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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗
DOE OSTI2025

Multi-faceted framework for extrapolating early age flexural strength to facilitate rapid lifting/handling of high-volume fly ash precast members

Maintaining adequate early-age structural performance for precast concrete components has grown in importance as more sustainable mix designs become more widespread. Achieving high-early flexural strength is particularly crucial to facilitate rapid removal of hardened concrete components from formwork, often within 24 h after fresh concrete placement. Limited research has assessed the effectiveness of traditional design methods in correlating flexural strength with compressive strength for next-generation mix designs, or demonstrated extrapolation of such material performance to larger-scale structural tests. This paper presents a multi-faceted framework to reassess early-age flexural strength for concretes made with relatively high proportions of fly ash from both fresh and harvested sources. Here, the framework provides several pathways, from which the user can select based upon available resources and the specific application, to improve accuracy of early-age cracking moment calculations. Furthermore, the scope includes evaluation of strength performance under curing conditions emulative of those in a precast facility, recommending modulus of rupture equations which are more performance-driven than current design provisions, and experimental tests on prefabricated concrete beams to validate the proposed methodologies. Correlations of early-age strength with both concrete age and maturity measurements compare the effectiveness of utilizing in-situ data to further enhance the prediction methods. Ultimately, the proposed framework helped reduce errors when calculating cracking moment capacity at early ages by tailoring calculations to reflect mix-dependent behavior. Furthermore, most estimates of cracking moment were within 25 % of their corresponding experimental test results, thus promoting confidence for using these strategies with high-volume fly ash precast structures.

42 ENGINEERING↗
DOE OSTI2026

An uncertainty visualization framework for large-scale cardiovascular flow simulations: A case study on aortic stenosis

We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated on a patient-specific stenosed aorta. The framework combines EasyVVUQ for parameter sampling with large-eddy simulation turbulence modeling in HemeLB, and executes ensembles on the Frontier exascale supercomputer. Spatially resolved metrics, including entropy and isosurface-crossing probability, are used to map uncertainty in pressure and wall shear stress fields directly onto vascular geometries. Two sources of model variability are examined: inlet peak velocity and the Smagorinsky constant. Inlet velocity variation produces high uncertainty downstream of the stenosis where turbulence develops, while upstream regions remain stable. Smagorinsky constant variation has little effect on the large-scale pressure field but increases WSS uncertainty in localized high-shear regions. In both cases, the stenotic throat manifests low entropy, indicative of robust identification of elevated WSS. By linking quantitative UQ measures to three-dimensional anatomy, the framework improves interpretability over conventional 1D UQ plots and supports clinically relevant decision-making, with broad applicability to vascular flow problems requiring both accuracy and spatial insight.

Hemodynamics↗
DOE OSTI2025

An accelerated framework for predicting creep rupture lifetimes in engineering alloys

Confidently predicting high-temperature deformation, including creep and creep rupture, is paramount for the design and commercialization of candidate materials for advanced nuclear energy systems. To accelerate creep quantification, we introduce a framework that enables rapid, cost-effective, and reliable prediction of creep rupture lifetimes, minimizing reliance on time-intensive bulk creep testing. Unlike conventional creep analysis, which requires extensive time and resources, our method leverages a maximum of four short-term bulk creep tests as training data for prediction. This framework combines high-throughput nanoindentation up to 700 °C with these targeted bulk tests to inform our creep rupture model in order to predict rupture lifetimes. The strong agreement between our predictions and conventional experimental data demonstrates the effectiveness of our approach for accelerated creep analysis and lifetime prediction of structural components in high-temperature applications. Our multi-pronged approach motivates further integration of computational tools and advanced instrumentation to establish a universal framework for understanding high-temperature material responses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2026

A framework and calculator for evaluating the impacts of shelf life extension and other food loss and waste reduction technologies

