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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Lightning Jump Algorithm Development for the GOES·R Geostationary Lightning Mapper

Current work on the lightning jump algorithm to be used in GOES‐R Geostationary Lightning Mapper (GLM)'s data stream is multifaceted due to the intricate interplay between the storm tracking, GLM proxy data, and the performance of the lightning jump itself. This work outlines the progress of the last year, where analysis and performance of the lightning jump algorithm with automated storm tracking and GLM proxy data were assessed using over 700 storms from North Alabama. The cases analyzed coincide with previous semi‐objective work performed using total lightning mapping array (LMA) measurements in Schultz et al. (2011). Analysis shows that key components of the algorithm (flash rate and sigma thresholds) have the greatest influence on the performance of the algorithm when validating using severe storm reports. Automated objective analysis using the GLM proxy data has shown probability of detection (POD) values around 60% with false alarm rates (FAR) around 73% using similar methodology to Schultz et al. (2011). However, when applying verification methods similar to those employed by the National Weather Service, POD values increase slightly (69%) and FAR values decrease (63%). The relationship between storm tracking and lightning jump has also been tested in a real‐time framework at NSSL. This system includes fully automated tracking by radar alone, real‐time LMA and radar observations and the lightning jump. Results indicate that the POD is strong at 65%. However, the FAR is significantly higher than in Schultz et al. (2011) (50‐80% depending on various tracking/lightning jump parameters) when using storm reports for verification. Given known issues with Storm Data, the performance of the real‐time jump algorithm is also being tested with high density radar and surface observations from the NSSL Severe Hazards Analysis & Verification Experiment (SHAVE).

Schultz. E.↗
NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗
DOE OSTI2026

Infrared-enhanced Photometric Redshifts for the Dark Energy Survey Y6 Gold catalogue

The Dark Energy Survey (DES) provides optical data across 5000 square degrees of the southern sky, enabling a broad range of extragalactic and cosmological studies. Combining DES data with infrared surveys offers the opportunity to improve its photometric redshift (photo-z) estimates. We aim to investigate improvements in photometric redshift estimation achieved by combining DES optical data with infrared measurements from the VISTA Hemisphere Survey (VHS) and the Wide-field Infrared Survey Explorer (WISE), and release an updated version of the catalogue. We performed a positional sky cross-match between the DES Y6 Gold catalogue matched to a spectroscopic dataset, the 2013 AllWISE Data Release, and VHS Data Release 5, in order to test these improvements using the Directional Neighbourhood Fitting (DNF) algorithm (Y6 Gold catalogue reference estimator). We additionally matched it to the unWISE catalogue to verify the performance against this deeper dataset. Adding infrared data reduces all the metrics (scatter, bias and outlier fraction) in photo-z estimates, particularly at higher redshifts in comparison with only using optical data from DES. The obtained results are globally better for the DES+WISE sample, with improvements that are statistically significant. On the other hand, the addition of the VHS bands to available depth is only marginal. The combined use of DES and WISE W1 and W2 data improves the photometric redshift metrics analysed here. The addition of VHS data at the DES and VHS depths explored here does not provide any further improvement at z less than 1.5, indicating that, under these constraints, WISE data may already capture the key infrared features and depth needed for accurate photo-z estimation. In addition, low signal-to-noise (less than 10) infrared data does not contribute to any improvement beyond the DES optical dataset.

Puebla, M. M. [Madrid U.; Madrid, CIEMAT]↗
NASA NTRS2016

Machine-z: Rapid Machine-Learned Redshift Indicator for Swift Gamma-Ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce 'machine-z', a redshift prediction algorithm and a 'high-z' classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

gamma-ray burst: general↗
DOE OSTI2024

Utilizing waste heat in wastewater treatment plants for water desalination: Modeling and Multi-Objective optimization of a Multi-Effect desalination system using Decision Tree Regression and Pelican optimization algorithm

