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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A systematic approach to resolve high impedance of multifunctional energy storage composites

Previous work has proposed and characterized the structural and electrical performance of Multifunctional Energy Storage Composite (MESC) structures: structural elements with embedded lithium-ion batteries which were developed by the Structures and Composite Laboratory (SACL) at Stanford University. This work conducts a comprehensive parametric study over the design variables in the manufacturing process to determine the root cause of increased DC impedance compared to commercial pouch cell batteries. Several MESC samples were assembled under various controlled environmental temperatures and pressures. The samples underwent Hybrid Peak Pulse Characterization (HPPC) to approximate the DC impedance as a function of state of charge (SOC). Failure limits of the battery with respect to assembly temperature and pressure were tested, and an MESC manufacturing simulation model was constructed in Abaqus to validate the manufacturing limits. Samples assembled without enough temperature or pressure failed due to a lack of structural integrity or hermetic seal. Conversely, samples which underwent elevated temperatures and pressures resulted in high DC impedance due to nonuniform electrolyte wetting. This work resolves the primary concern of MESC by achieving comparable DC impedance to commercial pouch cells of the same chemistry and testing procedure.

25 ENERGY STORAGE↗
DOE OSTI2022

Snow Albedo Feedbacks Enhance Snow Impurity-Induced Radiative Forcing in the Sierra Nevada

This study employs a fully coupled meteorology-chemistry-snow model to investigate the impacts of light-absorbing particles (LAPs) on snow darkening in the Sierra Nevada. After comprehensive evaluation with spatially and temporally complete satellite retrievals, the model shows that LAPs in snow reduce snow albedo by 0.013 (0-0.045) in the Sierra Nevada during the ablation season (April-July), producing a midday mean radiative forcing (RF) of 4.5 W m -2 which increases to 15-22 W m -2 in July. LAPs in snow accelerate snow aging processes and reduce snow cover fraction, which doubles the albedo change and radiative forcing caused by LAPs. The impurity-induced snow darkening effects decrease snow water equivalent and snow depth by 20 mm and 70 mm in June in Sierra Nevada bighorn sheep habitat. The earlier snowmelt reduces root zone soil moisture by 20%, deteriorating the forage productivity and playing a negative role in the survival of bighorn sheep.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Veggie: Space Vegetables for the International Space Station and Beyond

The Veggie vegetable production system was launched to the International Space Station (ISS) in 2014. Veggie was designed by ORBITEC to be a compact, low mass, low power vegetable production system for astronaut crews. Veggie consists of a light cap containing red, blue, and green LEDs, an extensible transparent bellows, and a baseplate with a root mat reservoir. Seeds are planted in plant pillows, small growing bags that interface with the reservoir. The Veggie technology validation test, VEG-01, was initiated with the first test crop of 'Outredgeous' red romaine lettuce. Prior to flight, lettuce seeds were sanitized and planted in a substrate of arcillite (baked ceramic) mixed with controlled release fertilizer. Upon initiation, astronauts open the packaged plant pillows, install them in the Veggie hardware, and prime the system with water. Operations include plant thinning, watering, and photography. Plants were grown on the ISS for 33 days, harvested, and returned frozen to Earth for analysis. Ground controls were conducted at Kennedy Space Center in controlled environment chambers reproducing ISS conditions of temperature, relative humidity, and CO2. Returned plant samples were analyzed for microbial food safety and chemistry including elements, antioxidants, anthocyanins and phenolics. In addition the entire plant microbiome was sequenced, and returned plant pillows were analyzed via x-ray tomography. Food safety analyses allowed us to gain approvals for future consumption of lettuce by the flight surgeons and the payload safety office. A second crop of lettuce was grown in 2015, and the crew consumed half the produce, with the remainder frozen for later analysis. This growth test was followed by testing of a new crop in Veggie, zinnias. Zinnias were grown to test a longer duration flowering crop in preparation for tests of tomatoes and other fruiting crops in the future. Zinnias were harvested in February. Samples from the second harvest of lettuce and the zinnia harvest are frozen on the ISS and will return with the next cargo return flight. Some challenges occurred in all tests, especially in the area of watering, with plants receiving insufficient or excess water leading to stressed growth. Zinnia plants were also challenged with fungal growth. Initial tests with Veggie have given us great insight into future crop production scenarios as we work to develop regular supplemental salad crop production on ISS and larger food production systems for our journey to Mars. Funding for this research comes from NASA's Space Biology Program.

