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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A biokinetic model for systemic sodium

This paper describes an updated biokinetic model for systemic sodium (Na), developed for use in a series of reports by the International Commission on Radiological Protection (ICRP) on occupational intake of radionuclides. In contrast to the ICRP's previous model for intake of radio-sodium by workers, the updated model depicts realistic directions of movement of Na in the body including recycling of activity between blood and tissues. The updated model structure facilitates extension of the baseline transfer coefficients for adults to different age groups and to special exposure scenarios such as transfer of radio-sodium from the mother to the foetus or the nursing infant. Dose coefficients for 22Na and 24Na based on the updated model generally do not differ greatly from those based on the ICRP's previous Na model when both models are connected to the ICRP's latest dosimetry system. The main exception is that the updated model yields roughly twofold higher dose coefficients for endosteal bone surface than does the previous model due to the dosimetrically cautious assumption in the updated model that exchangeable Na in bone resides on bone surface.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO 2 Conversion Reagents in Metal–Organic Frameworks

Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO 2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO 2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.

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

Aqueous ion partitioning in Nafion: Applicability of Manning's counter-ion condensation theory

Nafion is a well-studied polymer of significant interest as a fuel cell membrane due to rapid transport of protons, which is often ascribed, at least in part, to its phase-separated morphology. The literature detailing ion sorption and transport in Nafion diverges considerably from that of water purification membranes, with contrasting analyses and models. To help bridge these gaps and facilitate comparisons of membranes with various structures, this study presents measurements of ion activity coefficients and sorption in Nafion 117 equilibrated with aqueous solutions of NaCl, MgCl 2 , and Na 2 SO 4 , which are relevant to water purification. The experimental data are compared to a theoretical framework based on Manning's counter-ion condensation theory and Donnan theory. Good agreement between the model and data is observed for NaCl and MgCl 2 , using no adjustable parameters. However, the model significantly underpredicts Na 2 SO 4 solubility, which is attributed to ion pairing effects. Furthermore, the results highlight the general applicability of counter-ion condensation theory to highly charged polymers, including those exhibiting nanoscale ordering, while revealing fundamental limitations related to ion pairing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗
DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗
DOE OSTI2021

Strongly temperature dependent ferroelectric switching in AlN, Al 1-x Sc x N, and Al 1-x B x N thin films

This manuscript reports the temperature dependence of ferroelectric switching in Al 0.84 Sc 0.16 N, Al 0.93 B 0.07 N, and AlN thin films. Polarization reversal is demonstrated in all compositions and is strongly temperature dependent. Between room temperature and 300 °C, the coercive field drops by almost 50% in all samples, while there was very small temperature dependence of the remanent polarization value. Furthermore, over this same temperature range, the relative permittivity increased between 5% and 10%. Polarization reversal was confirmed by piezoelectric coefficient analysis and chemical etching. Applying intrinsic/homogeneous switching models produces nonphysical fits, while models based on thermal activation suggest that switching is regulated by a distribution of pinning sites or nucleation barriers with an average activation energy near 28 meV.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-scale computational screening and mechanistic insights of cyclic amines as solvents for improved lignocellulosic biomass processing

A computational screening workflow for the efficient deconstruction of cellulose, lignin and hemicellulose fractions of lignocellulosic biomass using cyclic amines as solvents. Lignocellulosic biomass is a promising feedstock for production of affordable fuels and chemicals from renewable resources. Effective solubilization and subsequent deconstruction of its cellulose, hemicellulose, and lignin fractions is essential for the viability of future biorefineries. This study used quantum chemistry-based equilibrium thermodynamics methods to evaluate the potential of 650 cyclic amines to solubilize cellulose, hemicellulose, and lignin. The activity coefficients of solvent - biopolymer interactions were predicted using the COSMO-RS (COnductor-like Screening MOdel for Real Solvents) method and used to identify cyclic amines that can efficiently dissolve and extract selective fractions of biopolymers during biomass pretreatment. Among the 650 cyclic amines, 1-piperazineethanmaine was predicted to be an effective solvent for extracting all three polymers and was experimentally shown to achieve the highest lignin removal (97.1%). Non-covalent interaction, reduced density gradient and quantum chemical calculations were performed to elucidate the dissolution mechanism of lignin, cellulose and hemicellulose and gain further molecular level insights into the interactions between the cyclic amines and biomass polymers that promote efficient solubilization and extraction. These analyses indicated that 1-piperazineethanmaine and 1-methylimidazole make noncovalent van der Waals, electrostatic interactions and hydrogen bonding with lignin, leading to enhanced lignin removal, while the strong intramolecular hydrogen bonding interactions in cellulose and hemicellulose result in weaker solvent-biopolymer interactions. Overall, the computational approach provided an efficient method for identifying cyclic amines tailored for optimal biomass pretreatment and resulted in the identification of a potential new class of solvents for effective biomass pretreatment.

