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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Scalable and compact magnetocaloric heat pump technology

Magnetocaloric heat pumping (MCHP) promises to be more efficient than traditional vapor compression while also eliminating the deleterious effects of gaseous refrigerants. While MCHP devices have shown the temperature spans and efficiencies needed for different heating and cooling applications, they struggle to become commercially viable due to their large size and mass, and resultant high cost. This paper evaluates a baseline MCHP device and explores methods to boost its system power density (SPD). The key components of the baseline system are the gadolinium packed-particle bed active magnetic regenerator (AMR) and a magnetic source composed of permanent magnets and high permeability magnetic steel. To enhance the SPD, the paper evaluates maximizing the AMR volume, opting for first-order magnetocaloric materials, optimizing the magnet and AMR geometry, and reducing the size of magnets and magnetic steel parts. At larger thermal powers, increasing the AMR diameter and the number of magnetic poles were evaluated. Using finite element models, solid models, and estimates of magnetocaloric material performance, thermal powers ranging from 37 W to 44 kW at a nominal 10 K temperature span were projected, and SPD was estimated to improve from 6 W/kg to 81 W/kg. Neglecting end effects, an upper limit of 114 W/g is estimated. Compared to SPD of off-the-shelf compressors with similar environment temperatures, MCHP power density using gadolinium is competitive up to roughly 200 W of cooling power. This is extended to 1 kW when using LaFeSi alloys and up to 3 kW in the limiting case. In conclusion, these results indicate that the performance and mass of MCHP can match that of compressors, which is a critical step toward cost-competitive magnetocaloric technology.

42 ENGINEERING↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2026

Modulating Cu electrode microenvironments with MOF coatings: insights from molecular dynamics and electrochemical experiments of CO reduction

Metal-organic frameworks (MOFs) present a compelling strategy for tuning electrochemical interfaces by reshaping interfacial solvent structure. In this study, we examine how MOF coatings influence the microenvironment at copper electrodes during the CO electroreduction reaction (CORR) using a combined approach of molecular dynamics (MD) simulations and electrochemical experiments. Two MOFs, NU-901 and ZIF-8, are selected to explore the impact of pore size and channel hydrophobicity on electrochemical activity and interfacial concentration in acetonitrile (ACN) and dimethyl sulfoxide (DMSO) electrolytes. Electrochemical measurements reveal that MOF@Cu electrodes exhibit lower Faradaic efficiencies for CO hydrogenation products (ethylene and methane) compared to bare copper but have dramatic impacts on the interfacial microenvironment. NU-901, with its larger pores and strong interactions with DMSO, traps DMSO molecules and enhances CO coordination in DMSO but suppresses CORR selectivity in favor of the hydrogen evolution reaction (HER). ZIF-8, with smaller pores and hydrophobic channels, limits the interfacial water concentration, and, in ACN, promotes CO coordination. The simulations provide insights into how MOFs can act as physical modulators of reactant delivery and interfacial structure to control electrochemical microenvironments. This work highlights the value of molecular dynamics in uncovering how structural features of MOFs influence interfacial phenomena, even when catalytic performance is not directly improved.

Copper electrode microenvironments↗
DOE OSTI2025

A Perspective on Multiscale Modeling of Explicit Solvation-Enabled Simulations of Catalysis at Liquid–Solid Interfaces

Catalysis at liquid-solid interfaces is profoundly influenced by the interfacial solvent structure, which affects catalytic activity, selectivity, and reaction pathways. This perspective discusses state-of-the-art multiscale modeling methods that integrate quantum mechanics and molecular mechanics approaches to apply explicit solvent molecules to capture these interfacial phenomena. Specifically, the construction of multiscale models, the importance of capturing the interfacial solvent structure, and the computational strategies used to achieve this are explored, and the challenges in balancing chemical accuracy with computational expense are highlighted. Additionally, this perspective addresses the limitations of current methods. Opportunities for integrating machine learning are proposed. Here, by advancing the efficiency and user friendliness of multiscale modeling, it is argued that deeper insights into heterogeneous catalysis in liquid phases can be provided, which will ultimately contribute to the development of more efficient catalytic processes.