This paper examines the feasibility of using waste heat from wastewater treatment plants (WWTPs) for water desalination. A model was developed to utilize waste heat from the gensets at As Samra WWTP in Jordan, using real data and TRNSYS® software to calculate available waste heat. The desalination process was then modeled with ASPEN PLUS® software, focusing on multi-effect desalination (MED). Both series and parallel configurations for the MED system were compared. The study investigated the effects of system feeding flow rate, feeding pressure, and heat input on productivity, performance ratio, and recovery ratio. The study also introduces a novel optimization technique combining machine learning and modern optimization algorithms to maximize system productivity and performance. Initially, a decision tree regression (DTR) model is developed to establish relationships between key independent variables (flow rate, feed pressure, and heat input) and dependent variables (productivity, performance ratio, and recovery ratio). The Pelican Optimization Algorithm (POA) is then used to identify the optimal values of the independent variables for maximum productivity and performance. The results show that using a series configuration yields a system productivity of 3984.2 kg/hr, a performance ratio of 3.78, and a recovery ratio of 0.991 at a feed flow rate of 4000 kg/hr, feed pressure of 3 bars, and heat input of 719 kW. Optimal productivity (4421 kg/hr), performance ratio (3.81), and recovery ratio (0.851) are achieved at a feed flow rate of 5166 kg/hr, feed pressure of 3.2 bars, and heat input of 794 kW. In conclusion, the techno-economic assessment indicates a levelized cost of water of 1.63 USD/m 3 for parallel configurations and 1.65 USD/m 3 for series configurations, with a payback period of less than two years.

42 ENGINEERING↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dehydration kinetics of nanoconfined water in beryl probed by high temperature single crystal synchrotron X-ray diffraction

Understanding changes in material properties through external stimuli plays a key role in validating the expected performance of materials and engineering material properties in a controlled manner. Here, we introduce a fundamental protocol to deduce dehydration reactions kinetics of water confined in nanopore channels, with the cyclosilicate beryl as the scaffold of interest, using time-resolved synchrotron X-ray diffraction (SXRD), in the temperature interval of 298–1038 K. The temperature-dependent intensity (I) of the strongest reflection (112) was used as the crystallite variable. An estimation of an isobaric thermal crystallite coefficient, k, analogous with the isobaric thermal expansion coefficient, established the rate of relative crystallization as a function of temperature, $\frac{∂I}{∂T}$. A plot of lnk and $\frac{1}{T}$ gives rise to two kinetic steps, indicating a slow dehydration stage up to ~ 700 K and a fast dehydration stage up to the investigated temperature 1038 K. The crystal structure of beryl determined up to 1038 K, in temperature increment as small as 10 K, indicates the presence of channel ions Na and Fe and a gradual decrease of water upon heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Development of a Bio-nanobattery for Distributed Power Storage Systems

Currently available power storage systems, such as those used to supply power to microelectronic devices, typically consist of a single centralized canister and a series of wires to supply electrical power to where it is needed in a circuit. As the size of electrical circuits and components become smaller, there exists a need for a distributed power system to reduce Joule heating, wiring, and to allow autonomous operation of the various functions performed by the circuit. Our research is being conducted to develop a bio-nanobattery using ferritins reconstituted with both an iron core (Fe-ferritin) and a cobalt core (Co-ferritin). Both Co-ferritin and Fe-ferritin were synthesized and characterized as candidates for the bio-nanobattery. The reducing capability was determined as well as the half-cell electrical potentials, indicating an electrical output of nearly 0.5 V for the battery cell. Ferritins having other metallic cores are also being investigated, in order to increase the overall electrical output. Two dimensional ferritin arrays were also produced on various substrates, demonstrating the necessary building blocks for the bio-nanobattery. The bio-nanobattery will play a key role in moving to a distributed power storage system for electronic applications.

King, Glen C.↗
NASA NTRS2009

Performance of Trajectory Models with Wind Uncertainty

Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty.

Lee, Alan G.↗
DOE OSTI2023

A single oscillating proto-hypothalamic neuron gates taxis behavior in the primitive chordate Ciona

Ciona larvae display a number of behaviors, including negative phototaxis. In negative phototaxis, the larvae first perform short spontaneous rhythmic casting swims. As larvae cast in a light field, their photoreceptors are directionally shaded by an associated pigment cell, providing a phototactic cue. This then evokes an extended negative taxis swim. We report here that the larval forebrain of Ciona has a previously uncharacterized single slow-oscillating inhibitory neuron (neuron cor-assBVIN78) that projects to the midbrain, where it targets key interneurons of the phototaxis circuit known as the photoreceptor relay neurons. The anatomical location, gene expression and oscillation of cor-assBVIN78 suggest homology to oscillating neurons of the vertebrate hypothalamus. Ablation of cor-assBVIN78 results in larvae showing extended phototaxis-like swims, but which occur in the absence of phototactic cues. These results indicate that cor-assBVIN78 has a gating activity on phototaxis by projecting temporally-oscillating inhibition to the photoreceptor relay neurons. However, in intact larvae the frequency of cor-assBVIN78 oscillation does not match that of the rhythmic spontaneous swims, indicating that the troughs in oscillations do not themselves initiate swims, but rather that cor-assBVIN78 may modulate the phototaxis circuit by filtering out low level inputs while restricting them temporally to the troughs in inhibition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Titanium isotope systematics of refractory inclusions: Echoes of molecular cloud heterogeneity