Kumar, Nikhil↗
DOE OSTI2020

Validation of the IRI-2016 model with Indian NavIC data for future navigation applications

The position accuracy of Navigation with Indian Constellation (NavIC) system is affected by several sources of errors. Among them, the Ionospheric Time Delay (ITD) error is the most predominant one which depends upon the total electron content (TEC) present in the ionosphere. The ITD variations are more intense over low latitude regions due to the equatorial anomaly effects. Hence, modelling of ITD error is necessary. The International Reference Ionosphere (IRI)-2016 model is one of the standard global ionospheric models to estimate the Vertical TEC (VTEC). This paper discusses about the VTEC deviations due to the IRI-2016 model over low latitude Hyderabad station, Indian region using NavIC, Global Positioning System and Global Navigation Satellite System signals at corresponding Ionospheric Pierce Point latitude and longitudes for all months and various Kp indices during the low solar activity year 2017. In this work, cross correlation coefficient, the metric norm (L2N), Symmetric Kullbacke Leibler Distance metrics are used to evaluate the performance of IRI-2016 model TEC with NavIC, GPS and GLONASS data. From the results, it is found that TEC predicted by the IRI-2016 model produced smaller estimation errors with NavIC data over Indian region. The obtained results will be helpful for future updates of IRI model.

42 ENGINEERING↗
DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modeling Europium (II/III) ion solvation in the LiCl-KCl eutectic mixture with polarizable force fields

Here, the solvation processes of Europium (II/III) ions within the molten salt eutectic mixture, 3LiCl-2KCl, are investigated over temperatures ranging from 673 K to 1173 K. New polarizable ion force fields are proposed to model Europium ions in a molten salt eutectic mixture with a goal of accurately capturing the underlying physics in the solutions. In contrast to rigid-ion models, the polarizable Drude model with an adjusted WBK (Wang-Buckingham) force field significantly improves predictions for diffusion coefficients, the average diffusion activation energy, and changes in excess ion chemical potential and partial molar entropy, producing good agreement with experimental measurements.

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

A comprehensive study of the radiative properties of NO—a first step toward a complete air opacity

Here, in this paper we propose a methodology to calculate the radiative properties of the diatomic molecular constituents of air, and utilize the present approach to model the radiative properties of nitrogen monoxide, NO. We also investigate the important physics involved in calculating accurate radiative quantities for air, such as the Planck and Rosseland mean opacities, and emission and absorption coefficients, as well as the couplings accounted for in rovibrational calculations. Complete active space self-consistent field multi-reference configuration interaction (CAS-MRCI) calculations were performed in order to model the NO X 2 Π, a 4 Π, b 4 Σ - , 1 2 Σ + , 2 2 Σ + , 3 2 Σ + , G 2 Σ - , B' 2 Δ, (C, B) 2 2 Π, (H', L) 3 2 Π, and 1 2 Φ adiabatic states, and calculate the respective molecular data. The γ X 2 Π - A 2 Σ + , ε X 2 Π - D 2 Σ + , β' X 2 Π - B' 2 Δ, '11 000 Å' A 2 Σ + - D 2 Σ + , 'infrared' X 2 Π - X 2 Π and X 2 Π - (C, B) 2 2 Π (δ and β) band systems are investigated in monochromatic spectra calculations, as well as the Ogawa a 4 Π - b 4 Σ - band and several other band systems. Several conclusions are drawn, such as the importance of including the Ogawa band, which has not been included in previous air radiative models or comprehensive line-list calculations, as well as the importance of performing coupled rovibrational line-list calculations in order to accurately calculate the Rosseland means. We also found that the additional band systems modeled here contribute significantly to the total Planck and Rosseland means.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Hall-coefficient diagnostics of the surface state in pressurized SmB 6

