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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Framework development for a SAVY-4000 nuclear material storage container structural integrity surveillance tool

Here, this work presents the preliminary design of an automated surveillance tool to assess the health of SAVY-4000 nuclear material storage containers. This tool is designed by training several machine learning (ML) regression models to predict maximum residual stress in plain dents on the container sidewall. The model is trained on an experimentally validated Finite Element Analysis (FEA) model built in Abaqus FEA. The accuracy of each ML model is compared. The potential for application as well as model shortcomings are assessed. Necessary FEA model improvements are outlined and the various ML models are proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A novel peridynamics-based approach to predict pharmaceutical tablet robustness

The pharmaceutical drug product development process can be greatly accelerated through the use of modeling and simulation techniques to predict the manufacturability and performance of a given formulation. The anticipation and possible mitigation of tablet damage due to manufacturing stresses represents a specific area of interest in the pharmaceutical industry for predicting formulation and tableting performance. While the finite element method (FEM) has been extensively used for predicting the mechanical behavior of powder material in the compaction processes, a shortcoming of the approach is the inherent difficulty to predict discontinuities (e.g., damage or cracking) within a tablet as FEM is a continuum-based approach. In this work, we propose a novel method utilizing peridynamics (PD), a numerical method that can capture discontinuities such as tablet fracture, to predict the evolution of damage and breakage in pharmaceutical tablets. The approach links (1) the finite element method – to elucidate the behavior of powders during die compaction – with (2) the peridynamics modeling technique – to model the discontinuous nature of damage and predict tablet breakage during the critical stages of unloading and ejection from the compression die. This short communication presents a proof of concept including a workflow to calibrate the linked FEM-PD simulation models. Further, it demonstrates promising results from a preliminary experimental validation of the approach. Following further development, this approach could be used to guide the optimization of compression processes through targeted changes to formulation material properties, compression process conditions, and/or tooling geometries to deliver improved process efficiency and tablet robustness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accounting for Sustainability and Performance: Contrasting Effects of PET-Derived Modifiers on Asphalt Durability

The valorization of waste plastics in roadway infrastructure requires careful balance between sustainability and performance, as ineffective modifiers may incur both environmental and financial costs. This study shows that two modifiers derived from the same waste plastic source (PET)-DOTP (N1,N4-(dioctyl)terephthalamide) and HETP (N1,N4-Bis(2-hydroxyethyl)terephthalamide)-exhibit opposite effects on asphalt properties, despite having nearly the same carbon footprint. DOTP improves elastic recovery in multiple creep recovery tests (MCRT), while HETP increases strain accumulation and reduces elasticity. Density functional theory (DFT) shows that DOTP's molecular structure can disperse well in bitumen mixtures by binding to both polar and non-polar components. In contrast, HETP binds only to the binder's polar sites, lacking adequate dispersion. This increases the risk of aggregation of polar compounds in the bitumen mixture, resulting in a more deformation-prone binder. A techno-economic analysis highlights trade-offs between sustainability and performance in industrial-scale synthesis. This study advocates for applying first-principles thinking in material design, coupled with experimental validation, to engineer plastic waste-derived additives that achieve both performance and sustainability goals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Conserving asphalt resources: Rethinking rejuvenator performance evaluation through peptizing efficiency

Timely rejuvenation and restoration of asphalt are essential conservation practices that help preserve and extend the service life of roads, bridges, driveways, and parking lots. The performance of asphalt rejuvenators is often assessed based on their diffusion rates and softening power, yet these metrics alone fail to capture true rejuvenation potential. Here, this study integrates density functional theory (DFT) modeling with experimental analysis to show that rejuvenation effectiveness is primarily governed by molecular interactions with oxidized asphaltene nanoaggregates. DFT results revealed that amide- and unsaturated-chain compounds, such as hexadecanamide, oleic acid, and 9,17-octadecadienal, act cooperatively to disrupt π–π stacking and exfoliate asphaltene layers, reducing binding strength and enhancing dispersion. Unlike bulky, rigid molecules that remain trapped, these components remain mobile and repeatedly interact with multiple aggregation sites. Experimental validation using rheometry and FTIR confirmed that such molecularly compatible rejuvenators restore the binder flexibility and polydispersity, even when diffusion is relatively slow. Building on this mechanistic foundation, six rejuvenators (A2, A5, A7, A8, A9, and A10) were evaluated using a three-metric performance framework encompassing cracking resistance (Glover–Rowe parameter), UV stability, and surface hydrophobicity. Among the six rejuvenators evaluated, A9 exhibited the highest overall performance, reducing the Glover–Rowe cracking parameter by 85% (from 351 kPa to 53 kPa), demonstrating the greatest resistance to UV-induced aging with a stability index of 4.17 h·kPa⁻¹, and increasing surface hydrophobicity to a contact angle of 103.6°. Its superior performance is primarily attributed to its amide- and unsaturated-chain components, which act cooperatively to disrupt π–π stacking interactions and exfoliate asphaltene layers, thereby promoting molecular deagglomeration and enhancing long-term durability. These results shift the criteria for selecting rejuvenators: effective candidates must pair the electronic capability to unlock aged asphaltenes with sufficient structural stability to resist secondary aging and restore hydrophobicity. Restoring hydrophobicity is critical, as aging reduces the asphalt’s water repellency and increases water diffusion, which in turn accelerates moisture-related damage; consequently, a high-performing rejuvenator must effectively restore the binder’s hydrophobic characteristic. Collectively, these findings provide a framework for the rational design of next-generation of bio-based rejuvenators that enhance pavement longevity and promote long-term sustainability.