Calcium-aluminum-rich inclusions (CAIs) are highly refractory objects found in different chondrite groups and represent some of the oldest known solids of the Solar System. As such, CAIs provide key information regarding the conditions prevailing in the solar protoplanetary disk as well as subsequent mixing and transport processes. Many studies have investigated CAIs for their isotopic compositions and reported nucleosynthetic isotope anomalies in numerous elements, which are typically explained by the variable incorporation of isotopically highly anomalous presolar phases. However, with the exception of 54 Cr-enriched nanospinels, the exact presolar phases responsible for the isotopic heterogeneities are yet to be identified. To address this issue, we here present in-situ Ti isotopic analyses obtained on a diverse set of CAIs from various CV3 chondrites. In this work, the in-situ measurements were performed by targeting individual mineral phases of 15 CAIs with laser-ablation mass spectrometry and indicate significant inter- and intra-CAI isotopic heterogeneity in the neutron-rich isotope 50 Ti. This is particularly pronounced for primitive fine-grained CAIs, whereas coarse-grained CAIs, which have been subject to melting, exhibit smaller degrees of Ti isotopic heterogeneity. To further investigate this Ti isotopic heterogeneity, we additionally obtained Ti isotopic compositions of sequential acid leachates from two fine-grained and two coarse-grained CAIs derived from CV3 chondrites. In contrast to potential expectations from the first part of the study, we do not observe any significant intra-CAI Ti isotopic heterogeneity between the different leaching steps. The lack of intra-CAI Ti isotopic heterogeneity in the acid leachate samples of this study likely reflects that the leaching procedure is unable to efficiently separate the carriers of isotopically anomalous Ti in CAIs. By comparing the bulk CAI Ti isotope compositions with Ti isotope data for hibonite-rich objects from the literature, we find that the range of Ti isotope compositions recorded by CAIs from various chondrite groups can be accounted for by the averaging of hibonite grains. In turn, the variable Ti isotope compositions of hibonite grains can be explained by the averaging of isotopically diverse presolar grains present in the Sun’s parental molecular cloud. This effect of averaging is statistically supported by the central limit theorem, and the concept has the potential to be useful for other isotopic systems.

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

Control of Peptide Amphiphile Supramolecular Nanostructures by Isosteric Replacements

Supramolecular nanostructures with tunable properties can have applications in medicine, pharmacy, and biotechnology. In this work, we show that the self-assembly behavior of peptide amphiphiles (PAs) can be effectively tuned by replacing the carboxylic acids exposed to the aqueous media with isosteres, functionalities that share key physical or chemical properties with another chemical group. Transmission electron microscopy, atomic force microscopy, and small-angle X-ray scattering studies indicated that the nanostructure’s morphologies are responsive to the ionization states of the side chains, which are related to their pK a values. Circular dichroism studies revealed the effect of the isosteres on the internal arrangement of the nanostructures. The interactions between diverse surfaces and the nanostructures and the effect of salt concentration and temperature were assessed to further understand the properties of these self-assembled systems. These results indicate that isosteric replacements allow the pH control of supramolecular morphology by manipulating the pK a of the charged groups located on the nanostructure’s surface. Theoretical studies were performed to understand the morphological transitions that the nanostructures underwent in response to pH changes, suggesting that the transitions result from alterations in the Coulomb forces between PA molecules. Furthermore, this work provides a strategy for designing biomaterials that can maintain or change behaviors based on the pH differences found within cells and tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Performance and Reliability Assessment of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Data Advisor (ADA)

The Atmospheric Radiation Measurement (ARM) User Facility provides one of the world's largest openly accessible repositories of atmospheric observations through the ARM Data Discovery platform. Although the repository contains more than three decades of measurements collected from permanent observatories, mobile facilities, aircraft campaigns, and field experiments, identifying appropriate datasets can be challenging, particularly for new users unfamiliar with ARM instrumentation and datastream organization. To improve data accessibility, the ARM Data Center developed the ARM Data Advisor (ADA), an artificial intelligence-powered assistant designed to facilitate scientific data discovery, dataset interpretation, and user guidance. This report evaluates ADA's performance as a domain-specific scientific assistant using realistic atmospheric science workflows. The evaluation examines five key capabilities: data retrieval and curation efficiency, hallucination resistance, scientific reasoning, response to ambiguous queries, and content retention and session continuity. Representative prompts were developed to simulate typical interactions between researchers and the ARM Data Discovery platform, and ADA's responses were assessed for retrieval completeness, scientific accuracy, consistency, and practical usefulness. In these representative tests, ADA reduced the complexity of discovering and accessing ARM datasets by recommending appropriate datastreams, explaining instrumentation, interpreting metadata, and assisting with data processing workflows. ADA also exhibits strong domain knowledge of atmospheric science terminology and generally resists hallucination by acknowledging unavailable datasets and requesting clarification when appropriate. Overall, the results indicate that ADA represents a promising advancement in scientific data discovery within the ARM User Facility and has considerable potential to improve researcher productivity, particularly for new users and interdisciplinary scientists seeking efficient access to ARM observations.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗
DOE OSTI2025