In this study, we report results of the high-pressure Hall coefficient (R H ) measurements in the putative topological Kondo insulator SmB 6 up to 37 GPa. Below 10 GPa, our data reveal that R H (T) exhibits a prominent peak upon cooling below 20 K. Remarkably, the temperature at which surface conduction dominates coincides with the temperature of the peak in R H (T). The temperature dependent resistance and Hall coefficient can be well fitted by a two-channel model with contributions from the metallic surface and the thermally activated bulk states. When the bulk of SmB 6 becomes metallic and magnetic at ~10 GPa, both the R H (T) peak and the resistance plateau disappear simultaneously. Here, our results indicate that the R H (T) peak is a fingerprint to diagnose the presence of a metallic surface state in SmB 6 . The high-pressure magnetic state of SmB 6 is robust to 180 GPa, and no evidence of superconductivity is observed in the metallic phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Activity model for 36 elements in Fe-Ni-Si-S-C liquids with application to terrestrial planet accretion and mantle geochemistry: New data for Ru, Re, Pt, Os, Ti, Nb, and Ta

Understanding siderophile element partitioning between metal and silicate melts under diverse conditions can be used to place important constraints on the materials and conditions of planetary accretion and core formation, as well as post core formation processes. However, the effects of Si on the partitioning and activity coefficients for these elements are not well known, despite Si likely being one of the dominant light elements in Earth’s core. To address this gap in understanding, we have undertaken a systematic study of the highly siderophile elements Re, Pt, Os, and Ru, and the refractory lithophile elements Nb, Ta and Ti at 1600 °C and 1 GPa, to derive epsilon interaction parameters for these elements in FeSi metallic liquids. Positive epsilon interaction parameters were measured for Nb, Ta, Ti, Ru, Re, Pt, and Os, indicating that dissolved Si in Fe liquids causes a decrease in their metal/silicate partition coefficients (or ‘silicophobic’ behavior). Furthermore, ε$_{Re, Os, or Ru}^{Si}$ > ε$_{Re, Os, or Ru}^{S}$ which means Si causes a larger decrease in D(metal/silicate) than S, and the chalcophile behavior expected from some elements will be completely masked by the presence of Si in a metallic liquid. The new parameters are used to update an activity model that now includes 36 siderophile elements in Fe-Ni-Si-S-C liquids (27 trace elements considered here). Systematic assessment of these 27 elements shows which have the strongest affinity for Si, C, and S, and also how activity coefficients for these elements would vary during accretion and core formation in Earth, Mars, and Mercury of widely differing fO 2 and core compositional conditions. The activity model is combined with new partitioning expressions for Mo, W, Cr, Re Ru, Pt, and Os and applied to aspects of post core formation mantle geochemistry of Earth, Mars, and Mercury. Our updated expressions show that the BSE Mo/W ratio can easily be achieved with metal/silicate partitioning during growth of the Earth, whereas Re, Os and Ru become lower than and highly fractionated compared with BSE values during core formation and accretion, and thus nearly 99% of their BSE abundances are likely contributed by late accretion. Ru isotopes should be a very good indicator of the source material for the late accretion. The high Pt/Os and Re/Os developed in a deepening magma ocean during the growth of the Earth, indicates 186 Os and 187 Os isotopes could be coupled if this ancient material remained isolated and subsequently became entrained in mantle plumes and measured in surficial lavas. The extent to which this occurred will be limited by the low Os content of this ancient material, thus requiring mixing as a major component in plume sources. Martian mantle Hf/W ratio stays low during accretion and core formation modelling, suggesting that W isotope anomalies are more likely due to solid/liquid silicate fractionation than to core formation. Finally, Ti contents measured by MESSENGER at Mercury’s surface can be explained by segregation of either a metallic core (IW-6 to -8) or metallic core + sulfide (IW-4 to -7.5) followed by mantle melting.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

A reversible perovskite air electrode for active and durable oxygen reduction and evolution reactions via the A-site entropy engineering