Ab initio molecular dynamics↗
DOE OSTI2025

Laser wavelength dependence of laser imprint

In laser direct-drive inertial confinement fusion, laser imprint is one of the major causes of degradation in target performance through its seeding of hydrodynamic instabilities. Early experiments and simulations have shown that laser imprint could be mitigated with a longer laser wavelength because of its lower critical density and longer conduction zone. Building upon this work, we explore a scenario where the laser wavelength during the picket pulse differs from that of the main pulse in order to gain the benefit of reduced imprint, while avoiding losses in drive coupling efficiency or an increase in laser-plasma instabilities. A series of 2D radiation-hydrodynamic simulations, which test three different laser wavelengths for the picket pulse, has been performed, where the intensity of the picket pulse is adjusted in order to maintain the same implosion adiabat. A detailed analysis of the growth of the mass density modulations at the ablation front over a large range of mode numbers confirms that the laser imprint can be mitigated with a picket pulse operating at a longer wavelength than the main pulse because of the longer conduction zone and enhanced thermal smoothing. The amplitude of the ablation front modulations is found to be lower for all mode numbers, which reduces the seeding of the Rayleigh–Taylor instability without affecting the mode growth rates.

Fourier analysis↗
DOE OSTI2025

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.

Li, Chaojian [ORNL] (ORCID:0000000340309777)↗
DOE OSTI2025

NeuroCoreX: Brain-Inspired Computing from Code to Circuit

NeuroCoreX is an open-source codebase that enables the implementation of brain-inspired, energy-efficient neuromorphic computing models on FPGA hardware. Designed to support real-time learning, all-to-all neural connectivity, and flexible network architectures, NeuroCoreX offers a hands-on, accessible platform for exploring biologically inspired models of neural computation. It empowers researchers, students, and developers to implement and experiment with adaptive systems—bringing the power of neuromorphic computing to a broader community through a low-cost, scalable, and reconfigurable framework.

Gautam, Ashish [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Advance Distribution Management System (ADMS)

This presentation explores the transition from traditional distribution management systems to Advanced Distribution Management Systems (ADMS) as a foundation for smart grid development. It highlights the key benefits of ADMS, including enhanced reliability, improved operational efficiency, and increased situational awareness. The presentation also addresses common implementation challenges such as system integration, data management, and organizational readiness. It concludes with a forward-looking perspective on the evolving role of ADMS and essential takeaways for utilities and stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Double Perovskite Interlayer Stabilized Highly Efficient Perovskite Solar Cells

Metal halide perovskite solar cell (PSC) technology has an impressive power conversion efficiency (PCE) exceeding 26.1% and demonstrates cost-effective manufacturing. However, the stability of these PSCs poses a significant challenge, hindering their widespread manufacturing and commercialization. To tackle the degradation issue inherent in PSCs, surface passivation techniques, particularly employing a thin layer of two-dimensional (2D) perovskites, create a 2D/3D heterostructure. Beyond this, the exploration of metal halide double perovskites adds a new dimension to the chemical and band gap phase space of materials for optoelectronic applications. In this study, we leverage a wide band gap double perovskite interlayer to enhance the stability of 3D metal halide perovskite. Specifically, the double perovskite nanoparticle Cs 2 AgBiBr 6 , with its substantial band gap of 2.2 eV and exceptional air stability, is utilized. Through optimization, a Cs 2 AgBiBr 6 -treated PSC achieves an open-circuit voltage of 1.12 V and an impressive PCE of 19.52%. Additionally, the Cs 2 AgBiBr 6 passivation layer proves to be effective in bolstering the stability of PSCs. This work demonstrates an additional strategy and design motif to simultaneously increase the PCE of PSCs along with achieving improved stability.

14 SOLAR ENERGY↗
DOE OSTI2026

Are Deep Energy Retrofits in Commercial Buildings Including Window Upgrades?

U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.

