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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Synergistic Evolution of Segmental Motion and Cooperative Relaxation within the Amorphous Phase of Organic Mixed Ionic-Electronic Conductors

Organic mixed ionic-electronic conductors (OMIECs) facilitate a variety of electrochemical processes and feature a heterogeneous microstructure composed of both crystalline and amorphous phases. However, structural evolution in amorphous regions during electrochemical doping remains poorly understood, limiting our understanding of mixed conduction mechanisms. Here, in this work, we develop operando chip calorimetry to probe amorphous phase evolution in poly­(3,4-ethylenedioxythiophene):poly­(styrenesulfonate) (PEDOT:PSS) under swelling and electrochemical (de)­doping. Our results reveal that amorphous regions providing ionic transport pathways and those within electronic transport channels undergo heterogeneous, yet synergistic, evolution during electrochemical modulation. The cooperative interplay between segmental motion and relaxation maintains ionic conductivity and intergrain electronic transport upon electrowetting and doping, while facilitating efficient ion hopping and adaptable chain conformations during dedoping. Such synergies are more pronounced in loose structures featuring a fibrillar morphology, which exhibit lower glass transition temperatures (T g ) and higher fragility (m). High-throughput robotic screening further establishes a strong correlation between elevated m/T g ratios and enhanced mixed conduction. These findings elucidate the role of the amorphous phase in the synthesis of OMIECs and underscore the potential of operando chip calorimetry in uncovering structure–property relationships in electroactive polymers.

amorphous phase↗
DOE OSTI2022

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Chemical looping based ammonia production - A promising pathway for production of the noncarbon fuel

Ammonia, primarily made with Haber–Bosch process developed in 1909 and winning two Nobel prizes, is a promising noncarbon fuel for preventing global warming of 1.5 °C above pre-industrial levels. However, the undesired characteristics of the process, including high carbon footprint, necessitate alternative ammonia synthesis methods, and among them is chemical looping ammonia production (CLAP) that uses nitrogen carrier materials and operates at atmospheric pressure with high product selectivity and energy efficiency. To date, neither a systematic review nor a perspective in nitrogen carriers and CLAP has been reported in the critical area. So, this work not only assesses the previous results of CLAP but also provides perspectives towards the future of CLAP. It classifies, characterizes, and holistically analyzes the fundamentally different CLAP pathways and discusses the ways of further improving the CLAP performance with the assistance of plasma technology and artificial intelligence (AI).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Effective removal of trace-level toxic metals from flue gas desulfurization wastewater using SiO 2 supported hydrogel sorbent

Flue gas desulfurization (FGD) wastewater generated from coal-fired power plants contain potentially harmful heavy metal pollutants that pose a threat to public health and clean water. In this work, we present a water stable polyethylenimine-n,n’-methylenebisacrylamide (PEI-MBAA) functionalized SiO 2 solid sorbent material (PMS-1.2/1/4) and investigate its metal adsorption kinetics, selectivity, regenerability, and space velocity. The kinetic studies of six of the toxic heavy metals (As, Cd, Cr, Pb, Se, and Hg) prepared with single elements in Milli-Q water showed the effect of chemical bonding and intraparticle mass transfer resistance on the sorption process. The selectivity studies demonstrated the significant adsorption efficiency toward trace-level heavy metals (Se, Cd, U, Al, etc.) from authentic industrial FGD wastewater. Through five consecutive adsorption–desorption cycles with the FGD (uptake)-citrate (release)-based buffer pair, the sorbent showed high heavy metal removal ability and good reusability. The maximum flow rate for the removal of Se from industrial FGD wastewater was determined to be as high as 8 bed volumes/minute of the sorbent bed. Finally, the results demonstrate the PMS-1.2/1.4 sorbent is a promising candidate for the removal of heavy metals from practical aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Critical insights into the steam electrolysis electrode in protonic ceramic cells for hydrogen production

Intermediate-temperature protonic ceramic electrolysis cells (PCECs), which combine the benefits of both lower- and higher-temperature electrolysis, are among the most efficient technologies for the production of green hydrogen. To ensure economic competitiveness and broad adoption, ongoing innovations in cell materials are essential to improve durability and reduce costs. The water oxidation half-reaction at the anode is a key area for improvement as it is a major contributor to performance degradation and efficiency loss in PCECs. Current anode designs, which are largely derived from solid oxide electrolysis cells, fail to address the specific requirements for PCECs under realistic operating conditions. Here, this Perspective highlights the unique challenges faced by PCEC anodes, focusing on the impact of high steam concentrations and the critical role of proton-coupled electron-transfer mechanisms—factors that are absent in solid oxide electrolysis cells. Furthermore, we explore design principles for advancing anodes tailored for PCECs, offering guidance for future research and development in this promising field.

Electrocatalysis↗
DOE OSTI2021

Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts During Natural and Human-made Disaster

Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as "Acute Phase" by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.

