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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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Four mechanofluorochromic (MFC) luminogenic materials were prepared and found to exhibit outstanding mechanically-induced emission enhancement (MIEE) by virtue of substituent and position effects. Such materials can be used in OLED devices.
This study explores the emerging development of electrochemical direct ocean capture (eDOC) as an effective negative emission technology; focusing on pH swing mechanisms, we highlight advancements in eDOC and identify key areas for future research.
Redox-electrodes are designed to selectively bind platinum group metals by auto-oxidation, and release them electrochemically. The platform can efficiently recover PGMs from catalytic converter leachates, and contribute to energy-efficient technologies for materials recycling.
Machine-learning molecular dynamics simulations pave the way to completely treat the anharmonicity of phonons. Low-energy anharmonic modes in transition-metal dichalcogenides drive the thermal and transport properties.
2H SnSe 2 and 1T VSe 2 van der Waals materials were synthesized and then subjected to redox exfoliation. Decoration of these 2D sheets with iron oxide nanoparticles produced a synergistic magnetization response that exceeded that of iron oxide alone.
The current paradigm of low-T combustion and autoignition of hydrocarbons is based on the sequential two-step oxygenation of fuel radicals. The key chain-branching occurs when the second oxygenation adduct (OOQOOH) is isomerized releasing an OH radical and a key ketohydroperoxide (KHP) intermediate. The subsequent homolytic dissociation of relatively weak O–O bonds in KHP generates two more radicals in the oxidation chain leading to ignition. Based on the recently introduced intramolecular “catalytic hydrogen atom transfer” mechanism (J. Phys. Chem. 2024, 128, 2169), abbreviated here as I-CHAT, we have identified a novel unimolecular decomposition channel for KHPs to form their classical isomers—enol hydroperoxides (EHP). The uncertainty in the contribution of enols is typically due to the high computed barriers for conventional (“direct”) keto–enol tautomerization. Remarkably, the I-CHAT dramatically reduces such barriers. The novel mechanism can be regarded as an intramolecular version of the intermolecular relay transfer of H-atoms mediated by an external molecule following the general classification of such processes (Catal. Rev.-Sci. Eng. 2014, 56, 403). Here, we present a detailed mechanistic and kinetic analysis of the I-CHAT-facilitated pathways applied to n-hexane, n-heptane, and n-pentane models as prototype molecules for gasoline, diesel, and hybrid rocket fuels. We particularly examined the formation kinetics and subsequent dissociation of the γ-enol-hydroperoxide isomer of the most abundant pentane-derived isomer γ-C5-KHP observed experimentally. To gain molecular-level insight into the I-CHAT catalysis, we have also explored the role of the internal catalyst moieties using truncated models. All applied models demonstrated a significant reduction in the isomerization barriers, primarily due to the decreased ring strain in transition states. In addition, the longer-range and sequential H-migration processes were also identified and illustrated via a combined double keto–enol conversion of heptane-2,6-diketo-4-hydroperoxide as a potential chain-branching model. To assess the possible impact of the I-CHAT channels on global fuel combustion characteristics, we performed a detailed kinetic analysis of the isomerization and decomposition of γ-C5-KHP comparing I-CHAT with key alternative reactions—direct dissociation and Korcek channels. Calculated rate parameters were implemented into a modified version of the n-pentane kinetic model developed earlier using RMG automated model generation tools (ACS Omega, 2023, 8, 4908). Simulations of ignition delay times revealed the significant effect of the new pathways, suggesting an important role of the I-CHAT pathways in the low-T combustion of large alkanes.
Understanding and predicting ion transport in aqueous electrolytes are crucial for advanced energy storage and biophysics, and many emergent technologies yet remain elusive. Herein, we introduce a unified framework to quantitatively describe and predict electrolyte conductivity that shifts from conventional molar concentration-based metrics to a volume fraction-based approach. Through analyzing a variety of electrolyte solutions via this perspective, we observe a universal conductivity peak at a 37% volume fraction. Small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations reveal that nanometer-scale ion clusters drive this general behavior. Moreover, key geometric features of the ion transport pathwayssuch as pore size, tortuosity, and connectivityfollow a consistent dependence with respect to the volume fraction, reinforcing the argument for the universal conductivity trend. This paradigm shift opens new avenues for designing high-performance electrolytes and provides transformative insights for advancing studies in many fields, wherein molecular aggregates dictate transport properties.
Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
Using ai-GCMC simulations,operandoWAXS, and kinetics analysis, we found that the high-rate performance of Sb as an alloy anode in Na-ion batteries is due to the presence of an amorphous intermediate phase formed during sodiation and desodiation.
Abstract Confined single metal atoms in graphene‐based materials have proven to be excellent catalysts for several reactions and promising gas sensing systems. However, whether the chemical activity arises from the specific type of metal atom or is a direct consequence of the confinement itself remains unclear.
This study presents a one-pot synthesis route to organometallic nanofibers based on copper thiolate, exhibiting distinctive chemical and physical characteristics. Electron microscopy analysis of morphology and composition revealed 2-10 μm-long, 50-90 nm-diameter hollow and non-hollow fibers composed of copper, sulfur, oxygen, hydrocarbon, and chlorine. Thermogravimetric analysis showed a pronounced mass loss within 120°C-135°C. To elucidate the thermal responsive pathways, the nanofibers were characterized before and after heating. X-ray photoelectron spectroscopy indicates that an initially mixed Cu(I)/Cu(II) oxidation states transition to predominantly Cu(I) upon heating. A layer of nanofiber was coated on battery pouch foil and evaluated as a candidate thermally sensitive coating. At elevated temperature (100-130°C), nanofiber coating released volatile organic compounds, sulfide and sulfur dioxide as detected using multiple gas sensors. This thermally responsive gas release/sensing approach provides a potential large-area temperature monitoring strategy, which is particularly relevant where direct temperature measurements of individual batteries is impractical. The results established proof of concept for nanofiber-coated battery pouch foil as overtemperature warning platform that can provide alerts when surface temperatures exceed a critical threshold. More broadly, the ability to form interconnected fiber networks positions copper thiolate nanofiber coatings as promising materials for advanced applications.