Cort, Katherine↗
DOE OSTI2025

A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions

Abstract Metabolism operates under physico-chemical constraints that result in multireaction dependencies. Understanding how multireaction dependencies affect metabolic phenotypes remains challenging, hindering their biotechnological applications. Here, we propose the concept of a forcedly balanced complex that allows to efficiently determine the effects of specific multireaction dependencies on metabolic network functions in constrained-based models. Using this concept, we found that the fraction of multireaction dependencies induced by forcedly balanced complexes in genome-scale metabolic networks followed power law with exponential cut-off. We identified forcedly balanced complexes that are lethal in cancer but have little effect on growth in healthy tissue models. In addition, these forcedly balanced complexes are largely specific to models of particular cancer types. Therefore, multireaction dependencies resulting from forced balancing of complexes represent an innovative means to control cancers that, we argue, can be implemented via transporter engineering. The presented constraint-based approaches pave the way for using multireaction dependencies in metabolic engineering for diverse biotechnological applications.

Küken, Anika↗
DOE OSTI2024

Exploring the Feasibility and Performance of Perovskite/Antimony Selenide Four-Terminal Tandem Solar Cells

The tandem solar cell presents a potential solution to surpass the Shockley–Queisser limit observed in single-junction solar cells. However, creating a tandem device that is both cost-effective and highly efficient poses a significant challenge. In this study, we present proof of concept for a four-terminal (4T) tandem solar cell utilizing a wide bandgap (1.6–1.8 eV) perovskite top cell and a narrow bandgap (1.2 eV) antimony selenide (Sb 2 Se 3 ) bottom cell. Using a one-dimensional (1D) solar cell capacitance simulator (SCAPS), our calculations indicate the feasibility of this architecture, projecting a simulated device performance of 23% for the perovskite/Sb 2 Se 3 4T tandem device. To validate this, we fabricated two wide bandgap semitransparent perovskite cells with bandgaps of 1.6 eV and 1.77 eV, respectively. These were then mechanically stacked with a narrow bandgap antimony selenide (1.2 eV) to create a tandem structure, resulting in experimental efficiencies exceeding 15%. The obtained results demonstrate promising device performance, showcasing the potential of combining perovskite top cells with the emerging, earth-abundant antimony selenide thin film solar technology to enhance overall device efficiency.

14 SOLAR ENERGY↗
DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Manipulating symmetry-breaking charge separation employing molecular recognition

The exploration of symmetry-breaking charge separation (SB-CS) is imperative when designing functional light-harvesting materials. Past explorations, however, have been confined to covalent systems, more often than not requiring complicated/demanding syntheses and facing inconvenient regulation of charge transfer processes. Here, in this work, we present a concept that regulates the efficiency of SB-CS through molecular recognition utilizing a pyridinium-based cyclophane as a host. This host undergoes photo-driven excited-state SB-CS. By employing different guests with distinct frontier molecular orbital energy levels, we have achieved comprehensive control of electron transfer pathways in the cyclophane, modulating between accelerated (>10-fold) intramolecular SB-CS involving superexchange and direct intermolecular electron transfer between the host and guest. The improvement in SB-CS efficiency results in catalytic activity for the photo-oxidation of a sulfur-mustard simulant. This research offers an opportunity for tuning SB-CS by utilizing molecular recognition, which holds the potential for achieving precise regulation without complicated organic syntheses.

charge transfer↗
DOE OSTI2026

Probing multi-dimensional composition spaces in search of strong metallic alloys

Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.

