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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Imaging Cycle-Induced Damage of MnO 2 Microparticles

MnO 2 has been proposed as an electrode material in electrochemical energy storage devices. However, poor cycle life, especially in aqueous electrolytes, remains a detriment to commercialization. Prior studies have suggested a number of explanations for this capacity loss; however, experiments aimed at elucidating the details of the degradation process (es) are sparse. We describe here a microtube-membrane construct that allows for electrodeposition of monodisperse MnO 2 microparticles distributed across the membrane surface, and for subsequent electrochemical cycling of these MnO 2 particles. This allowed for a detailed analysis of the effect of cycling on the MnO 2 , by simply imaging the membrane surface before and after cycling. When an aqueous electrolyte was used, gross changes in particle shape, size and morphology were observed over the course of 500 cycles. Partial dissolution occurred as well. No such changes were observed when the MnO 2 particles were cycled (up to 500 times) in a propylene carbonate electrolyte solution.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Coupling Noah-Multiparameterization land-surface Model with Energy Research and Forecasting Model

The Energy Research and Forecasting (ERF) model is a high-performance atmospheric model built on the AMReX adaptive mesh refinement (AMR) framework, enabling efficient simulations on heterogeneous computing platforms that combine multicore processors with hardware accelerators. To support land–atmosphere interactions within ERF’s AMR-based environment, a land-surface model must be capable of operating directly on hierarchically refined meshes. In this work, we present a methodology for coupling the Fortran-based Noah-Multiparameterization (Noah-MP) land-surface model with ERF’s C++ codebase. Rather than rewriting Noah-MP, we construct a Fortran–C interoperability layer using CodeScribe, a tool that leverages large language models (LLMs) to automate the generation of interface code. CodeScribe applies structured prompting techniques to generate bindings that support efficient data exchange and function calls between ERF and Noah-MP. The coupling framework also incorporates AMR-aware data handling strategies, allowing NoahMP to operate seamlessly within ERF’s hierarchical mesh structure. This work provides a structured approach for integrating legacy Fortran models into modern C++-based modeling systems using LLM-assisted code generation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Galaxy Clustering in the Mira-Titan Universe. I. Emulators for the Redshift Space Galaxy Correlation Function and Galaxy–Galaxy Lensing

Abstract We construct accurate emulators for the projected and redshift space galaxy correlation functions and excess surface density as measured by galaxy–galaxy lensing, based on halo occupation distribution modeling. Using the complete Mira-Titan suite of 111 N -body simulations, our emulators vary over eight cosmological parameters and include the effects of neutrino mass and dynamical dark energy. We demonstrate that our emulators are sufficiently accurate for the analysis of the Baryon Oscillation Spectroscopic Survey DR12 CMASS galaxy sample over the range 0.5 ≤ r ≤ 50 h −1 Mpc. Furthermore, we show that our emulators are capable of recovering unbiased cosmological constraints from realistic mock catalogs over the same range. Our mock catalog tests show the efficacy of combining small-scale galaxy–galaxy lensing with redshift space clustering and that we can constrain the growth rate and σ 8 to 7% and 4.5%, respectively, for a CMASS-like sample using only the measurements covered by our emulator. With the inclusion of a cosmic microwave background prior on H 0 , this reduces to a 2% measurement of the growth rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Fuzzball Paradigm

We describe the puzzles that arise in the quantum theory of black holes, and explain how they are resolved in string theory. We review how the Bekenstein entropy is obtained through the count of brane bound states. We describe the fuzzball construction of black hole microstates. These states have no horizon and radiate from their surface like a normal body, so there is no information puzzle. We explain how the semiclassical approximation is violated in gravitational collapse even though curvatures are low at the classical horizon. This violation happens because the collapse leads to a stretching of space that is {\it fast}: light does not have time to travel across the collapsing region to establish the `vecro' correlations needed in the quantum gravitational vacuum. These vecro correlations arise from the existence of virtual fuzzball fluctuations in the gravitational vacuum, and are significant because of the large degeneracy of fuzzball states implied by the Bekenstein entropy. It is plausible that similar effects of fast expansion may be responsible for effects like dark energy and the Early Dark Energy postulated to explain the Hubble tension.

FOS: Physical sciences↗
DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

SPiRaL: a multiresolution global tomography model of seismic wave speeds and radial anisotropy variations in the crust and mantle