Low color, space environmentally durable polymeric films with sufficient electrical conductivity to mitigate electrostatic charge (ESC) build-up have potential applications on large, deployable, ultra-light weight Gossamer spacecraft as thin film membranes on antennas, solar sails, thermal/optical coatings, multi-layer insulation blankets, etc.. The challenge has been to develop a method to impart robust electrical conductivity into these materials without increasing solar absorptivity (alpha ) or decreasing optical transparency or film flexibility. Since these spacecraft will require significant compaction prior to launch, the film portion of the spacecraft will require folding. The state-of-the-art clear, conductive coating (e.g. indium-tin-oxide, ITO) is brittle and cannot tolerate folding. In this report, doping a polymer with single-walled carbon nanotubes (SWNTs) using two different methods afforded materials with good flexibility and surface conductivities in the range sufficient for ESC mitigation. A coating method afforded materials with minimal effects on the mechanical, optical, and thermo-optical properties as compared to dispersal of SWNTs in the matrix. The chemistry and physical properties of these nanocomposites are discussed.
Abstract The chemistry of electrons in actinide complexes and materials is still poorly understood and represents a serious challenge and opportunity for experiment and theory. The study of the electron density distribution of the ground state of such systems through X‐ray diffraction represents a unique opportunity to quantitatively investigate different chemical bonding interactions at once, but was considered “almost impossible” on heavy‐atom systems, until very recently. Here, we present a combined experimental and theoretical investigation of the electron density distribution in UCl_ 4 crystals and comparison with the previously reported spin density distribution from polarized neutron diffraction. All approaches provide a consistent picture in terms of electron and spin density distribution, and chemical bond characterization. More importantly, the synergy between experiments and quantum‐mechanical calculations allows to highlight the remarkable sensitivity of X‐ray diffraction to electrons in materials.
Abstract The operation of low‐temperature electrochemical energy conversion systems (fuel cells, electrolyzers) at high pH values is of perennial interest due to the possibility of moving away from expensive platinum group metal catalysts and reducing cost. Historically, the anion exchange membranes (AEMs) and ionomers have been hampered by performance (ionic conductivity, mechanical strength) and chemical stability issues. In this context, select developments over the past decade in alternate AEM chemistries, water management methods, and production of membrane electrode assemblies (MEAs) that have enabled a significant leap in performance of alkaline fuel cells are examined. These developments are linked to performance improvements in alkaline H 2 /O 2 fuel cells and also consider developments in alkaline fuel cells using nitrogen‐containing fuels (ammonia, hydrazine), carbon‐containing fuels (alcohols, glycols), and boron‐containing fuels (sodium borohydride, ammonia borane). Finally, current challenges and bottlenecks are identified, and potential solutions are proposed.
Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protocols remains unclear beyond certain commuting Hamiltonians. This work provides significant analytical and numerical insights into the power of dissipation for preparing the ground state of noncommuting Hamiltonians. For quasi-free dissipative dynamics, including certain 1D spin systems with boundary dissipation, our results reveal a new connection between the mixing time in trace distance and the spectral properties of a non-Hermitian Hamiltonian, leading to an explicit and sharp bound on the mixing time that scales polynomially with system size. For more general spin systems, we develop a tensor network-based algorithm for constructing the Lindblad jump operator and for simulating the dynamics. Using this algorithm, we demonstrate numerically that dissipative ground state preparation protocols can achieve rapid mixing for certain 1D local Hamiltonians under bulk dissipation, with a mixing time that scales logarithmically with the system size. We then prove the rapid mixing result for certain weakly interacting spin and fermionic systems in arbitrary dimensions, extending recent results for high-temperature quantum Gibbs samplers to the zero-temperature regime. Together, these results show that dissipation can be a powerful tool for ground state preparation, with potential applications across condensed matter physics, quantum materials science, and beyond.
Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.
This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.
DNA-functionalized colloidal nanoparticles assemble through flexible, nanoscale DNA hybridization interactions that limit atomic-level structural order. Here, we report a valence-centric strategy that enables DNA-bonded, protein single crystals with unconventional mechanical properties. An octameric enzyme, glutarate L-2-hydroxylase, was site- and number-selectively conjugated with eight self-complementary single-stranded DNA, yielding octavalent molecular bonds. The resulting conjugate assembled into the designed body-centered tetragonal crystals that diffracted to 1.42- to 2.61-angstrom resolution, with contacts mediated by B-form DNA helices spanning 17 to 25 angstroms. Increasing oligonucleotide length induces anisotropic lattice expansion while preserving atomic periodicity, even with partial DNA occupancy. Mechanistic studies suggest that the dynamic motion of unhybridized DNA facilitates crystallization, analogous to fluctuating electron clouds in atomic bonding. Compared with native protein crystals, DNA-hybridized crystals are 23-fold softer. These results challenge the assumption that flexibility is incompatible with structural order and establish a programmable framework for biomolecular crystallization and nanomaterials engineering with atomic precision.