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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ion-imbibed membranes based on proton conducting aromatic polyether type copolymers and their application in redox flow batteries

The present invention relates to a class of polymer ion imbibed membranes for electrolyte flow batteries. The membranes are a conducting aromatic polyether type copolymer bearing nitrogen heterocycles groups, especially pyridine type. While the membranes can be used in acid, basic, and neutral electrolytes, the nitrogen heterocycles in the membrane interact with acid in the electrolyte to form a proton transport network, so as to keep the proton transport performance of the membrane. The membrane has excellent mechanical stability and thermostability as well as tunable porosity.

Paloumbis, George↗
DOE OSTI2022

Redox enzyme-embedded pyrene-poly(ethylenimine) hydrogel electrode for electrosynthesis

Devices, systems, and compositions of matter involving enzyme-mediated bioelectrocatalysis are disclosed and described. An enzyme electrode can include an electrode, a bioelectric material coupled to the electrode, the bioelectric material further including a water-permeable polymer matrix, a planar linker covalently coupled to the water-permeable polymer matrix and noncovalently coupled to the electrode, and electrochemically active oxidoreductase enzyme molecules functionally embedded in the water-permeable polymer matrix.

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

Direct Study of Changes in Catalyst Structure-Kinetic Properties During Redox Transitions

The Temporal Analysis of Products (TAP) pulse response methodology is a transient technique that provides the time resolution needed to deconvolve reaction steps from the complex networks typical in industrial catalytic processes. Traditionally, TAP measurements observe gas phase dynamics at the reactor exit but lack direct measurements of changes in the catalyst itself. Recently, a new operando technique was developed that couples gas phase transients to dynamic changes in metal centers with the precise TAP methodology for nanomole titration. Using an industrial CrOx/Al2O3 catalyst used for propane dehydrogenation, we demonstrate the capabilities of this unique device to reveal key catalytic processes: 1) total propane oxidation not associated with chromia centers, 2) reduction of Cr6+ to Cr3+ correlated with selective product formation, and 3) subsequent carbon accumulation. By utilizing incremental pulsing in a diffusion-only transport regime, the spectrokinetic device allows us to resolve detailed changes in catalyst structure, composition, and kinetic function that are otherwise indistinguishable in conventional operando devices. The unification of the TAP methodology with time-resolved spectroscopic measurements offers new and unique insights into the complex kinetic phenomena regulated by solid catalyst surfaces.

03 - NATURAL GAS↗
DOE OSTI2021

Defect-Driven Oxide Transformations and the Electrochemical Interphase

The redox reaction pathway is crucial to the sustainable production of the fuels and chemicals required for a carbon-neutral society. Our society is becoming increasingly dependent on devices using batteries and electrolyzers, all of which rely on a series of redox reactions. The overall properties of oxide materials make them very well suited for such electrochemical and catalytic applications due to their associated cationic redox properties and the static site-adsorbate interactions. As these technologies have matured, it has become apparent that defect-driven redox reactions, defect-coupled diffusion, and structural transformations that are both time- and rate-dependent are also critical materials processes. This change in focus, considering not only redox properties but also more complex, dynamic behaviors, represents a new research frontier in the molecular sciences as they are strongly linked to device operation and degradation and lie at the heart of various phenomena that take place at electrochemical interfaces. Fundamental studies of the structural, electronic, and chemical transformation mechanisms are key to the advancement of materials and technological innovations that could be implemented in various electrochemical systems. In this Account, we focus on recent studies and advances in characterizing and understanding the dynamic redox evolution and structural transformations that take place in model perovskites and layered oxides under reactive conditions and correlate them with degradation mechanisms and operations in electrolyzers and batteries. We show that the dynamic evolution of oxygen vacancies and cationic migration in the surface or bulk occurs at the solid-liquid interface, using a combination of different synchrotron-based Xray spectroscopies and scattering probes. Detailed redox-structure-reactivity correlation studies show how defects and diffusion processes can be tailored to drive various physical and chemical transformations in electrolyzers and batteries. We also highlight a strong correlation between oxygen redox reactivity and structural reorganization in both model thin films and particles, helping to bridge the gap between fundamental studies of the reaction mechanism and device applications. Finally, on the basis of these findings, we discuss strategies to probe and tune the redox reactivity and structural stability of the redox-active oxide interphase toward devising efficient pathways for energy and chemical harvesting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Mesolytic cleavage of homobenzylic ethers for programmable end-of-life function in redoxmers