Optimization of the food supply chain (FSC) depends on reducing food waste, especially at the consumer stage, where a substantial portion of food is not eaten, but instead disposed of via landfill, incineration, or in-sink disposals. One key strategy is to increase the time that consumers have before food goes bad or expires. This study developed a framework to assess the efficacy of shelf-life extension (SLE) technologies for mitigating food loss and waste (FLW), such as packaging improvements. The impact flows through the entire FSC, reducing FLW, energy use, and other inputs at each stage. The framework and resulting calculator can be used to evaluate the impact of FLW reduction at any stage for any food commodity. As shown by two SLE cases, the calculator is valuable for policy-makers, government entities, and professionals, specifically those in marketing, business development, and capital projects teams, to comprehensively evaluate the impacts of FLW reduction technologies and practices. The framework and calculator are sensitive to the shape of the consumption curve, the fraction of inedible waste, and the current shelf life. The calculator was used to assess the impacts of the United States goal of reducing food waste by consumers through various SLE lengths. It was found that uptake of several near-ready-to-deploy SLE technologies would reduce annual food production demand by about 10–19 MMT and supply chain energy consumption by 240–410 PJ in the United States.

Food loss and waste (FLW)↗
DOE OSTI2026

Enabling microbial electrolysis cell scale-up via electrochemistry-, hydrodynamic-, and microbial ecology-informed framework

Microbial electrolysis cells (MECs) can produce green hydrogen while removing organic contaminants from liquid waste streams by leveraging the metabolic activity of electroactive microorganisms. Despite their potential in a sustainable, circular economy, large-scale MECs that can treat relevant volumes of wastewater have failed to deliver performance proportional to their lab-scale counterparts. The reason behind this lower performance at scale remains unclear. Here, in this study, we developed a combined electrochemistry-, hydrodynamic-, and microbial ecology-informed framework to analyze and optimize MEC performance during scale-up, enabling accurate quantification of major limitations and the identification of strategies to overcome them, ultimately facilitating equivalent performance at scale. Applying this framework to the scale-up of a zero-gap MEC from 9 cm 2 electrode area to 100 cm 2 electrode area, resulted in similar maximum current densities in a 100 cm 2 MEC (21.7 ± 1.1 A/m 2 ) compared to a 9 cm 2 system (25.1 ± 2.7 A/m 2 ), as well as equivalent hydrogen production rates of 69.3 L/L-d (100 cm 2 ) and 67.7 ± 2.4 L/L-d (9 cm 2 ). COMSOL flow dynamics simulations were used to scale up the reactor configuration without negatively affecting electrolyte velocity and distribution in the cell, minimizing the increase in internal resistances during scale-up (11.7 ± 0.5 mΩm 2 at 9 cm 2 ; 19.7 ± 1.3 mΩm 2 at 100 cm 2 ). Microbial community structures were assessed at both scales using high-throughput sequencing, highlighting the differences of populations across electrode dimensions and operational parameters. The framework presented here accelerates the development of effective strategies toward the scale-up of MECs by furthering the understanding of how electrochemical, hydrodynamic, and microbial ecology parameters change as the reactor dimension is increased. Ultimately, this approach contributes to advancing electrochemical biotechnology toward practical deployment in energy-efficient wastewater treatment systems.

Flow path↗
DOE OSTI2025

Role of Ammonium Hydroxide on Glucose Oxidase Immobilized in Metal–Azolate Framework-7 Enzyme Activity