Food Safety↗
NASA NTRSDate not supplied

SimUAM: A Comprehensive Microsimulation Toolchain to Evaluate the Impact of Urban Air Mobility in Metropolitan Areas

Over the past several years, Urban Air Mobility (UAM) has galvanized enthusiasm from investors and researchers, marrying expertise in aircraft design, transportation, logistics, artificial intelligence, battery chemistry, and broader policymaking. However, two significant questions remain unexplored: (1) What is the value of UAM in a region’s transportation network? and (2) How can UAM be effectively deployed to realize and maximize this value to all stakeholders, including riders and local economies? To adequately understand the value proposition of UAM for metropolitan areas, the authors develop a holistic multi-modal toolchain, SimUAM, to model and simulate UAM and its impacts on travel behavior. This toolchain has several components: (1) Microsimulation Analysis for Network Traffic Assignment (MANTA): A fast, high-fidelity regional-scale traffic microsimulator, (2) VertiSim: Agranular, discrete-event vertiport and pedestrian simulator, (3) Flexible Engine for Fast-time Evaluation of Flight Environments (Fe3): A high-fidelity, trajectory-based aerial microsimulation. SimUAM, rooted in granular, GPU-based microsimulation, models millions of trips and their movements in the street network and in the air, producing interpretable and actionable performance metrics for UAM designs and deployments. Once the ground-air interface is modeled, the authors find that the market for UAM decreases across all network designs relative to models with static assumptions about transfer times. However, significant improvements can be made to balance the demand and optimize the networks for transfer time, likely increasing the number of benefited trips. The modularity, extensibility, and speed of the platform will allow for rapid scenario planning and sensitivity analysis, effectively acting as a detailed performance assessment tool.

urban air mobility↗
DOE OSTI2021

Photo-Switching of Protein Dynamical Collectivity

We examine changes in the picosecond structural dynamics with irreversible photobleaching of red fluorescent proteins (RFP) mCherry, mOrange2 and TagRFP-T. Measurements of the protein dynamical transition using terahertz time-domain spectroscopy show in all cases an increase in the turn-on temperature in the bleached state. The result is surprising given that there is little change in the protein surface, and thus, the solvent dynamics held responsible for the transition should not change. A spectral analysis of the measurements guided by quasiharmonic calculations of the protein absorbance reveals that indeed the solvent dynamical turn-on temperature is independent of the thermal stability/photostate however the protein dynamical turn-on temperature shifts to higher temperatures. This is the first demonstration of switching the protein dynamical turn-on temperature with protein functional state. The observed shift in protein dynamical turn-on temperature relative to the solvent indicates an increase in the required mobile waters necessary for the protein picosecond motions, that is, these motions are more collective. Melting-point measurements reveal that the photobleached state is more thermally stable, and structural analysis of related RFP’s shows that there is an increase in internal water channels as well as a more uniform atomic root mean squared displacement. These observations are consistent with previous suggestions that water channels form with extended light excitation providing O2 access to the chromophore and subsequent fluorescence loss. We report that these same channels increase internal coupling enhancing thermal stability and collectivity of the picosecond protein motions. The terahertz spectroscopic characterization of the protein and solvent dynamical onsets can be applied generally to measure changes in collectivity of protein motions.