Excitation of whistler and slow-X waves by runaway electrons in a collisional plasma

Runaway electrons are known to provide robust ideal or collisionless kinetic drive for plasma wave instabilities in both the whistler and slow-X branches, via the anomalous Doppler-shifted cyclotron resonances. In a cold and dense post-thermal-quench plasma, collisional damping of the plasma waves can compete with the collisionless drive. Previous studies have found that, due to their higher wavelength and frequency, slow-X waves suffer stronger collisional damping than the whistlers, while the ideal growth rate of slow-X modes is higher. Here, we study runaway avalanche distributions that maintain the same eigen distribution and increase only in magnitude over time. The distributions are computed from the relativistic Fokker–Planck–Boltzmann solver, upon which a linear dispersion analysis is performed to search for the most unstable or least damped slow-X and whistler modes. Taking into account the effect of plasma density, plasma temperature, and effective charge number, we find that the slow-X modes tend to be excited before the whistlers in a runaway current ramp-up. Furthermore, even when the runaway current density is sufficiently high that both branches are excited, the most unstable slow-X mode has a much higher growth rate than the most unstable whistler mode. The qualitative and quantitative trends uncovered in the current study indicate that even though past experiments and modeling efforts have concentrated on whistler modes, there is a compelling case that slow-X modes should also be a key area of focus in the runaway self-mediation through wave instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A General Framework for Error-controlled Unstructured Scientific Data Compression

Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework designed to enhance the compression of floating-point unstructured mesh data, which is common in scientific applications. Our approach involves interpolating mesh data onto a rectilinear grid and then separately compressing the grid interpolation and the interpolation residuals. This method is general, independent of mesh types and typologies, and can be seamlessly integrated with existing lossy compressors for improved performance. We evaluated our framework across twelve variables from two synthetic datasets and two real-world simulation datasets. The results indicate that the multi-component framework consistently outperforms state-of-the-art lossy compressors on unstructured data, achieving, on average, a 2.3 − 3.5× improvement in compression ratios, with error bounds ranging from 1 × 10 the −6 to 1×10−2. We further investigate impact of hyperparameters, such as grid spacing and error allocation, to deliver optimal compression ratios in diverse datasets.

Gong, Qian↗
DOE OSTI2021

Understanding water and energy fluxes in the Amazonia: Lessons from an observation‐model intercomparison

Abstract Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat ( H ), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia—along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%–30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%–15% of incoming solar radiation, compared to 0.15%–0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season ET and associated cooling). These partially compensating model‐observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation ( R n ), the key driver of water and energy fluxes (LE ~ 0.6 R n and H ~ 0.15 R n ), though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation–atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Restrepo‐Coupe, Natalia↗
DOE OSTI2021

Elucidating the Role of Hydroxide Electrolyte on Anion-Exchange-Membrane Water Electrolyzer Performance

Many solid-state devices, especially those requiring anion conduction, often add a supporting electrolyte to enable efficient operation. The prototypical case is that of anion-exchange-membrane water electrolyzers (AEMWEs), where addition of an alkali metal solution improves performance. However, the specific mechanism of this performance improvement is currently unknown. This work investigates the functionality of the alkali metal solution in AEMWEs using experiments and mathematical models. The results show that additional hydroxide plays a key role not only in ohmic resistance of the membrane and catalyst layer but also in the reaction kinetics. The modeling suggests that the added liquid electrolyte creates an additional electrochemical interface with the electrocatalyst that provides ion-transport pathways and distributes product gas bubbles; the total effective electrochemical active surface area in the cell with 1 M KOH is 5 times higher than that of the cell with DI water. In the cell with 1 M KOH, more than 80% of the reaction current is associate with the liquid electrolyte. These results indicate the importance of high pH of electrolyte and catalyst/electrolyte interface in AEMWEs. The understanding of the functionality of the alkali metal solution presented in this study should help guide the design and optimization of AEMWEs.

25 ENERGY STORAGE↗