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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Controlling solvation in conducting redox polymers for selective electrochemical separation of nitrate from wastewater

Selective capture of nitrate from wastewater is crucial for ensuring safe drinking water and promoting resource circularity. This study investigated alkylated polyaniline redox polymers as highly-selective electrosorbents to address this challenge. By controlling polymer solvation properties through synthetic functionalization, poly(N-methylaniline) (PNMA) achieves a nitrate uptake of up to 1.38 mmol g −1 -polymer and a separation factor of 7 over chloride. Poly(N-butylaniline) (PNBA) further enhances selectivity, achieving a separation factor beyond 14 due to increased hydrophobicity. The mechanisms underlying this selectivity are investigated using ab initio molecular dynamics (AIMD) and in-situ electrochemical quartz crystal microbalance (EQCM) studies, which reveal that hydrophobicity reduces chloride binding. A technoeconomic analysis indicates that methylation on PANI reduces nitrate removal costs by 50% compared to non-functionalized PANI, due to enhanced selectivity and uptake, and decreased energy consumption. PNMA electrodes demonstrate practical nitrate selectivity over 20 versus chloride in real wastewater, while avoiding sulfate binding. This study highlights the potential of controlling solvation at electroactive polymers to enhance nitrate selectivity, offering a promising design path for redox-mediated electrochemical separations.

chemical engineering↗
DOE OSTI2025

Enhanced production of active species and NH 3 using non-equilibrium ferroelectric barrier discharge

Non-equilibrium plasma-assisted ammonia synthesis is investigated through enhanced active species production with ferroelectric discharge. Time-resolved in-situ diagnostics of femtosecond two-photon absorption laser-induced fluorescence, coherent anti-Stokes Raman scattering, and laser absorption spectroscopy, as well as optical emission spectroscopy, were conducted to probe the key intermediate species, such as H and N radicals as well as N 2 (ν), ions, and NH 3 to achieve better understanding of non-equilibrium energy transfer and ammonia formation. The results reveal that ferroelectric discharge improved ammonia yield by four times. Results also show that ferroelectrics not only enhanced ions (N 2 + ) production, radicals (N, H) number density, but also increased the N 2 vibrational temperature. Further plasma modeling identified the couplings between elevated radical and ion production and enhanced vibrational excitation reactions, e.g., N + H 2 (ν)→NH + H, N 2 (ν)+H → NNH, N 2 + + H 2 → H + N 2 H + , and N 2 H + +e→NH + N, facilitated by ferroelectric discharge. These findings provide critical insight into the mechanism of ferroelectric plasma catalysis and highlight their potential in advancing energy-efficient chemical synthesis.

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

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A unifying equation for fermentation sustainability across the titer-rate-yield landscape

Industrial fermentation is central to the sustainable production of fuels and chemicals, yet commercial viability of emerging technologies hinges on improving fermentation titer, rate, and yield (TRY). How these metrics shape system cost remains difficult to generalize due to complex interactions among feedstocks, fermentation, separations, catalytic upgrading, waste management, and facility design. Here, we systematically map theoretical fermentation performance spaces (formed by all potential TRY combinations) for 32 representative biomanufacturing facilities—spanning distinct choices for feedstocks, fermentation regimes and products, separations, and catalytic upgrading—by simulating and evaluating them (via techno-economic analysis, TEA) under uncertainty (600,000 Monte Carlo simulations) and across TRY combinations (7500 TRY combinations for each of 32 configurations). Across this wide design and thermodynamic simulation space, we find the relationship between fermentation TRY and system cost is captured by a simple, generalizable mathematical equation (R 2 of 0.992 − 1.000 across our simulations; 0.954 − 1.000 when validated against prior studies that used different tools). We use this equation to elucidate key drivers that shape cost sensitivity to fermentation performance, generating widely applicable insights. By demonstrating a unifying relationship governs the impact of fermentation on biomanufacturing economics, this work establishes a foundation for agile, holistically predictive, resource-efficient strategies to prioritize fermentation research and development needs and accelerate commercialization of emerging biomanufacturing technologies.

applied mathematics↗
DOE OSTI2023

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2021

High-performing commercial Fe–N–C cathode electrocatalyst for anion-exchange membrane fuel cells

Here, to reduce the cost of fuel cell stacks and systems, it is important to create commercial catalysts that are free of platinum group metals (PGMs). To do this, such catalysts must have very high activity, but also have the correct microstructure to facilitate the transport of reactants and products. Here, we show a high-performing commercial oxygen reduction catalyst that was specifically developed for operation in alkaline media and is demonstrated in the cathode of operating anion-exchange membrane fuel cells (AEMFCs). With H 2 /O 2 reacting gases, AEMFCs made with Fe–N–C cathodes achieved a peak power density exceeding 2 W cm –2 (>1 W cm –2 with H 2 /air) and operated with very good voltage durability for more than 150 h. These AEMFCs also realized an iR-corrected current density at 0.9 V of 100 mA cm –2 . Finally, in a second configuration, Fe–N–C cathodes paired with low-loading PtRu/C anodes (0.125 mg PtRu per cm 2 , 0.08 mg Pt per cm 2 ) demonstrated a specific power of 10.4 W per mg PGM (16.25 W per mg Pt).