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

Modeling dark- and light-induced crystal structures and single-crystal optical absorption spectra of ruthenium-based complexes that undergo SO 2 -linkage photoisomerization

A family of coordination complexes of the type [Ru(SO 2 )(NH 3 ) 4 X] m+ Y n – (m, n = 1 or 2) exhibit optical switching capabilities in their single-crystal states. This striking effect is caused by the light-induced formation of SO 2 -linkage photoisomers, which are metastable if kept at suitably cool temperatures. We modeled the dark- and light-induced states of these large crystalline complexes via plane-wave (PW)- and molecular-orbital (MO)-based density functional theory (DFT) and time-dependent DFT in order to calculate their structural and optical properties; the calculated results are compared with experimental data. We show that the PW-DFT-based periodic models replicate the structural properties of these complexes more effectively than the MO-DFT-based molecular-fragment models, observing only small deviations in key bond lengths relative to the experimentally derived crystal structures. The periodic models were also found to more effectively simulate trends seen in experimental optical absorption spectra, with optical absorbance and coverage of the visible region increasing with the formation of the photoinduced geometries. The contribution of the metastable photoisomeric species more heavily focuses on the lower-energy end of the spectra. Spectra generated from the molecular-fragment models are limited by the geometry of the fragment used and the number of excited-state roots considered in those calculations. In general, periodic models outperform the molecular-fragment models owing to their ability to better appreciate the periodic phenomena that are present in these crystalline materials as opposed to MO approaches, which are finite methods. We thus demonstrate that PW-DFT-based periodic models should be considered as a more than viable method for simulating the optical and electronic properties of these single-crystal optical switches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Halogen Bond Structure and Dynamics from Molecular Simulations

Halogen bonding has emerged as an important noncovalent interaction in a myriad of applications, including drug design, supramolecular assembly, and catalysis. Current understanding of the halogen bond is informed by electronic structure calculations on isolated molecules and/or crystal structures that are not readily trans- ferable to liquids and disordered phases. To address this issue, we present a first-principles simulation-based approach for quantifying halogen bonds in molecular systems rooted in an understanding of nuclei-nuclei and electron-nuclei spatial correlations. We then demonstrate how this approach can be used to quantify the structure and dynamics of halogen bonds in condensed phases, using solid and liquid molecular chlorine as prototypical examples with high concentrations of halogen bonds. We close with a discussion of how the knowledge generated by our first-principles approach may inform the development of classical empirical models, with a consistent representation of halogen bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Elevated Temperature Effects on Protein Turnover Dynamics in Arabidopsis thaliana Seedlings Revealed by 15 N-Stable Isotope Labeling and ProteinTurnover Algorithm

Global warming poses a threat to plant survival, impacting growth and agricultural yield. Protein turnover, a critical regulatory mechanism balancing protein synthesis and degradation, is crucial for the cellular response to environmental changes. We investigated the effects of elevated temperature on proteome dynamics in Arabidopsis thaliana seedlings using 15 N-stable isotope labeling and ultra-performance liquid chromatography-high resolution mass spectrometry, coupled with the ProteinTurnover algorithm. Analyzing different cellular fractions from plants grown under 22 °C and 30 °C growth conditions, we found significant changes in the turnover rates of 571 proteins, with a median 1.4-fold increase, indicating accelerated protein dynamics under thermal stress. Notably, soluble root fraction proteins exhibited smaller turnover changes, suggesting tissue-specific adaptations. Significant turnover alterations occurred with redox signaling, stress response, protein folding, secondary metabolism, and photorespiration, indicating complex responses enhancing plant thermal resilience. Conversely, proteins involved in carbohydrate metabolism and mitochondrial ATP synthesis showed minimal changes, highlighting their stability. This analysis highlights the intricate balance between proteome stability and adaptability, advancing our understanding of plant responses to heat stress and supporting the development of improved thermotolerant crops.

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

Particle Matter Index and Fuel Wall-wetting Relations on Stochastic Pre-ignition

This works explores the effect of the particle matter index (PMI) and aromatic content on fuel wall impingement and associated stochastic pre-ignition (SPI) propensity. Statically significant measurements of SPI rates are directly coupled with laser induced florescence (LIF) measurements of fuel dilution from spray-linear impingement. Literature suggests that PMI is could be correlated with the number of SPI events, but the root cause(s) of PMI and SPI are directly causational or are a predicator of SPI. Three fuels have been used in this study with 3 different PMI and two different aromatic contents. These fuels are direct injected at two different injection timings, an earlier injection timing which targets the piston crown, 310°CA bTDC, and a later injection timing that the linear, 220°CA bTDC start of injection timings (SOI) respectively. The earlier 310 SOI injection increases soot, whereas the later 220°CD SOI targets the linear and increases wall-wetting. The findings of this work highlight that the SPI activity has a weak correlation with the soot promoted by the combustion process and increased PMI. However, SPI activity shows a strong dependency with high levels of oil dilution and aromatic content, suggesting that PMI might be an indicator of fuel chemistry conducive to SPI and the soot form increased PMI fuels is not a strong source of SPI relative to fuel wall wetting.