SUMMARY SPiRaL is a joint global-scale model of wave speeds (P and S) and anisotropy (vertical transverse isotropy, VTI) variations in the crust and mantle. The model is comprised of >2.1 million nodes with five parameters at each node that capture velocity variations for P- and S-waves travelling at arbitrary directions in transversely isotropic media with a vertical symmetry axis (VTI). The crust (including ice, water, sediments and crystalline layers) is directly incorporated into the model. The default node spacing is approximately 2° in the lower mantle and 1° in the crust and upper mantle. The grid is refined with ∼0.25° minimum node spacing in highly sampled regions of the crust and upper mantle throughout North America and Eurasia. The data considered in the construction of SPiRaL includes millions of body wave traveltimes (crustal, regional and teleseismic phases with multiples) and surface wave (Rayleigh and Love) dispersion. A multiresolution inversion approach is employed to capture long-wavelength heterogeneities commonly depicted in global-scale tomography images as well as more localized details that are typically resolved in more focused regional-scale studies. Our previous work has demonstrated that such global-scale models with regional-scale detail can accurately predict both teleseismic and regional body wave traveltimes, which is necessary for more accurate location of small seismic events that may have limited signal at teleseismic distances. SPiRaL was constructed to predict traveltimes for event location and long-period waveform dispersion for seismic source inversion applications in regions without sufficiently tuned models. SPiRaL may also serve as a starting model for full-waveform inversion (FWI) with the goal of fitting waves with periods 10–50 s over multiple broad regions (thousands of kilometres) and potentially the globe. To gain insight to this possibility, we simulated waveforms for a small set of events using SPiRaL and independent waveform-based models for comparison. For the events tested, the performance of the traveltime-based SPiRaL model is shown to be generally on par with regional 3-D waveform-based models in three regions (western United States, Middle East, Korean Peninsula) suggesting SPiRaL may serve as a starting model for FWI over broad regions.

58 GEOSCIENCES↗
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2023

Open millifluidics based on powder-encased channels

Millifluidics, the manipulation of liquid flow in millimeter-sized channels, has been a revolutionary concept in chemical processing and engineering. The solid channels that contain the liquids, though, are not flexible in their design and modification, and prevent contact with the external environment. All-liquid constructs, on the other hand, while flexible and open, are imbedded in a liquid environment. Here, we provide a route to circumvent these limitations by encasing the liquids in a hydrophobic powder in air that jams on the surface, containing and isolating flowing fluids, offering flexibility and adaptability in design, as manifest in the ability to reconfigure, graft, and segment the constructs. Along with the open nature of these powder-contained channels that allow arbitrary connections/disconnections and substance addition/extraction, numerous applications can be opened in the biological, chemical, and material arenas.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Engineering Surface Oxygenated Functionalities on Commercial Carbon toward Ultrafast Sodium Storage in Ether-Based Electrolytes

The pursuit of a high-capacity anode material has been urgently required for commercializing sodium-ion batteries with a high energy density and an improved working safety. In the absence of thermodynamically stable sodium intercalated compounds with graphite, constructing nanostructures with expanded interlayer distances is still the mainstream option for developing high-performance carbonaceous anodes. In this regard, a surface-functionalized and pore-forming strategy through a facile CO 2 thermal etching route was rationally adopted to engineer negligible oxygenated functionalities on commercial carbon for boosting the sodium storage process. Benefitted from the abundant ionic/electronic pathways and more active reaction sites in the microporous structure with noticeable pseudocapacitive behaviors, the functionalized porous carbon could achieve a highly reversible capacity of 505 mA h g -1 at 50 mA g -1 , an excellent rate performance of 181 mA h g -1 at 16,000 mA g -1 , and an exceptional rate cycle stability of 176 mA h g -1 at 3200 mA g -1 over 1000 cycles. These outstanding electrochemical properties should be ascribed to a synergistic mechanism, fully utilizing the graphitic and amorphous structures for synchronous intercalations of sodium ions and solvated sodium ion compounds, respectively. Finally, the controllable generation and evolution of a robust but thin solid electrolyte interphase film with the emergence of obvious capacitive reactions on the defective surface, favoring the rapid migration of sodium ions and solvated species, also contribute to a remarkable electrochemical performance of this porous carbon black.

25 ENERGY STORAGE↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Porewater and Surface Water Chemistry of Wetland, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2022-07-06 to 2023-12-15

This dataset contains the chemistry data of surface water and porewater samples collected from a wetland, referred to as The Cove, at Old Woman Creek Estuarine Research Reserve in Huron, OH. Surface and pore water samples were taken to analyze what nutrient and/or metal constituents were present at different depths in the wetland at different times and to compare to redox potential values. Porewater was collected by constructed sippers and deployed into the underlying soil of the wetland, and they were samples approximately every 2 weeks (when groundwater was present) in 2023. Surface water was collected approximately 1 inch below the surface of the water column (if available) via a syringe. Unfiltered and 0.45 micron filtered samples were collected. Samples were analyzed for various chemistry and is reported in the SWChem (surface water chemistry) and PWChem (porewater chemistry) .csv datafiles. Collection information about the samples can be found within the PWChem_SWChem_SampleMetdata.csv file. All files associated with this dataset are listed in the PWChem_SWChem_FLMD.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

In-situ thermodynamics measurements at metal oxides-solution interfaces using Flow Adsorption Microcalorimetry.

