection procedure conducted by analysis of eigenvectors and associated reconstructed solutions. The higher frequency eigenmodes, named erratic in this study, are also investigated analytically and numerically.

97 MATHEMATICS AND COMPUTING↗

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗

Convergence of Emerging Technologies - Blickfeld Test Results

The Blickfeld Cube 1 Lidar is an inexpensive flash lidar being developed for autonomous navigation with an advertised maximum range of 75 meters that uses a Class 1 eye-safe laser. Figure 1 shows an example of the installation of the Cube 1 lidar and Figure 2 shows an example of the point cloud generated, with the red circle indicating an intruder. The Cube 1 lidar has a software adjustable field of view and as many as 5 lidars can be stitched together. (Figure 2 shows two Cube 1 lidars stitched together.)

47 OTHER INSTRUMENTATION↗

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗

Sustainable Valorization of CO2 through Nuclear Power-to-X Pathways

Some of the issues concerning energy security and climate change can be addressed by employing nuclear power (NP) to supply the energy required for the conversion of carbon dioxide (CO2) into chemicals, products, and materials. Nuclear energy represents a neutral carbon source that can be generated sustainably, reliably, and consistently. Nuclear power plants (NPPs) could supply energy in the form of heat, electricity, and ionizing radiation to drive CO2 chemical reactions underpinning NP-to-X type of pathways. CO2 conversion processes are either commercially available or emerging technologies at different developmental maturity stages. This work reviews the published literature (articles and patents) that reports R&D results and the understanding and development of chemical reactions and processes, as well as the efforts in integrating NPPs and chemical processes (CPs). As will be made evident, a new industrial era for the manufacturing of decarbonized chemicals, products, and materials will be possible by developing and implementing new (more energy- and carbon-efficient) processes responding to the NP-to-X pathways. This new decarbonizing platform not only contributes to achieving net zero goals but also broadens the NPP product beyond electricity.

08 HYDROGEN↗

Deepfaked online content is highly effective in manipulating people’s attitudes and intentions

In recent times, disinformation has spread rapidly through social media and news sites, biasing our (moral) judgements of other people and groups. “Deepfakes”, a new type of AI-generated media, represent a powerful new tool for spreading disinformation online. Although Deepfaked images, videos, and audio may appear genuine, they are actually hyper-realistic fabrications that enable one to digitally control what another person says or does. Given the recent emergence of this technology, we set out to examine the psychological impact of Deepfaked online content on viewers. Across seven preregistered studies (N = 2558) we exposed participants to either genuine or Deepfaked content, and then measured its impact on their explicit (self-reported) and implicit (unintentional) attitudes as well as behavioral intentions. Results indicated that Deepfaked videos and audio have a strong psychological impact on the viewer, and are just as effective in biasing their attitudes and intentions as genuine content. Many people are unaware that Deepfaking is possible; find it difficult to detect when they are being exposed to it; and most importantly, neither awareness nor detection serves to protect people from its influence. All preregistrations, data and code available at osf.io/f6ajb.

Hughes, Sean↗

BETO 2021 Peer Review - Rational Design of Robust Reactor Feeding Systems for Heterogeneous Cellulosic and Agricultural Wastes Based on Biomass Quality Characteristics

Consistent and reliable preprocessing, conveyance, and reactor in-feed systems, particularly for low-cost waste feedstocks, remains a major technical challenge for the emerging Bioeconomy. By identifying critical biomass attributes and connecting them to flow and conversion behavior, science-driven system designs can address these often-overlooked solids handling challenges. The Wonderful Company (TWC) is the world's largest almond and pistachio grower, generating 250,000 dry tons/year of waste material including hulls, shells, and wood (>5 million tons/year industry wide in the U.S.). This project seeks to turn this environmental and economic liability into a sustainable and profitable resource, targeting conversion via gasification to syngas for electricity and bio-char, by addressing related material handling and feeding challenges. Based on optimized preprocessing strategies, bulk material flow, and thermal conversion properties, an overall system design will be developed. The methodology will be tested with FCIC's benchmark loblolly pine residues, to demonstrate the robustness of the overall approach and provide insight and guidance for future systems. The project will culminate in extended field trials to demonstrate an improved continuous feeding system with a commercial biomass-to-electricity gasifier vendor, and an economic analysis demonstrating a reduction in electricity production costs by maximizing on-stream time while minimizing preprocessing and CapEx costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