42 ENGINEERING↗
DOE OSTI2023

Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations

The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. Furthermore, the link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.

Anions↗
DOE OSTI2024

Untargeted metabolite data from a root surface in a rhizobox

Raw data is provided from samples analyzed using separate reverse phase chromatographic methods on a high performance liquid chromatograph with mass spectrometry. These porewater samples were collected from a microdialysis which generated samples along the surface of a growing A. sative root (all_hc). This data was used to answer questions connecting rhizosphere metabolite (putatively identified metabolites, hc_putative_norm) changes over time (root growth) with changes in the surrounding rhizosphere biogeochemistry (DOC, redox, pH). Rhizosphere biogeochemistry values are provided in the hc_putative_norm file as averages over their respective range of time that they were collected at. The hc_putative_norm file also contains all normalized values over only the intensity values collected for putatively identified metabolites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Soil water percolation and nutrient fluxes as a function of topographical, seasonal and soil texture variation in Central Amazonia, Brazil

Abstract Understanding soil water dynamics and transport of nutrients is challenging in tropical rainforests due to the uniqueness of several properties related to soils, vegetation and seasonality that make relating patterns found in temperate environments to tropical sites difficult. We address the need for better edaphic characterization in tropical environments by investigating soil water percolation rates and chemistry across topographic, soil texture and seasonal gradients in a mature tropical rainforest in Central Amazonia, Brazil. We utilized a passive wick flux meter (e.g., drainage lysimeter) to directly measure real‐time percolation fluxes at 60‐cm depth, and to sample a suite of chemical species across plateau, slope and valley topographic positions. We found percolation flux volume and chemical exports generally increase with decreasing elevation and clay content, which was lowest in the valley. Daily percolation flux was observed to be 2.39 ± 0.44 in plateau, 3.01 ± 0.50 in slope and 6.16 ± 0.83 mm in valley. Most solutes were present in small amounts of <1 mg L −1 , such as PO₄ 3− , Fe 2+ /Fe 3+ and Mn 2+ ; however, NO 3 − concentrations were >20 mg L −1 , even exceeding 100 mg L −1 in the valley. Based on additional isotopic analysis, we speculate high NO 3 − concentrations are partially an artefact of root decomposition following installation of the flux meters. The empirical relationships we show among percolation volume and nutrient exports under varying topographies and soil textures can improve Earth System Model performance by better constraining ecohydrological relationships to nutrient fluxes, which can in‐turn better illuminate the important factors that govern their behaviour.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Local and global approaches to treat the torsional barriers of 4-methylacetophenone using microwave spectroscopy

The Fourier transform microwave spectrum of 4-methylacetophenone recorded from 8 GHz to 18 GHz under jet-cooled conditions has revealed large tunneling splittings arising from a low barrier to internal rotation of the ring methyl group and small splittings from a high torsional barrier of the acetyl methyl group. The large splittings are especially challenging to model, while the small splittings are difficult to analyze due to the resolution limit of 120 kHz. The combination of two methyl groups undergoing internal rotations caused each rotational transition to split into five torsional species, which were resolved and fitted using a modified version of the XIAM code and the newly devel- oped ntop code to a root-mean-square deviation close to measurement accuracy, providing an estimate of the V 3 potential barriers of about 22 cm -1 and 584–588 cm -1 for the ring and the acetyl methyl groups, respectively. The assignment was aided by separately fitting the five torsional species using odd-power order operators. Only one conformer in which all heavy atoms are located on a symmetry plane could be identified in the spectrum, in agreement with results from conformation analysis using quantum chemical calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Tracking Excited States in Wave Function Optimization Using Density Matrices and Variational Principles