Mineral-fluid interfaces are the principal sites of geochemical processes near Earth’s surface, hosting chemical reactions that play a fundamental role in (bio)geochemical cycles and in the fate and transport of anthropogenic contaminants, and that effectively control the compositions of soil and water environments. These complex interfaces are critical for our energy and environmental future. The mineral-fluid interface has been studied in an unprecedented level of detail with both experimental and computational approaches, separately and in combination. However, conspicuously missing from studies of the mineral-fluid interface are direct measurements of the energies of ion sorption and exchange. The literature on energetics and enthalpies of exchange, adsorption, dissolution, precipitation, and surface protonation reactions, especially those directly supported by experimental data, remains scarce despite their fundamental nature. The overarching goal of this project is to complete a systematic study of the thermodynamics properties of interfacial reactions at four MO surfaces (Rutile (α-TiO2), Quartz (SiO2), boehmite (γ-AlOOH) and goethite (α-FeOOH)) through the application and construction of novel flow adsorption microcalorimetry techniques and instrumentations. These unique and specialized microcalorimeters will operate at various temperatures and solution chemical compositions allowing for in-situ measurements across metal oxides and ligands of various characteristics. The overall research goal will be accomplished by completing the following three specific objectives (O): O1) Determine the energetics of surface protonation and deprotonation, ion exchange and ligand sorption reactions; O2) Investigate the surface charge thermodynamic properties under a range of temperature and solution chemical compositions; and O3) Develop predictive trends of the interplay between MO structure, surface coverage and surface reactivity. In addition to key thermodynamics parameters, calorimetric measurements provide a wealth of mechanistic information about reactions energetics and kinetics, surface charge characteristics, and structure-reactivity or selectivity relationships, all obtained in-situ and in real-time. This report includes science highlights from various projects completed over the performance period of the project. Also listed are dissemination opportunities, people supported on the grant and the impact on available physical resources and the discipline as a whole.

58 GEOSCIENCES↗
DOE OSTI2024

Molecular Fe-N 4 Moieties Coupled with Atomic Co-N 4 Sites Toward Improved Oxygen Reduction Performance

Research on high-efficiency and cost-efficient catalysts for oxygen reduction reaction (ORR) is still a vital but challenging issue for commercializing metal–air batteries. Herein, a single-molecule/atom hybrid catalyst is developed to boost the ORR, in which iron phthalocyanine molecules containing molecular Fe-N 4 moieties couple with atomic Co-N 4 sites on the surface of polyhedral carbon. Density functional theory calculations reveal that face-to-face laminated construction of Fe-N 4 and Co-N 4 in the hybrid catalyst can effectively modulate the electronic structure of active iron atoms and reduce the energy barrier of the rate-determining step for ORR. As a result, this hybrid catalyst demonstrates excellent ORR performance, featuring a half-wave potential of 0.904 V, a peak power density of 238.3 mW cm -2 for zinc–air battery, and outstanding electrocatalytic stability. Here, this work offers a distinctive and robust molecular/atomic engineering approach to creating efficient electrocatalysts, advancing the fields of metal–air batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2024

Construction of Reconfigurable and Polymorphic DNA Origami Assemblies with Coiled‐Coil Patches and Patterns

Abstract DNA origami nanodevices achieve programmable structure and tunable mechanical and dynamic properties by leveraging the sequence‐specific interactions of nucleic acids. Previous advances have also established DNA origami as a useful building block to make well‐defined micron‐scale structures through hierarchical self‐assembly, but these efforts have largely leveraged the structural features of DNA origami. The tunable dynamic and mechanical properties also provide an opportunity to make assemblies with adaptive structures and properties. Here the integration of DNA origami hinge nanodevices and coiled‐coil peptides are reported into hybrid reconfigurable assemblies. With the same dynamic device and peptide interaction, it is made multiple higher‐order assemblies (i.e., polymorphic assembly) by organizing clusters of peptides into patches or arranging single peptides into patterns on the surfaces of DNA origami to control the relative orientation of devices. The coiled‐coil interactions are used to construct circular and linear assemblies whose structure and mechanical properties can be modulated with DNA‐based reconfiguration. Reconfiguration of linear assemblies leads to micron scale motions and ≈2.5‐10‐fold increase in bending stiffness. The results provide a foundation for stimulus‐responsive hybrid assemblies that can adapt their structure and properties in response to nucleic acid, peptide, protein, or other triggers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multi-technique characterization of spray coated and roll-to-roll coated gas diffusion fuel cell electrodes

Here this work reports the characterization of catalyst layer (CL) structure and composition for a set of gas-diffusion electrodes (GDEs) fabricated by different coating methods with the goal of developing a better understanding of CL processing-structure-property-performance relationships. The CL coating techniques used in this study were ultrasonic spray coating, and two roll-to-roll (R2R) methods - gravure and slot die. Scanning transmission electron microscopy (STEM) coupled with X-ray energy dispersive spectroscopy (EDS) analysis of GDE cross-sections provided bulk information, while scanning electron microscopy (SEM) with EDS and X-ray photoelectron spectroscopy (XPS) were used to investigate the near surface and surface composition of the CLs that will be in direct contact with the membrane once the membrane electrode assembly (MEA) is constructed. This study demonstrates that common quantification parameters, namely the F:Pt ratio extracted from cross-sectional STEM-EDS maps and top-down CL measurements with SEM and XPS, along with roughness parameters quantified from SEM micrographs are useful parameters in explaining and potentially predicting performance trends. Additionally, it highlights the importance of understanding key CL surface properties for advancing the manufacturability of high-performance components for polymer electrolyte membrane (PEM) technologies.

30 DIRECT ENERGY CONVERSION↗