Enzymes are nature’s catalysts, but their instability outside native environments limits practical applications. Metal–organic frameworks (MOFs) offer a promising platform for enzyme immobilization, enhancing stability, and reusability through their tunable porosity and crystallinity. While ZIF-based MOFs have been extensively studied, metal azolate framework-7 (MAF-7) remains largely unexplored for biomimetic mineralization. Its hydrophilic character makes it a promising alternative for enzyme encapsulation; however, the role of essential basic modulators such as ammonium hydroxide (NH 4 OH) on enzyme structure and function has not been investigated. Here, in this study, we examine the NH 4 OH mediated synthesis of glucose oxidase within MAF-7 using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), cryogenic transmission electron microscopy (cryo-TEM), and enzyme assays. Our results show that NH 4 OH modulates crystal morphology, enzyme activity, and nucleation behavior, with higher concentrations leading to partial enzyme denaturation and reduced catalytic performance. By integrating time-resolved cryo-TEM with activity assays, we uncover how modulator concentration impacts early stage crystallization and enzyme folding, a mechanistic insight not previously demonstrated in MOF systems. These findings highlight modulator chemistry as a critical, underexplored variable in MOF synthesis, advancing rational design strategies for enzyme@MOFs (E@MOF) beyond ZIFs and toward more tunable, biocompatible frameworks like MAF-7.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthetic Accessibility and Sodium Ion Conductivity of the Na8–x A x P2O9 (NAP) High-Temperature Sodium Superionic Conductor Framework

Advancement of solid-state electrolytes (SSEs) for all solid-state batteries typically focuses on modification of a known structural framework to improve conductivity, e.g., cation substitution for an immobile ion or varying the concentration of the mobile ions. Novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, thereby unlocking optimal conductors with different properties. Herein, we perform a high-throughput survey of a structural framework for sodium ion conduction, Na8–x A x P2O9 (NAP), to understand the family’s thermodynamic stability, synthesizability, and ionic conduction. We show that the parent phase Na4TiP2O9 (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons arising from pseudo-Jahn–Teller mode in the 1D titanium chains. Screening compounds in which Ti is substituted by other metals computationally reveal a number of candidates that are predicted to be low in formation energy and have high predicted ionic conductivities. High-throughput experimental and subsequent methodology optimization trials deliver one new compound, Na4SnP2O9 (NSP). X-ray diffraction (XRD), microscopy, and spectroscopy characterization indicate that the room-temperature structure of NSP is similar to the high-temperature, orthorhombic NTP phase but with some small unresolved structural differences. These uncharacterized structural details are speculated to limit the ion conductivity. Temperature-dependent XRD and electrochemical impedance spectroscopy indicate multiple coupled conductivity–structure transitions at a high temperature. We demonstrate the challenges with synthesis development and a priori identification of promising SSE phases as a major bottleneck in new (energy) materials development.

Chemical reactions↗
DOE OSTI2024

A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma

Undocumented Orphaned Wells (UOWs) are wells without an operator that have limited or no documentation with regulatory authorities. An estimated 310,000 to 800,000 UOWs exist in the United States (US), whose locations are largely unknown. These wells can potentially leak methane and other volatile organic compounds to the atmosphere, and contaminate groundwater. In this study, we developed a novel framework utilizing a state-of-the-art computer vision neural network model to identify the precise locations of potential UOWs. The U-Net model is trained to detect oil and gas well symbols in georeferenced historical topographic maps, and potential UOWs are identified as symbols that are further than 100 m from any documented well. A custom tool was developed to rapidly validate the potential UOW locations. We applied this framework to four counties in California and Oklahoma, leading to the discovery of 1301 potential UOWs across >40,000 km 2 . We confirmed the presence of 29 UOWs from satellite images and 15 UOWs from magnetic surveys in the field with a spatial accuracy on the order of 10 m. This framework can be scaled to identify potential UOWs across the US since the historical maps are available for the entire nation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2026

Nanocluster Rearrangement Forms a Family of Ordered Cerium–Titanium Bimetallic Metal–Organic Frameworks with Three Different Nodes, Nanocavities, and Thermal Stabilities