We introduce an approach for finding individual excited states' energy stationary points in complete active space self-consistent field theory that is compatible with standard optimization methods and highly effective at overcoming difficulties due to root flipping and near-degeneracies. Inspired by both the maximum overlap method and recent progress in excited-state variational principles, our approach combines these ideas in order to track individual excited states throughout the orbital optimization process. In a series of tests involving root flipping, near-degeneracies, charge transfers, and double excitations, we show that this approach is more effective for state-specific optimization than either the naive selection of roots on the basis of energy ordering or a more direct generalization of the maximum overlap method. We offer evidence that this state-specific approach improves the performance of complete active space perturbation theory for vertical excitation energies. Furthermore, we find that the state-specific optimization can help avoid state-averaging-induced discontinuities on potential energy surfaces. With a simple implementation, a low cost, and compatibility with large active space methods, the approach is designed to be useful in a wide range of excited-state investigations.

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

Computer-aided design of stability enhanced nicotinamide cofactor biomimetics for cell-free biocatalysis

Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.

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

Data for "Enhancing Lipid Production in Plant Cells through Automated High-Throughput Genome Engineering and Phenotyping"

Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.

AI/ML↗
DOE OSTI2021

Fourier-like Thermal Relaxation of Nanoscale Explosive Hot Spots

Hot spots are local regions of high temperature that are widely considered to govern explosive initiation. Hot spot dynamics rests on a delicate balance between heat generation due to chemical reactions and heat loss through thermal conduction, making accurate determinations of the conductivity under extreme conditions a key component of predictive explosive models. We develop here an approach to directly determine the thermal transport properties of explosive hot spots with realistic initial structures through a combination of molecular dynamics (MD) and diffusive heat equation (HEq) modeling. Effective thermal conductivity values are determined by fitting HEq models to MD predictions of long timescale hot spot relaxation. The approach is applied to model hot spots in the molecular crystalline explosive 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) for a range of shock strengths and two limiting cases for impact orientation. Isotropic and anisotropic HEq models yield similar results, despite TATB exhibiting some of the largest and most anisotropic thermal conductivity values for explosive near normal conditions. The conductivity is found to be a strong function of density, which parametrically captures dependence on temperature, pressure, and material state. The associated root-mean-square errors of the fitted HEq models are approximately 5% of MD predicted final equilibrium temperatures. Here, the conductivity values determined here for TATB hot spots are considerably larger than those used in a prior hot spot criticality study, which may significantly impact predictions for critical hot spot sizes. The approach provides a convenient foundation for determining the effective thermal conductivity for hot spot problems in other explosives and directly yields information on reasonable approximations that might be taken in higher-level models for those materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Origin of Selective Production of Hydrogen Peroxide by Electrochemical Oxygen Reduction

Oxygen reduction reaction (ORR) is one of the most important electrochemical reactions. Starting from a common reaction intermediate *–O–OH, the ORR splits into two pathways, either producing hydrogen peroxide (H 2 O 2 ) by breaking the *–O bond or leading to water formation by breaking the O–OH bond. However, it is puzzling why many catalysts, despite the strong thermodynamic preference for the O–OH breaking, exhibit high selectivity for hydrogen peroxide. Moreover, the selectivity is dependent on the potential and pH, which remain not understood. Here, for this work, we develop an advanced first-principles model for effective calculation of the electrochemical reaction kinetics at the solid–water interface, which were not accessible by conventional models. Using this model to study representative catalysts for H 2 O 2 production, we find that breaking the O–OH bond can have a higher energy barrier than breaking *–O, due to the rigidity of the O–OH bond. Importantly, we reveal that the selectivity dependence on potential and pH is rooted into the proton affinity to the former/later O in *–O–OH. For single cobalt atom catalyst, decreasing potential promotes proton adsorption to the former O, thereby increasing the H 2 O 2 selectivity. In contrast, for the carbon catalyst, the proton prefers the latter O, resulting in a lower H 2 O 2 selectivity in acid condition. These findings explain the experiments and highlight the kinetic origins of the selectivity. Our work improves the understanding of ORR by uncovering the proton affinity as a new factor and provides a new model to effectively simulate the atomic-level kinetics of heterogeneous electrochemistry.

08 HYDROGEN↗