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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nanoscopic Plugs Block Hydrogen Crossover in Submicron Thick Proton-Conducting SiO 2 Membranes for Water Electrolysis

Zero-gap electrolyzers based on submicron thick proton-conducting oxide membranes (POMs) represent a promising approach to increasing the efficiency of H 2 production from water electrolysis while moving away from conventional perfluorosulfonic acid (PFSA) membranes. A critical barrier to the commercialization of such electrolyzers is that the ultrathin nature of POMs, which is necessary to achieve low cell resistance, makes them more susceptible to defects that can lead to unacceptably high rates of H 2 crossover. Herein, we demonstrate an approach to mitigate this problem through selective deposition of carbon-containing silicon oxide (SiO x C y ) “nanoplugs” into the defects of submicron thick SiO 2 membranes using a facile electrochemically mediated deposition process. Selective deposition of nanoplugs within the defects was verified by multiple characterization techniques, while scanning electrochemical microscopy (SECM) was used to confirm selective plugging of H 2 -crossover hotspots associated with defects at identical locations. Thanks to the use of nanoplugs, the H 2 permeance of 250 nm thick SiO 2 membranes was reduced by 5 to 6 orders of magnitude compared to the unmodified atomic layer deposition (ALD) SiO 2 membranes while having negligible impact on the ionic resistance of the membrane. These plug-modified membranes also enabled safe and stable operation of a zero-gap full cell electrolysis cell, in contrast to cells lacking nanoplugs that produced anode effluent streams having H 2 concentrations near or exceeding the lower flammability limit (LFL) of H 2 . Furthermore, beyond water electrolysis, this defect-sealing strategy has the potential to be broadly implemented in other applications, such as fuel cells and flow batteries, offering a versatile solution to mitigate crossover-related performance losses.

ALD SiO2↗
DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗
DOE OSTI2020

Using operando techniques to understand and design high performance and stable alkaline membrane fuel cells

There is a need to understand the water dynamics of alkaline membrane fuel cells under various operating conditions to create electrodes that enable high performance and stable, long-term operation. Here we show, via operando neutron imaging and operando micro X-ray computed tomography, visualizations of the spatial and temporal distribution of liquid water in operating cells. We provide direct evidence for liquid water accumulation at the anode, which causes severe ionomer swelling and performance loss, as well as cell dryout from undesirably low water content in the cathode. We observe that the operating conditions leading to the highest power density during polarization are not generally the conditions that allow for long-term stable operation. This observation leads to new catalyst layer designs and gas diffusion layers. This study reports alkaline membrane fuel cells that can be operated continuously for over 1000 h at 600 mA cm -2 with voltage decay rate of only 32-μV h -1 – the best-reported durability to date.

25 ENERGY STORAGE↗
DOE OSTI2025

Visible-light H 2 evolution using dye-sensitized TiO 2 : effects of physicochemical properties of TiO 2 on excited carrier dynamics and activity

Dye-sensitized photocatalysts have emerged as promising materials for solar-driven water splitting due to their ability to utilize visible light, in contrast to conventional wide-band-gap semiconductors. However, the relationship between semiconductor properties and charge carrier dynamics remains insufficiently understood. In this study, we investigated Pt/TiO 2 systems sensitized with a visible-light-absorbing Ru(II) polypyridyl complex (RuP), focusing on how the crystal phase and specific surface area of TiO 2 influence excited carrier dynamics and H 2 evolution activity. To isolate the effects of TiO 2 properties, Pt and RuP loadings were standardized across samples. Emission lifetime analysis showed similarly efficient electron injection from RuP to TiO 2 in all cases, suggesting that injection efficiency does not account for observed differences in activity. Transient absorption measurements revealed that back electron transfer (BET) rates depended strongly on the TiO 2 phase, with anatase and P25 exhibiting slower BET and higher activity for H 2 evolution than rutile. The highest apparent quantum yield for H 2 evolution was 12.0% at 450 nm. Among anatase samples, larger surface areas correlated with higher activity, while smaller-area samples exhibited slower BET rates but still low H 2 evolution activity, implying a role for RuP dye–dye interactions in performance loss. This was further supported by improvements in H 2 evolution activity by lowering RuP loading or adding co-adsorbents. Overall, these results demonstrate that both BET suppression and control over RuP dye aggregation are essential for designing efficient dye-sensitized photocatalytic systems.