Aging↗
DOE OSTI2025

Comments on “Failure analysis of corroded hydrogen-blended natural gas pipelines based on finite element analysis and genetic algorithm-back propagation neural network” [262 (2025) 111174]

This is a brief commentary paper to highlight and discuss the determination of hydrogen concentration in pipeline steel, effect of hydrogen embrittlement (HE) on the mechanical properties of the material, burst strength of corroded pipelines using finite element analysis (FEA) simulations, and curve-fit models for assessing remaining strength of X80 corroded pipelines for transporting hydrogen blended natural gas. Recently, Xie et al. [1] proposed a methodology to quantify the impact of HE on material properties and numerically determined burst pressure of X80 corroded pipelines. However, their HE quantification overestimated the degradation of tensile strength for hydrogen blending ratios beyond the original data range, and their FEA results of burst pressure are nonconservative. This work thus recharacterized the hydrogen concentration in the steel pipeline and the effect of HE on tensile strength, and then redetermined burst pressures for a set of typical corrosion defect cases considered by Xie et al. [1] based on an experimentally validated FEA modelling method. With the new FEA results, two empirical corrosion models were proposed for X80 corroded pipelines for hydrogen service. At zero hydrogen blending ratio, the novel empirical models predict burst pressures to be consistent with the industry-accepted corrosion models. Furthermore, both the numerical simulation method and the novel corrosion models are significant contributions to the pipeline industry and the hydrogen community. Application of these results will enhance the safety, reliability, and integrity of natural gas pipelines when used to transport hydrogen.

Burst pressure prediction↗
DOE OSTI2026

Thermomechanical analysis and modeling of a high-temperature light trapping planar cavity solar receiver

The development of durable particle-based high-temperature solar receivers is critical for advancing concentrating solar-thermal (CST) technologies to enable high-efficiency power generation and industrial process heat. Here, this study presents a computational framework to evaluate the thermomechanical performance of a proposed enclosed light-trapping planar cavity receiver designed for particle-based thermal energy systems. The receiver incorporates absorptive cavities and fluidized particle-bed channels to enhance heat capture and reduce thermal losses. Finite element analysis (FEA) is employed to assess stress, strain, and creep-fatigue behavior under concentrated solar flux using realistic thermal boundary conditions derived from coupled system models and experimental assembly parameters. The analysis investigates the influence of particle-to-wall heat transfer coefficients (HTC) ranging from 800 to 1800 W/m 2 .K on the thermomechanical response of six candidate high-temperature alloys: Alloy 740H, Alloy 282, Alloy 617, 316H, Alloy 230, and 800H. Results show that increasing HTC reduces thermal gradients, leading to lower stresses and strains and extended minimum predicted creep life. While all materials satisfy fatigue life requirements under the investigated conditions, significant differences in creep resistance are observed. Alloy 740H consistently exhibits the longest minimum predicted creep life and the most favorable durability margins, followed by Alloy 282, with the remaining materials showing reduced creep resistance under identical loading. The reported creep lives are conservative lower-bound estimates intended for comparative material evaluation. This framework highlights the critical roles of material selection and geometry optimization in improving mechanical durability and reliability of solar-thermal receivers, forming a foundation for future experimental validation and design optimization.

14 SOLAR ENERGY↗
DOE OSTI2025

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning

Specific structural motifs in inorganic solids are often related to their targeted physical properties. For many classes of solids, such as Zintl phases and polar intermetallics, the crystal structures are diverse and not easy to predict. Various antimonides that are potential thermoelectric materials were proposed to be synthesizable on the basis of their estimated formation energies. Their structures were broadly classified as clathrate, channel, layered, or network through a machine learning model trained on existing ternary phases and features based on elemental properties using the sure independence screening and sparsifying operator algorithm. Through experimental validation, three new ternary antimonides were synthesized and confirmed to form layered structures: tetragonal RbAlSb 2 and CsAlSb 2 , which are isopointal but not isotypic to LiBSi 2 ; and monoclinic Rb 2 Al 2 Sb 3 , which adopts the Na 2 Al 2 Sb 3 -type structure. Finally, reinvestigation of the related compound Cs 2 In 2 Sb 3 revealed a low thermal conductivity and p-type semiconducting behavior.