Thakur, Gautam Malviya↗
DOE OSTI2021

Shockley-Queisser analysis of the temperature-efficiency correlation of solar cells in the presence of non-radiative heat transfer

An empirical negative T cell − η correlation has been widely applied, without careful justifications, to evaluate the efficiency ( η ) increase of solar cells due to their temperature ( T cell ) decrease. Here, a framework is introduced to simultaneously compute T cell and η . This model confirms the negative T cell − η correlation if the above-bandgap absorptivity ( α I ) is not suppressed in the optimization of the sub-bandgap reflectivity ( ρ II ) or the infrared emissivity, otherwise it may suggest a positive correlation. It further gives a guideline to enhance η of silicon cells under typical conditions: every 1% increase of α I is equivalent to a 57% increase of ρ II .

14 SOLAR ENERGY↗
DOE OSTI2022

Few-Atom Copper Catalyst for the Electrochemical Reduction of CO to Acetate: Synergetic Catalysis between Neighboring Cu Atoms

Single-atom catalysts (SACs) are gaining increasing recognition because of their superior catalytic properties for various reactions. However, the performance of SACs is often limited by the lack of neighboring metal centers to cooperate in catalysis. Herein, a synergetic interaction between neighboring Cu atoms of a few-atom catalyst (FAC) on graphdiyne is found to greatly enhance the production of acetate in CO electroreduction reaction relative to Cu SACs. In a 1.0 M KOH electrolyte, this Cu FAC exhibits an acetate Faradaic efficiency of 53.8±1.5 % and an ultrahigh relative purity of up to 97 wt% for liquid products, and excellent stability of over 23 h continuous electrolysis at -0.8 V versus reversible hydrogen electrode (RHE). Theoretical studies suggest that the intersite catalytic communication between two neighboring metal atoms confined in each pore of GDY facilitates the formation of acetic acid through either stepwise hydrogenation of CH 2 CO* or the direct reaction of H 2 O with CH 2 CO*. Our study demonstrates the unprecedented synergetic catalysis of Cu FAC in promoting the selective CO electroreduction toward acetate production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Target Selection and Validation of DESI Emission Line Galaxies

The Dark Energy Spectroscopic Instrument (DESI) will precisely constrain cosmic expansion and the growth of structure by collecting ~40 million extragalactic redshifts across ~80% of cosmic history and one-third of the sky. The Emission Line galaxy (ELG) sample, which will comprise about one-third of all DESI tracers, will be used to probe the universe over the 0.6 < z < 1.6 range, including the 1.1 < z < 1.6 range, which is expected to provide the tightest constraints. We present the target selection for the DESI Survey Validation (SV) and Main Survey ELG samples, which relies on the imaging of the Legacy Surveys. The Main ELG selection consists of a g-band magnitude cut and a (g – r) versus (r – z) color box, while the SV selection explores extensions of the Main selection boundaries. The Main ELG sample is composed of two disjoint subsamples, which have target densities of about 1940 deg –2 and 460 deg –2 , respectively. We first characterize their photometric properties and density variations across the footprint. We then analyze the DESI spectroscopic data that have been obtained from 2020 December to 2021 December in the SV and Main Survey. We establish a preliminary criterion for selecting reliable redshifts, based on the [O ιι] flux measurement, and assess its performance. Using this criterion, we are able to present the spectroscopic efficiency of the Main ELG selection, along with its redshift distribution. We thus demonstrate that the Main selection 1940 deg –2 subsample alone should provide 400 deg –2 and 460 deg –2 reliable redshifts in the 0.6 < z < 1.1 and the 1.1 < z < 1.6 ranges, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Directing High-Efficiency Na Plating with Carbon–Aluminum Junction Interfaces for Anode-Free Na Metal Batteries

Anode-free sodium metal batteries are highly promising for future energy storage but suffer from much faster cycling degradation as they are sensitive to even trace levels of irreversible side reactions. This work focuses on the most practical Al foil current collectors and systematically examined the effect of nano-sized carbon coating on improving the Na plating and stripping stability. We identified that the carbon-Al junction interface generated by carbon coating enabled more uniform Na depositing with lower overpotentials, delivering higher than 99.8% Faradaic efficiencies for a wide range of cycling currents between 0.5 and 3.0 mA cm -2 . This performance is much better than the 96.4% efficiency observed on uncoated Al foils under the same conditions, and was also confirmed under lean electrolyte and freezing electrolyte conditions, and can be attributed to the stronger interfacial binding and enhanced sodiophilic properties of the carbon-aluminum junction sites. Furthermore, these sites not only ensure uniform Na plating but also eliminates side reactions that would otherwise cause electrolyte depletion. As a result, Na-metal free full cells assembled with high capacity Na 3 V 2 (PO 4 ) 3 cathode delivered ~ 85% capacity retention for 100 cycles, higher than the 73% of retention of uncoated Al foil.