Harada, Kakeru [Institute of Science Tokyo (Japan)↗
DOE OSTI2026

Uniformity, performance, and durability of roll-to-roll-coated iridium oxide electrolyzer catalyst layers

This work investigates the use of roll-to-roll coating methods for the production of iridium oxide catalyst layers for proton exchange membrane water electrolyzers. Catalyst layers were produced using two coating methods: slot die and gravure. By varying the solids content of the catalyst ink and coating process variables loadings between 0.08 and 0.64 mg Ir cm −2 were prepared with relatively high spatial uniformity. However, at loadings below 0.2 mg Ir cm −2 microscopy reveals voids in the catalyst layer due to similar length scales of catalyst agglomerates and overall layer thickness. Electrochemical testing shows that these voids do not impact initial membrane electrode assembly performance but lead to increased performance losses after potential cycling compared to spray coated catalyst layers.

08 HYDROGEN↗
DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2026

Toward Energy-Efficient HPC: Insights from Power Profiling a Cloud-Resolving Earth System Model

Power is a fundamental constraint as supercomputing advances to exascale. Efficient operation within strict power budgets requires application-aware power management based on a detailed understanding of application-level power behavior. This work analyzes the Energy Exascale Earth System Model (E3SM) atmosphere component, SCREAM, on Perlmutter (NERSC) and Frontier (OLCF). We characterize power variation across inputs, concurrency levels, and power caps, evaluate the energy impact of code optimizations, and attribute energy within the code using a newly developed GPU energy model. Results show that SCREAM’s peak power remains stable during its core execution phase and decreases gradually as concurrency increases. Power capping experiments reveal a performance–energy "sweet spot". On Perlmutter, limiting GPU power to 50% of thermal design power (TDP) achieves up to 15% energy savings with a 7% performance penalty. On Frontier, a 40% TDP cap yields up to 10% energy savings with less than 10% performance loss. Code optimizations reduce SCREAM energy by shortening run time without increasing power. Modeling reveals a critical insight: data movement accounts for approximately 70% of SCREAM’s GPU energy. This fundamentally shifts the optimization focus from FLOPS to data transfer reduction for this class of applications, offering the most impactful strategy for improving energy efficiency. This work establishes a foundation for practical, application-aware power management at exascale.

Zhao, Zhengji [Lawrence Berkeley National Laborato↗
DOE OSTI2023

Recent Advancements in Reconfigurable mmWave Devices Based on Phase-Change and Metal Insulator Transition Materials

Chalcogenide Phase Change Materials (PCM) and metal insulator transition (MIT) materials are a group of materials that are capable of switching between low resistance and high resistance states. These emerging materials have been widely used in optical storage media and memory devices. Over the past recent years, there have been interests in exploiting the PCM and MIT materials, especially germanium antimony telluride (GST) alloys and vanadium dioxide (VO 2 ), for radio frequency (RF) applications. The PCM and MIT-based RF devices are expected to bridge the gap between semiconductor switches and microelectromechanical system (MEMS) switches as they combine the low insertion loss performance of MEMS technology and the small size and reliability performance of semiconductor technology. This article presents an overview of the PCM and MIT materials for RF circuits and discusses the recent advancements in reconfigurable millimeter-wave (mmWave) devices based on PCM and MIT materials in depth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2020

Efficient tandem solar cells with solution-processed perovskite on textured crystalline silicon

Stacking solar cells with decreasing band gaps to form tandems presents the possibility of overcoming the single-junction Shockley-Queisser limit in photovoltaics. The rapid development of solution-processed perovskites has brought perovskite single-junction efficiencies >20%. However, this process has yet to enable monolithic integration with industry-relevant textured crystalline silicon solar cells. We report tandems that combine solution-processed micrometer-thick perovskite top cells with fully textured silicon heterojunction bottom cells. To overcome the charge-collection challenges in micrometer-thick perovskites, we enhanced threefold the depletion width at the bases of silicon pyramids. Moreover, by anchoring a self-limiting passivant (1-butanethiol) on the perovskite surfaces, we enhanced the diffusion length and further suppressed phase segregation. These combined enhancements enabled an independently certified power conversion efficiency of 25.7% for perovskite-silicon tandem solar cells. These devices exhibited negligible performance loss after a 400-hour thermal stability test at 85°C and also after 400 hours under maximum power point tracking at 40°C.