Irreparable chemical damage to redox-active monomers, oligomers, and polymers (i.e. redoxmers) limits the lifetime of energy storage devices (e.g. redox flow batteries) by causing electrode and membrane fouling, as well as irreversible capacity loss. To predictably restore device and materials performance, it is desirable to add programmed destruction capabilities into these damage-prone materials. Here, we report the use of triggerable retrograde reactions to achieve programmable end-of-life function in redoxmers. Retrograde reactions are intended to break up damaged redoxmer materials into more soluble small-molecule constituents to prevent irreparably fouling battery interfaces. We investigated the redox-triggered mesolytic cleavage of homobenzylic ethers (HBEs) for this purpose. Combining experimental methods with simulations, we probed the influence of ring substituents on the programmable bond-scission behavior of HBEs. Variation of para-substituents on the HBEs allowed us to modify the oxidation potential, degradation pathway, and electrochemical mechanisms of the generated products. Given its ideal oxidation potential, we selected the para-methoxy-based HBE as the cleavage scaffold and integrated it with dialkoxybenzene, TEMPO, viologen, and para-nitrobenzene redox centers for compatibility, bulk deconstruction, and electrode defouling tests. Upon applying a high oxidation potential, the redox-active pendant is cleaved from the backbone via HBE bonds; of the four redox centers tested, three of them remained fully redox active. Viologen-appended redox active polymers with HBE linkers were cleaved to fully separate the redox centers from the backbone. Polymer-filmed electrodes were partially restored following HBE oxidation defouling. Here these studies highlight the co-design of new materials functions for developing sustainable energy storage materials.

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

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Thiol post-translational modifications modulate allosteric regulation of the OpcA–G6PDH complex through conformational gate control

In cyanobacteria, the redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and thereby precisely regulating carbon flux between anabolic and catabolic pathways. Although redox-sensitive cysteines in OpcA are known to regulate G6PDH, the mechanisms by which redox post-translational modifications (PTMs) on OpcA control G6PDH structure and activity remain unclear. Here, we combine computational modeling with experimental redox proteomics in Synechococcus elongatus PCC 7942 to dissect this mechanism. Experimentally, redox proteome analysis revealed differential redox PTM patterns, particularly on cysteines within the G6PDH-binding site of OpcA. These environmentally sensitive PTM changes at the interface suggest that thiol modifications in this region form a key regulatory node. More broadly, redox proteomics identified site-specific cysteine modifications under light/dark transitions and circadian cycling, linking distinct redox regimes to discrete PTM states. We employed PTM-Psi simulations to show that thiol PTMs near the OpcA–G6PDH interface are critical for allosteric regulation of G6PDH. The thiol PTMs on OpcA affect a putative gate region in G6PDH for substrate ingress and product egress as well as key hydrogen-bond networks within the active site. We infer that PTMs on OpcA tune the conformational landscapes of individual G6PDH subunits toward functionally relevant configurations according to environmental gradients, biasing the enzyme toward catalytically favorable states. Together, our results reveal a molecular mechanism in which thiol PTMs on OpcA modulate G6PDH structure and function through PTM-induced reorganization of conformational dynamics and allosteric communication. These findings demonstrate that PTM-level regulation provides a critical control layer from genotypes to phenotypes that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning.

Allosteric regulation↗
DOE OSTI2025

Decoding the Oxygen Activity with Iron through Ligand-to-Metal Charge Transfer in Li-Rich Layered Cathodes

The quest for high-energy-density and cost-effective cathode materials has revitalized lithium-rich iron-based oxides, where Fe 3+ ions with their stable half-filled d 5 electronic configuration demonstrate unique potentials in regulating oxygen redox activity through ligand-to-metal charge transfer. Using operando 57 Fe Mössbauer spectroscopy, synchrotron X-ray techniques, and density functional theory calculations, we unravel the enhanced Fe–O redox activity through the LMCT process and the concomitant evolution of local structure distortion in the Li-rich layered system Li 1.2 MO 2 (M = Ni, Mn, Fe). The metastable Fe 4+ intermediates facilitate early activation of oxygen redox through LMCT along with increased delithiation at a lowered potential (below 4.5 V). Here, this process inherently suppresses Jahn–Teller distortion of Fe 4+ and exhibits Fe–O redox dominance over conventional Fe 3+ /Fe 4+ redox in Li-rich oxides. The Fe–O coupling redox exhibits improved initial Coulombic efficiency and 95% capacity retention over 200 cycles at 4.4 V. However, the LMCT process is accompanied by aggravated cation migration and voltage fade at low cutoff potentials, which requires further mitigation for the utilization of Fe–O redox. Overall, these findings demonstrate the regulating mechanism of iron for oxygen redox through LMCT and provide fundamental insights into the design principles for high-performance, cost-effective iron-based cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