25 ENERGY STORAGE↗
DOE OSTI2021

Violating Kirchhoff’s Law of Thermal Radiation in Semitransparent Structures

Kirchhoff’s law of thermal radiation imposes a constraint on photon-based energy harvesting processes since part of the incident energy flux is inevitably emitted back to the source. By breaking the reciprocity of the system, it is possible to overcome this restriction and improve the efficiency of energy harvesting. Here, we design and analyze a semitransparent emitter that fully absorbs normally incident energy from a given direction with zero backward and unity forward emissivity. The nearly ideal performance with wavelength-scale thickness is achieved due to the magneto-optical effect and the guided-mode resonance engineered in the emitter structure. We derive the general requirements for the nonreciprocal emitter using the temporal coupled mode theory and the symmetry considerations. As a result, we provide a realistic emitter design based on a photonic crystal slab consisting of a magnetic Weyl semimetal and silicon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Retarded Charge-Carrier Recombination in Photoelectrochemical Cells from Plasmon-Induced Resonance Energy Transfer

N-type metal oxides such as hematite (α-Fe 2 O 3 ) and bismuth vanadate (BiVO 4 ) are promising candidate materials for efficient photoelectrochemical water splitting; however, their short minority carrier diffusion length and restricted carrier lifetime result in undesired rapid charge recombination. In this paper, a 2D arranged globular Au nanosphere (NS) monolayer array with a highly ordered hexagonal hole pattern (hereafter, Au array) is introduced onto the surface of photoanodes comprised of metal oxide films via a facile drying and transfer-printing process. Through plasmon-induced resonance energy transfer, the Au array provides a strong electromagnetic field in the near-surface area of the metal oxide film. The near-field coupling interaction and amplification of the electromagnetic field suppress the charge recombination with long-lived photogenerated holes and simultaneously enhance the light harvesting and charge transfer efficiencies. Consequently, an over 3.3-fold higher photocurrent density at 1.23 V versus reversible hydrogen electrode (RHE) is achieved for the Au array/α-Fe 2 O 3 . Furthermore, the high versatility of this transfer printing of Au arrays is demonstrated by introducing it on the molybdenum-doped BiVO 4 film, resulting in 1.5-fold higher photocurrent density at 1.23 V versus RHE. The tailored metal film design can provide a potential strategy for the versatile application in various light-mediated energy conversion and optoelectronic devices.

2D pattern array↗
DOE OSTI2022

Hybrid particle-in-cell simulations of electromagnetic coupling and waves from streaming burst debris

Various systems can be modeled as a point-like explosion of ionized debris into a magnetized, collisionless background plasma-including astrophysical examples, active experiments in space, and laser-driven laboratory experiments. Debris streaming from the explosion parallel to the magnetic field may drive multiple resonant and non-resonant ion-ion beam instabilities, some of which can efficiently couple the debris energy to the background and may even support the formation of shocks. We present a large-scale hybrid (kinetic ions + fluid electrons) particle-in-cell simulation, extending hundreds of ion inertial lengths from a 3D explosion, that resolves these instabilities. We show that the character of these instabilities differs notably from the 1D equivalent by the presence of unique transverse structure. Additional 2D simulations explore how the debris beam length, width, density, and speed affect debris-background coupling, with implications for the generation of quasi-parallel shocks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Semi-Supervised Disaggregation of Load Profiles at Transmission Buses with Significant Behind-the-Meter Solar Generations

It is of imperative interests for regional transmission organizations (RTOs) to effectively extract daily load profiles at transmission buses, which remains a gap in existing technology paradigm. This digest proposes an explicit yet efficient linear estimator, to disaggregate metered load profiles at buses with significant behind-the-meter (BTM) solar generations in a data driven manner. The proposed estimator is based on utility zonal load profiles and proxy solar irradiance profiles, which in reality is the aggregated waveform at each transmission bus and equivalent to the mix of summed load profiles minus actual BTM solar generation. To overcome technical challenges in the lack of “ground truth” and validate the performance of supervised learning algorithms, we propose semi-supervised mechanisms with parameter tuning, and leverage the unique characteristics of zero-crossing points in BTM solar peaking behaviors.

machine leaning, behind-the-meter, power grid anal↗
DOE OSTI2024

Advancements in Direct Air Capture: Unveiling a Simple and Robust Synthesized Fibrous Amine-functionalized Matrix (FAM) Sorbent for Commercial Scale-up

The escalating challenge of climate change necessitates innovative solutions in the realm of carbon management, particularly in mitigating the impact of fossil fuel emissions. Direct Air Capture (DAC) technology emerges as a critical component within the spectrum of Carbon Capture and Sequestration (CCS) solutions, offering the distinct advantage of directly removing CO2 from the atmosphere irrespective of the source. This attribute grants DAC systems unparalleled flexibility in deployment location sand the potential to make substantial contributions to lowing atmospheric CO2 levels. The success of DAC technologies significantly depends on the development of efficient, economical sorbent capable of selective and durable CO2 capture from ambient air. Recent advancements in material science have led to the exploration of amine-functionalized sorbents, hollow fiber sorbents and membranes, and other novel materials designed to meet these criteria. This study introduces a significant advancement in DAC technology with the development of a Fibrous Amine-functionalized Matrix (FAM) sorbent. The FAM sorbent distinguished itself through its mechanical robustness, a streamlined synthesis process, and the capability for low-temperature regeneration. Its exceptional adsorption-desorption kinetics enable swift CO2 capture and release, crucial for the viability of DAC on a commercial scale.

Wang, Qiuming↗