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

Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Here, the migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

14 SOLAR ENERGY↗
DOE OSTI2025

Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning

Here, we present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. In conclusion, through this computational material design study, a previously unexplored mechanism in PL formation, solvent–solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

Carbon capture↗
DOE OSTI2025

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.

Chalcogenides↗
DOE OSTI2026

Tuning the Functionalities of Porous Liquids for Emergent Gas-Capture Properties

Type 3 Porous Liquids (PLs) are a class of materials with the potential to revolutionize gas capture, storage, and utilization. These PLs are formed by suspending sorbent nanoparticles (e.g., metal−organic frameworks) in sterically excluded solvents, creating permanent porosity for gas capture in a processable, low-viscosity phase. Herein, a computational study revealed sorbent surface functionalization strategies to enhance CO 2 sorption, and the molecular structural signatures underpinning the enhancements in gas uptake. PLs composed of a ZIF-8 surface functionalized with 3-amino-1,2,4-triazole (Atz) in glyceryl triacetate were targeted for emergent CO 2 capture, exceeding that of the unfunctionalized ZIF-8 PL. ZIF-8 was surface functionalized with Atz at various surface coverage fractions (f), and classical molecular dynamics simulations predicted an increase in CO 2 sorption capacity with increasing f, up to f = 0.75. Additionally, detailed structural analyses revealed that solvent orientational order, derived from the solvent triplet-angle distribution, can identify the gas-capture potential of a PL without requiring computationally expensive direct modeling of the CO 2 sorption. Combined with experimental validation, initial computational screening of PL compositions promises to accelerate the discovery of PL compositions for novel gas separation materials platforms.

gas capture↗
DOE OSTI2026

Shining a Light on Some Fundamental Research Opportunities in Semiconductor Photoelectrochemistry

Decades of research in semiconductor photoelectrochemistry have yielded a deep understanding of charge transfer, energetics, and stability at solid−liquid interfaces. Theoretical frameworks developed by Gerischer and contemporaries, together with extensive experimental validation, have clarified the key principles affecting the interfacial kinetics and energetics of semiconductor photoelectrodes. Nevertheless, significant opportunities remain for advances in fundamental understanding of semiconductor photoelectrochemistry. Exciting opportunities include exploiting advances in theory, synthesis, and instrumentation to determine the chemical identity and reactivity of surface states; exerting control of band-edge energetics through molecular-level surface modification processes; and systematically improving emerging photoelectrode protection strategies to enable long-term photoelectrode operation under both oxidative and reductive conditions. Advanced morphologies, such as nanowire and microwire arrays, present new pathways to combine efficient light absorption with effective charge collection and catalyst integration. Unique light−matter interactions during photoelectrochemical deposition of p-type semiconductors readily allow preparation at scale of complex 3D morphologies that are difficult, if not impossible, to access by other methods. Continued exploration of these avenues will expand the fundamental understanding of semiconductor−liquid interfaces and could additionally advance the realization of efficient, stable, and scalable systems for solar fuel generation and other emerging photoelectrochemical applications.

Bean, Paul J. L. [California Institute of Technolo↗
DOE OSTI2026

Lithium–Sulfur Batteries Enabled by Fluorine-Free Electrolytes with a Compressed Solvation Structure

In this paper, a fluorine-free aromatic cosolvent strategy is presented to regulate electrolyte solvation chemistry in Li-S batteries with sulfurized polyacrylonitrile (SPAN) cathodes. Assisted by the Uni-ELF AI tool and experimental validation, toluene is identified as an optimal weakly solvating cosolvent. Its incorporation compresses the Li⁺ solvation sheath and induces an anion-dominated solvation structure, thereby enhancing interfacial ion transport and sulfur redox kinetics through controlled π-π interactions with polysulfides. Consequently, Li || Li symmetric cells exhibit stable cycling for over 1,000 cycles at a current density of 1 mA cm⁻². Meanwhile, Li-S cells employing high-loading SPAN cathodes retain more than 75% of their initial capacity after 250 cycles at -10 °C. Additionally, a practical pouch cell with high SPAN loading and a low electrolyte-to-SPAN ratio of 3 µL mg⁻¹ delivers an initial capacity of around 600 mAh gSPAN⁻¹, underscoring the potential of fluorine-free electrolytes for practical metal-sulfur batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Accelerated Discovery of Cost-Effective Photoabsorber Materials for Near-Infrared (λ = 1600 nm) Photodetector Applications