25 ENERGY STORAGE↗
DOE OSTI2024

Mild pH-decoupling aqueous flow battery with practical pH recovery

Establishing a pH difference between the two electrolytes (pH decoupling) of an aqueous redox flow battery (ARFB) enables cell voltages exceeding the 1.23 V thermodynamic water-splitting window, but acid–base crossover penalizes efficiency and lifetime. Here we employ mildly acidic and mildly alkaline electrolytes to mitigate crossover, achieving high round-trip energy efficiency with open circuit voltage >1.7 V. We implemented an acid–base regeneration system to periodically restore electrolytes to their initial pH values. The combined system exhibited capacity fade rate <0.07% per day, round-trip energy efficiency >85% and approximately 99% Coulombic efficiency during stable operation for over a week. Cost analysis shows that the tolerance of acid–base crossover could be increased if the pH-decoupling ARFB achieved a higher voltage output and lower resistance. Finally, this work demonstrates principles for improving lifespan, rate capability and energy efficiency in high-voltage pH-decoupling ARFBs and pH recovery concepts applicable for pH-decoupling systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Spatiotemporal control of liquid crystal structure and dynamics through activity patterning

Active materials are capable of converting free energy into mechanical work to produce autonomous motion, and exhibit striking collective dynamics that biology relies on for essential functions. Controlling those dynamics and transport in synthetic systems has been particularly challenging. Here, we introduce the concept of spatially structured activity as a means of controlling and manipulating transport in active nematic liquid crystals consisting of actin filaments and light-sensitive myosin motors. In this work, simulations and experiments are used to demonstrate that topological defects can be generated at will and then constrained to move along specified trajectories by inducing local stresses in an otherwise passive material. These results provide a foundation for the design of autonomous and reconfigurable microfluidic systems where transport is controlled by modulating activity with light.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unit-cell-thick zeolitic imidazolate framework films for membrane application

Zeolitic imidazolate frameworks (ZIFs) are a subset of metal–organic frameworks with more than 200 characterized crystalline and amorphous networks made of divalent transition metal centres (for example, Zn 2+ and Co 2+ ) linked by imidazolate linkers. ZIF thin films have been intensively pursued, motivated by the desire to prepare membranes for selective gas and liquid separations. To achieve membranes with high throughput, as in ångström-scale biological channels with nanometre-scale path lengths, ZIF films with the minimum possible thickness—down to just one unit cell—are highly desired. However, the state-of-the-art methods yield membranes where ZIF films have thickness exceeding 50 nm. Here we report a crystallization method from ultradilute precursor mixtures, which exploits registry with the underlying crystalline substrate, yielding (within minutes) crystalline ZIF films with thickness down to that of a single structural building unit (2 nm). The film crystallized on graphene has a rigid aperture made of a six-membered zinc imidazolate coordination ring, enabling high-permselective H 2 separation performance. The method reported here will probably accelerate the development of two-dimensional metal–organic framework films for efficient membrane separation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncovering the predictive pathways of lithium and sodium interchange in layered oxides

Ion exchange is a powerful method to access metastable materials with advanced functionalities for energy storage applications. However, high concentrations and unfavourably large excesses of lithium are always used for synthesizing lithium cathodes from parent sodium material, and the reaction pathways remain elusive. Here, using layered oxides as model materials, we demonstrate that vacancy level and its corresponding lithium preference are critical in determining the accessible and inaccessible ion exchange pathways. Taking advantage of the strong lithium preference at the right vacancy level, we establish predictive compositional and structural evolution at extremely dilute and low excess lithium based on the phase equilibrium between Li 0.94 CoO 2 and Na 0.48 CoO 2 . Such phase separation behaviour is general in both surface reaction-limited and diffusion-limited exchange conditions and is accomplished with the charge redistribution on transition metals. Guided by this understanding, we demonstrate the synthesis of Na y CoO 2 from the parent Li x CoO 2 and the synthesis of Li 0.94 CoO 2 from Na y CoO 2 at 1-1,000 Li/Na (molar ratio) with an electrochemical assisted ion exchange method by mitigating the kinetic barriers. Our study opens new opportunities for ion exchange in predictive synthesis and separation applications. Ion exchange is a powerful method to access metastable materials for energy storage, but identifying lithium and sodium interchange in layered oxides remains challenging. Using such model materials, vacancy level and corresponding lithium preference are shown to be crucial for ion exchange pathway accessibility.