Materials science↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2025

Entropy is an important design principle in the photosystem II supercomplex

Photosystem II (PSII) can achieve near-unity quantum efficiency of light harvesting in ideal conditions and can dissipate excess light energy as heat to prevent the formation of reactive oxygen species (ROS) under light stress. Understanding how this pigment–protein complex accomplishes these opposing goals is a topic of great interest that has so far been explored primarily through the lens of the system energetics. Despite PSII’s known flat energy landscape, a thorough consideration of the entropic effects on energy transfer in PSII is lacking. In this work, we aim to discern the free energetic design principles underlying the PSII energy transfer network. To accomplish this goal, we employ a structure-based rate matrix and compute the free energy terms in time following a specific initial excitation to discern how entropy and enthalpy drive ensemble system dynamics. We find that the interplay between the entropy and enthalpy components differ among each protein subunit, which allows each subunit to fulfill a unique role in the energy transfer network. This individuality ensures that PSII can accomplish efficient energy trapping in the reaction center (RC), effective nonphotochemical quenching (NPQ) in the periphery, and robust energy trapping in the other-monomer RC if the same-monomer RC is closed. We also show that entropy, in particular, is a dynamically tunable feature of the PSII free energy landscape accomplished through regulation of LHCII binding. These findings help rationalize natural photosynthesis and provide design principles for more efficient solar energy harvesting technologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Colloidal Behavior of Plutonium Oxide in Concentrated Electrolyte Solutions

The Hanford Site in Washington State manages legacy high-level radioactive waste streams that display major chemistry and engineering challenges, including the high salt levels and pH values that correspond to conditions under which many classical concepts describing chemical reactivity cannot be applied. One particular challenge that needs to be tackled at Hanford is that Pu concentrations, [Pu], in the soluble phases of the tank wastes are higher than expected based on the solubility of crystalline PuO2, which is widely accepted to be caused by the formation of PuO2 colloid, consisting of nano- to submicron-sized particles (PuO2 NPs). Fundamental research underpinning the behavior of PuO2 NPs under conditions not only relevant to the Hanford tank waste but at high ionic strength in general is needed to reliably predict the chemical reactivity of PuO2 NPs and develop engineering solutions to safely and efficiently process high-level radioactive waste into forms suitable for long-term storage. In this work, we study the behavior of PuO2 NPs (particle size ~100 nm) under high ionic strength conditions by reacting it with highly concentrated (up to 5 M) salt solutions. We explore different electrolyte compositions to elucidate the impact of different anions (NO3-, Cl-, ClO4-, SO42-, C2O42-, CO32-) on the stability of PuO2 NP in the acidic and alkaline pH regime. PuO2 NP aggregation and precipitation as function electrolyte concentration is tracked by a combination of liquid scintillation counting, dynamic light scattering for determination of particle size distributions, and zeta potentials as a proxy for particle charge. At acidic pH, electrolytes containing non-coordinating anions, such as NaNO3, NaCl, and NaClO4 mostly stabilize PuO2 NPs over a large electrolyte concentration range, showing only subtle differences in their reactivity. Other electrolyte anions show a more pronounced effect on the PuO2 NP stability: SO42-, binds directly to the particles’ surface, reverses the particle charge, and precipitates the PuO2 NPs efficiently even at intermediate sulfate concentrations (>0.1 M). In contrast, C2O42- is found to lead to high [Pu] in solution, in the milli-molar range, even at mildly acidic pH (~4). Thermodynamic modeling of the dissolved Pu concentrations using PHREEQC is unable to predict the observed [Pu] in the acidic pH regime, supporting the influence of colloids in maintaining elevated [Pu]. It is noteworthy that the current thermodynamic databases do not include constants for colloidal Pu phases and cannot accurately predict many of the high ionic strength solutions relevant to this work. The mechanisms and models responsible for these observations will need further investigation in the future. At high pH values (~12), PuO2 NPs exhibits classical sol-gel chemistry, meaning that upon destabilization of the colloidal sol, for example by addition of concentrated NaOH, highly porous and viscid PuO2 coagulates are formed that consist of a three-dimensional network likely held together by physical interactions. The PuO2 NP coagulate shows no significant reversibility of the aggregation when contacted with concentrated brines; however, PuO2 NPs can be efficiently resuspended in solution by addition of diluted electrolytes, alkaline solutions containing high amounts of carbonate, or simple addition of water. Especially carbonate is shown to stabilize PuO2 NPs in solution at high pH, characterized by stable colloidal suspensions that are resistant against sedimentation during centrifugation. Thermodynamic modeling of the carbonate system was able to predict an increasing dissolved Pu concentration with increasing carbonate concentration. However, the model was profoundly sensitive to the fixed redox potential and does not include any thermodynamic constants for colloidal Pu species.

Neumann, Julia↗