14 SOLAR ENERGY↗
DOE OSTI2024

Learning Robust Marking Policies for Adaptive Mesh Refinement

Here in this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM). Experience shows that a naïve marking policy leads to inefficient use of computational resources for adaptive mesh refinement (AMR). Consequently, using AMR in practice often involves ad-hoc or time-consuming offline parameter tuning to set appropriate parameters for the marking subroutine. To address these practical concerns, we recast AMR as a Markov decision process in which refinement parameters can be selected on-the-fly at run time, without the need for pre-tuning by expert users. In this new paradigm, the refinement parameters are also chosen adaptively via a marking policy that can be optimized using methods from reinforcement learning. We use the Poisson equation to demonstrate our techniques on h- and hp-refinement benchmark problems, and our experiments suggest that superior marking policies remain undiscovered for many classical AFEM applications. Furthermore, an unexpected observation from this work is that marking policies trained on one family of PDEs are sometimes robust enough to perform well on problems far outside the training family. For illustration, we show that a simple hp-refinement policy trained on 2D domains with only a single re-entrant corner can be deployed on far more complicated 2D domains, and even 3D domains, without significant performance loss. For reproduction and broader adoption, we accompany this work with an open-source implementation of our methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA, which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Computer Science↗
DOE OSTI2020

Editors’ Choice—Perspective—Challenges in Moving to Multiscale Battery Models: Where Electrochemistry Meets and Demands More from Math

There has been significant recent interest in studying multiscale characteristics of current and next-generation batteries, including lithium-metal and lithium-sulfur batteries. Advances in computing power make researchers believe that the detailed multiscale models can be efficiently simulated to arrive at the insights for the degradation and performance loss; however, this is not true and special attention needs to be paid to local singularities, boundary layers, moving boundaries, etc. This article presents 2D examples that illustrate the importance of grid convergence studies, provides well-defined detailed models to test the efficiency of numerical schemes, and discusses the associated simulation challenges.

Shah, Krishna (ORCID:0000000278026361)↗
DOE OSTI2023

PVplr-stGNN 0.1.10

PV Performance Loss Rate Estimation using Spatio-temporal Graph Neural Networks PVplr-stGNN is a Python 3 package developed by the SDLE Research Center at Case Western Reserve University in Cleveland OH. This repository contains the full source PVplr-stGNN package. The package contains the PV-stGAE for missingness data detection and imputation and PV-DynGNN for PLR estimation.

Fan, Yangxin [Case Western Reserve Univ., Clevelan↗
DOE OSTI2022

AI4IO: A suite of AI-based tools for IO-aware scheduling

Traditional workload managers do not have the capacity to consider how IO contention can increase job runtime and even cause entire resource allocations to be wasted. Whether from bursts of IO demand or parallel file systems (PFS) performance degradation, IO contention must be identified and addressed to ensure maximum performance. In this paper, we present AI4IO (AI for IO), a suite of tools using AI methods to prevent and mitigate performance losses due to IO contention. AI4IO enables existing workload managers to become IO-aware. Currently, AI4IO consists of two tools: PRIONN and CanarIO. PRIONN predicts IO contention and empowers schedulers to prevent it. CanarIO mitigates the impact of IO contention when it does occur. We measure the effectiveness of AI4IO when integrated into Flux, a next-generation scheduler, for both small- and large-scale IO-intensive job workloads. Our results show that integrating AI4IO into Flux improves the workload makespan up to 6.4%, which can account for more than 18,000 node-h of saved resources per week on a production cluster in our large-scale workload.

Wyatt, II, Michael R.↗