25 ENERGY STORAGE↗
DOE OSTI2022

Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst

We report electrochemically converting nitrate ions, a widely distributed nitrogen source in industrial wastewater and polluted groundwater, into ammonia represents a sustainable route for both wastewater treatment and ammonia generation. However, it is currently hindered by low catalytic activities, especially under low nitrate concentrations. Here we report a high-performance Ru-dispersed Cu nanowire catalyst that delivers an industrial-relevant nitrate reduction current of 1 A cm –2 while maintaining a high NH 3 Faradaic efficiency of 93%. More importantly, this high nitrate-reduction catalytic activity enables over a 99% nitrate conversion into ammonia, from an industrial wastewater level of 2,000 ppm to a drinkable water level <50 ppm, while still maintaining an over 90% Faradaic efficiency. Coupling the nitrate reduction effluent stream with an air stripping process, we successfully obtained high purity solid NH 4 Cl and liquid NH 3 solution products, which suggests a practical approach to convert wastewater nitrate into valuable ammonia products. Density functional theory calculations reveal that the highly dispersed Ru atoms provide active nitrate reduction sites and the surrounding Cu sites can suppress the main side reaction, the hydrogen evolution reaction.

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

Low-iridium stabilized ruthenium oxide anode catalyst for durable proton-exchange membrane water electrolysis

While mixing iridium (Ir) with ruthenium oxide (RuO 2 ) has proven to be an effective strategy for reducing Ir loading in anode catalysts for proton-exchange membrane (PEM) water electrolysers, achieving industrially relevant long-term stability typically requires an Ir-rich, Ru-lean combination. Here, in this study, by combining density functional theory with Metropolis Monte Carlo methods, we discovered that sufficient stabilization in the RuO 2 lattice could be achieved with less than 50 at.% of Ir, and that Ir in the first subsurface layer plays a critical role. By effectively dispersing Ir dopants within the RuO 2 lattice, we demonstrated an Ir:Ru atomic ratio of only 1:6 that exhibited exceptional stability for over 1,500 h of continuous water electrolysis at 2 A cm −2 . Our Ru 6 IrO x catalyst has the potential to reduce Ir loading by 80% compared with current commercial PEM water electrolysers, and its stability was further validated under industrial testing conditions in a 25-cm 2 PEM electrolyser.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Selective sulfidation of metal compounds

There is urgent, unprecedented demand for critical by-product and co-product metallic elements for the infrastructure (magnets, batteries, catalysts and electronics) needed to power society with renewable electricity1,2,3. However, the extraction of d-block and f-block metals from mineral and recycled streams is thermodynamically difficult, typically requiring complete dissolution of the materials, followed by liquid–liquid separation using metal-ion complexing or chelating behaviour4,5. The similar electronic structure of these metals results in poor separation factors, necessitating immense energy, water and chemicals consumption6,7,8. Here a metal-processing approach based on selective anion exchange is proposed. Several simple process levers (gas partial pressure, gas flowrate and carbon addition) are demonstrated to selectively sulfidize a target metal from a mixed metal-oxide feed. The physical and chemical differences between the sulfide and oxide compounds (for example, density, magnetic susceptibility and surface chemistry) can then be exploited for vastly improved separation compared with liquid–liquid methods9. The process conditions of sulfidation are provided for 56 elements and demonstrated for 15 of them. An assessment of the environmental and economic impacts suggests a path towards 60–90% reductions in greenhouse gas emissions while offering substantial capital cost savings compared with liquid–liquid hydrometallurgy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

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

Atomic dynamics of gas-dependent oxide reducibility

Understanding oxide reduction is critical for advancing metal production, catalysis and energy technologies. Although carbon monoxide (CO) and hydrogen (H 2 ) are widely used reductants, the mechanisms by which they work are often presumed to be similar, both involving lattice oxygen removal. However, because of growing interest in replacing CO with H 2 to lower CO 2 emissions, distinguishing gas-specific reduction pathways is critical. Yet, capturing these atomic-scale processes under reactive gas and high-temperature conditions remains challenging. Here we use environmental transmission electron microscopy, which is capable of real-time, atomic-resolution imaging of gas–solid redox reactions to directly visualize the gas-dependent oxide reduction dynamics in NiO. We show that CO drives surface nucleation and the growth of metallic Ni islands, leading to self-limiting surface metallization. Conversely, H 2 activates a coupled surface-to-bulk transformation, where protons from dissociated H 2 infiltrate the oxide lattice to promote the inward migration of surface-generated oxygen vacancies and enabling bulk metallization. By contrast, oxygen vacancies formed by CO remain confined near the surface, where they rapidly form a metallic Ni layer that inhibits further reduction. Furthermore, these results reveal distinct atomistic pathways for CO and H 2 and provide insights that may guide metallurgical processes and catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions

Abstract Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12// ω B97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol −1 ) relative to those calculated at ω B97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients $${k}_{\infty }(T)$$ k ∞ ( T ) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