The performance of reversible solid oxide electrochemical cells (R-SOECs) is largely hindered by the insufficient electroactivity and poor durability of the bifunctional air electrodes, where the oxygen reduction and evolution reactions (ORR and OER) occur. Here, we report our findings in boosting the electrochemical activity and durability of an air electrode with Pr 0.2 Ba 0.2 Sr 0.2 La 0.2 Ca 0.2 CoO 3-δ (PBSLCC) via an A-site entropy engineering. The PBSLCC electrode shows enhanced oxygen reaction activity and excellent durability compared to binary and ternary double perovskites (PrBaCo 2 O 5+δ and Pr 0.8 Ba 0.8 Ca 0.4 Co 2 O 5+δ , respectively). A low and nearly unchanged area-specific resistance of 0.042Ωcm 2 is achieved at 750 °C during the 225-h stability test. La 0.9 Sr 0.1 Ga 0.8 Mg 0.2 O 3-δ electrolyte-supported cells with the PBSLCC air electrode show remarkable performance at 800 °C, demonstrating a peak power density of 1.2Wcm -2 in the fuel cell mode, and a current density of -1.1 Acm -2 at 1.3 V in the electrolysis mode while maintaining the excellent cycling durability of 228 h at ±0.5 Acm -2 under humidified H 2 (10% H 2 O). A bulk oxygen p-band center model is applied to verify that tailoring of the A-site entropy strongly influences the surface exchange coefficients (k* chem ), leading to higher oxygen reaction activity of PBSLCC than the binary and ternary perovskites. Finally, this study opens a new class of high-entropy perovskites for the rational design of air electrodes for R-SOECs with high activity and durability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerated Zymonic Acid Formation from Pyruvic Acid at the Interface of Aqueous Nanodroplets

To explore the role of the liquid interface in mediating reactivity in small compartments, the formation kinetics of zymonic acid (ZA) is measured in submicron aerosols (average radius = 240 nm) using mass spectrometry. The formation of ZA, from a condensation reaction of two pyruvic acid (PA) molecules, proceeds over days in bulk solutions, while in submicron aerosols, it occurs in minutes. The experimental results are replicated in a kinetic model using an apparent interfacial reaction rate coefficient of krxn = (0.9 ± 0.2) × 10-3 M-1 s-1. The simulation reveals that surface activity of PA coupled with an enhanced interfacial reaction rate drives accelerated ZA formation in aerosols. Experimental and simulated results provide compelling evidence that the condensation reaction of PA occurs exclusively at the aerosol interface with a reaction rate coefficient that is enhanced by 4 orders of magnitude (∼104) relative to what is estimated for macroscale solutions.

Kim, Pyeongeun↗
DOE OSTI2025

Cations Enhance Hydride Transfer to Noncatalytic Metals in Concentrated Alkaline Electrolytes

Alkali metal cations are known to influence the kinetics of the hydrogen evolution reaction (HER), acting as either promoters or inhibitors to the rate-determining step depending on the metal surface, local pH, and cation concentration. Despite the importance of concentrated electrolytes for commercial electrochemical cells, the impact of cations on the HER in concentrated alkaline environments (>1 M) and in mixed cation systems remains poorly understood. Here, this study quantifies the HER kinetics at polycrystalline metal surfaces (Pt, Au, Cu, and Fe) and the equilibrium solvation environment in pure and mixed alkali metal hydroxide electrolytes at concentrations up to 3.0 M. Kinetic analyses of Au, Cu, and Fe revealed a positive cation-concentration-effect that was primarily driven by changes to the charge transfer coefficient. Multinuclear NMR spectroscopy examined the solvation of H 2 O/OH – species and the alkali cations as a function of (mixed) alkali cation concentration(s), and demonstrated rapid exchange between solvent, hydroxide, and solvated cations. Together, these findings support models where HER kinetics on noncatalytic metal surfaces in strongly alkaline conditions are primarily governed by the average polarization and polarizability of the metal/solution interface and that increasing cation activity continues to increase the transfer coefficient at metal-hydroxide concentrations up to 3.0 M. Electrolytes and additives which can outcompete weakly hydrated cations and disrupt the interfacial water structure are expected to suppress parasitic HER at electrodes for energy storage and electroplating.

Cations↗