Metal–organic frameworks (MOFs) provide a versatile platform for incorporating multiple metal ions within a single crystalline framework, yet achieving spatial and stoichiometric order in heterometallic nodes remains a synthetic challenge. Building on our previously reported, highly thermally stable Ce/Ti bimetallic MOF NU-3000, we identified and isolated two additional crystalline phases, NU-2998 and NU-2999, that arise from the same Ce/Ti nanocluster precursor under modified solvothermal conditions. Systematic variation of reaction temperature, time, solvent ratio, and modulator concentration directs the assembly of these distinct frameworks. Structural analysis and comprehensive characterization studies reveal that these MOFs each feature an unreported nodal geometry with nanocavities of different sizes. NU-2998 even adopts an unreported topology, denoted nui , that features an elongated pore spanning 4 nm. Together, these findings establish a synthesis route that starts with a nanocluster and ends with a set of bimetallic MOFs, offering a glimpse into the pathway-dependent assembly of multimetallic porous materials. Finally, we evaluated the thermal stability of each additional analogue and compared them to NU-3000, providing further insight into material stability. NU-3000 maintained the highest thermal stability and was evaluated as a catalyst for CO oxidation at elevated temperatures.

bimetallic MOFs↗
DOE OSTI2024

Critical Role of Framework Flexibility and Disorder in Driving High Ionic Conductivity in LiNbOCl 4

Understanding Li-ion transport is key for the rational design of superionic solid electrolytes with exceptional ionic conductivities. LiNbOCl 4 is reported to be one of the most highly conducting materials in the recently realized new class of soft oxyhalide solid electrolytes, exhibiting an ionic conductivity of ~11 mS·cm -1 . Here, we apply X-ray/neutron diffraction and pair distribution function analysis - coupled with density functional theory/ab-initio molecular dynamics - to determine a structural model that provides a rationale for the high conductivity that we observe experimentally in this nanocrystalline solid. We show that it arises from unusually high framework flexibility at room temperature. This owes to isolated 1-D [NbOCl 4 ] - anionic chains which exhibit energetically favorable orientational disorder that is - in turn - correlated to multiple, disordered and equi-energetic Li + sites in the lattice. As the Li-ions sample the 3-D energy landscape with a fast predicted diffusion coefficient of 5.1 x 10 -7 cm 2 /s at room temperature (σ i calc = 17.4 mS·cm -1 ), the inorganic polymer chains can reorient or vice versa. The activation energy barrier for Li migration through the frustrated energy landscape is especially reduced by the elastic nature of the NbO 2 Cl 4 octahedra evident from very widely dispersed Cl-Nb-Cl bond angles in AIMD snapshots at 300 K. The phonon spectra are predominantly influenced by Cl vibrations in the low energy range, and there is strong overlap between the framework (Cl, Nb) and Li partial pDOS in the region between 1.2 - 4.0 THz. The framework flexibility is also reflected in a relatively low bulk modulus of 22 GPa. In conclusion, our findings pave the way for investigation of future “flex-ion” inorganic solids and open up a new direction for the design of high conductivity, soft solid electrolytes for all-solid-state batteries.

AIMD↗
DOE OSTI2025

Electrochemical CO 2 Capture by a Quinone-Based Covalent Organic Framework

Electrochemical CO 2 capture is an emerging technology that promises to be more energy-efficient than traditional thermal or pressure-swing processes. Herein, the first evidence of electrochemical capture of CO 2 using a covalent organic framework (COF) is presented. We hypothesized that the assembly of anthraquinone units into a well-defined porous framework electrode would lead to enhanced electrochemical CO 2 capture compared to previous approaches that grafted anthraquinones on carbon supports and suffered from low CO 2 capacities and stabilities. To test this, an anthraquinone-based COF is employed, and it is found that the quinones are electrochemically accessible for reversible CO 2 capture in an ionic liquid electrolyte. The system achieves a high electrochemical CO 2 uptake capacity >2.6 mmol g –1 COF, reaching half of the theoretical CO 2 capacity of the material and surpassing the capacities of anthraquinone-functionalized carbons. The stability and CO 2 uptake rate issues encountered with the ionic liquid system are also addressed by using aqueous electrolytes where we attained stable carbon capture for 500 cycles with a 99.6% Coulombic efficiency and an electrical energy consumption of 31 kJ mol CO 2 –1 . The use of covalent organic framework electrodes can become a general strategy for understanding and enhancing the electrochemical CO 2 capture.