Current infrared sensing devices are based on costly materials with relatively few viable alternatives known. To identify promising candidate materials for infrared photodetection, we have developed a high-throughput screening methodology based on high-accuracy r 2 SCAN and HSE calculations in density functional theory. Using this method, we identify ten already synthesized materials between the inverse perovskite family, the barium silver pnictide family, the alkaline pnictide family, and ZnSnAs 2 as top candidates. Among these, ZnSnAs 2 emerges as the most promising candidate due to its experimentally verified band gap of 0.74 eV at 0 K and its cost-effective synthesis through Bridgman growth. BaAgP also shows potential with an HSE-calculated band gap of 0.64 eV, although further experimental validation is required. Lastly, we discover an additional material, Ca 3 BiP, which has not been previously synthesized, but exhibits a promising optical spectra and a band gap of 0.56 eV. The method applied in this work is sufficiently general to screen wider bandgap materials in high-throughput and now extended to narrow-band gap materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Slow-Light Mid-IR Silicon Photonic Chips for NO 2 and CH 4 Gas Detection

A compact, chip-scale mid-infrared gas sensor is demonstrated, leveraging a two-dimensional photonic crystal waveguide (PCW) fabricated on a silicon-on-insulator (SOI) platform. The PCW comprises a hexagonal lattice with lattice constant a = 860 nm and hole radius r = 0.22a, incorporating a central line defect of reduced-radius holes (r s = 0.7r) to induce slow-light propagation near the photonic band edge with a group index of approximately 73, thereby enhancing light-matter interaction. The sensor operates at fundamental absorption wavelengths of 3.42 μm for nitrogen dioxide (NO 2 ) and 3.40 μm for methane (CH 4 ), utilizing the strongest molecular vibrational transitions for maximum sensitivity. Experimental validation was conducted using dynamically diluted gas mixtures generated by mass flow controllers, with signal acquisition performed by a liquid nitrogen-cooled InSb detector. For NO 2 , the sensor exhibited excellent linear response over 5–25 ppm (part per million) with coefficient of determination R 2 = 0.9934, achieving a detection limit of 210 ppb (part per billion)─representing the first reported silicon photonic-based NO 2 detection. For CH 4 , exposure to 25 ppm resulted in a 6.4% decrease in transmitted intensity, demonstrating multigas sensing capability. The CMOS-compatible fabrication process and compact 3 mm device footprint establish this SOI-PCW platform as a scalable, low-power solution for integrated mid-infrared gas sensing, with significant potential for environmental monitoring and industrial safety applications.

Crystals↗
DOE OSTI2025

Mechanistic Investigation of the Ce(III) Chloride Photoredox Catalysis System: Understanding the Role of Alcohols as Additives

Photocatalytic C–H activation is an emerging area of research. While cerium chloride photocatalysts have been extensively studied, the role of alcohol additives in these systems remains a subject of ongoing discussion. It was demonstrated that the photocatalyst [NEt 4 ] 2 [Ce IV Cl 6 ] ( 1 ) produces •Cl and added alcohols exhibit zero-order kinetics. Prior studies by other researchers suggested that 1 and alcohols lead to cerium alkoxide [Ce–OR] and alkoxy radical intermediates. Here, to understand these seemingly divergent mechanistic proposals, an expanded investigation comparing cerium(IV) catalyst 1 and cerium(III) complex [NEt 4 ] 3 [Ce III Cl 6 ] ( 2 ), which exhibit markedly different reactivity and C–H selectivity, is disclosed. Our findings reveal that alcohol additives accelerate the conversion of cerium(III) to cerium(IV) catalysts, forming key intermediates such as [NEt 4 ] 2 [Ce III Cl 5 (HOCH 3 )] ( 5 ) and [NEt 4 ] 2 [Ce IV Cl 5 (OCH 3 )] ( 6 ), driven by excited-state di-tert-butyl azodicarboxylate under blue light irradiation. The active complex 6 releases the •OCH 3 radical, in sharp contrast to •Cl radicals initiated by cerium(IV) photoredox catalyst 1 . These different reactivity and selectivity profiles can be understood in the context of complex 5 generation and in situ formation of base to afford complex 6 . Experimental validation shows enhanced selectivity toward C–H bonds with different reactivity with catalyst 1 and methanol upon the addition of base and decreased selectivity with catalyst 2 and methanol upon the addition of acid. These findings unify the previously contrasting observations of cerium halide/alkoxide photocatalytic systems and provide a comprehensive understanding on the essential role of base/acid and alcohol in selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