carbon capture and storage↗
DOE OSTI2026

Toward Hydrogen Isotope Separations through Strong Hydrogen Adsorption at Open Copper(I) Sites in an Ultramicroporous Metal-Organic Framework

Metal-organic frameworks with coordinatively unsaturated metal sites (open metal sites) capable of engaging in orbital interactions with pi-acidic gases are of interest for enabling ambient-temperature gas separations, such as hydrogen isotope separations. In view of the weakly pi-acidic nature of H2, we sought to strengthen pi-backbonding-mediated H2 adsorption through pore confinement effects. Toward that end, we synthesized and characterized the ultramicroporous metal-organic framework CuxZn5-xCl4-yHz(bbta)3 (CuIZn-MFU-4; H2bbta = 1H,5H-benzo(1,2-d:4,5-d')bistriazole), featuring pi-basic trigonal pyramidal CuI sites that reside within 7 A of one another at their closest. Gas adsorption measurements reveal an H2 adsorption enthalpy of -38 kJ/mol, exceeding that of the larger-pore analog (CuIZn-MFU-4l; -33 kJ/mol) and representing the strongest H2 adsorption yet achieved in a metal-organic framework. The stronger H2 adsorption in CuIZn-MFU-4 is attributed to a combination of pore confinement effects and the increased ..sigma..-accepting nature of the CuI sites caused by a more electron-withdrawing bbta2- linker, as supported by structural, spectroscopic, and computational evidence. With the strongest H2 adsorption, equilibrium isotope effects in CuIZn-MFU-4 lead to a D2/H2 selectivity (as estimated by ideal adsorbed solution theory) of 1.35 even at 298 K, approaching the values reported below 200 K for conventional porous materials.

08 HYDROGEN↗
DOE OSTI2026

Field-Coupled Water Splitting with Metal-Free Donor–Acceptor Covalent Organic-Framework Junctions

Advancing metal-free electrocatalysts for hydrogen and oxygen evolution reactions (HER/OER) across acidic and alkaline media requires coordinated control of intermediate binding thermodynamics, interfacial charge delivery, and near-electrode transport dynamics. Here, we design amide-linked benzene–triazine covalent organic frameworks (BTA/TzTA-Hz COFs) and integrate them with carbon nanotubes (CNTs) to form COF–CNT junctions that establish a built-in interfacial electric field. Density functional theory (DFT) and electrostatic potential maps indicate complementary active motifs, with benzene-proximal fragments associated with HER and triazine-proximal motifs associated with OER. CNT integration shifts the contact-potential difference by ≈0.20 V, while operando electrochemical impedance spectroscopy suggests partially separable high-frequency junction-charging and lower-frequency Faradaic/transport responses. A 300 mT static magnetic field lowers the HER and OER overpotentials by tens of millivolts. Under anodic bias, the effective interfacial charging capacitance increases, and Mott–Schottky analysis shows an apparent ∼0.15 V flat-band shift with an essentially unchanged slope. Together, these observations are consistent with field-perturbed interfacial charging and altered bias partitioning. Field-dependent impedance and bubble imaging are consistent with magnetohydrodynamic convection that promotes bubble detachment and near-electrode mass transport for both half-reactions, and they reveal an OER-specific high-frequency perturbation under anodic bias. Under field, the heterostructure reaches an OER onset overpotential of ∼261 mV and requires an overpotential of 366 mV at 10 mA cm –2 in alkaline electrolyte. These results illustrate how reticular-framework chemistry, junction engineering, and both built-in and applied fields can program reactivity through interfacial electrostatics and near-electrode transport in organic-framework electrocatalysts.

Garcia-Enriquez, Lissette [Univ. of Texas at El Pa↗