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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Electron Microscopy Image Classification Using Data Augmentation

Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.

Welsman, Jordan A↗
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 OSTI2024

Electrochemical Oxidation of Seawater for Renewable Energy Generation and Storage in Coastal Areas

In this work, we demonstrated how the Clean Sustainable Electrochemical Treatment process can be used to process seawater while simultaneously generating molecular hydrogen. The specific goal of this project is to (1) demonstrate selective removal of chemical oxygen demand over the oxygen and chlorine evolution reactions, (2) optimize electrode composition to improve electrochemical performance, and (3) demonstrate long-term (>100 h) stability experiments.

08 HYDROGEN↗
DOE OSTI2026

Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Xue, Guoliang [Arizona State University]↗
DOE OSTI2026

Design of a Molten Salt Flow Cell for Combined Absorbance and Laser-Induced Breakdown Spectroscopy for Online Measurements

A novel flow cell allowing for multiple optical spectroscopy measurements on flowing molten salts was designed, and demonstrative calibrations of impurities in aqueous samples were performed. Online compositional measurements of molten salts are of high interest to monitor the state of relevant solar and nuclear systems. Here, the Spectroscopic Configuration for Optical Real-Time Characterization of High-Temperature (SCORCH) fluids cell was designed to meet this need by providing optical access to a high-temperature molten salt sample stream without physical contact between the sample and window materials. Laser-induced breakdown spectroscopy (LIBS) was utilized to quantify Li, Cr, Fe, Ni, Sr, and Pr at concentrations ranging nominally from 0 to 315 mmol L −1 . Laser power, frequency, and plasma position were optimized to mitigate challenges associated with sample splashing. Univariate calibration models were built with R 2 > 0.98, percent root mean square error of cross-validation (%RMSECV) as low as 2.7%, and limits of quantification (LOQs) down to 4.1 mmol L −1 . Simultaneously, absorbance calibrations were developed for the applicable analytes (Cr, Ni, and Pr) using Beer’s law with a pathlength of 4.41 ± 0.10 mm. These models provide excellent quantification performance with R 2 > 0.999, %RMSECV as low as 0.6%, and LODs down to 0.08 mmol L −1 . Although these calibrations were performed for each spectroscopic technique separately, the two methods may be combined in the future through multivariate modeling and sensor fusion to provide more robust models with the benefits of both techniques (e.g., absorbance: oxidation state concentrations, LIBS: elemental concentration). Additionally, optimized spectrometers may be deployed to enhance sensitivity.

absorbance spectroscopy↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Simple device modification simultaneously enhances indoor passive evaporative cooling power and duration

Passive evaporative cooling from a damp cloth or vessel can be used for localized cooling of perishables such as food and medicine, drinking water, and even people. Such applications have two key objectives, which are normally in tension with each other: minimizing the water consumption rate and maximizing the cooling (steady-state temperature swing between the cool cloth and the warm surroundings). Here, we present a simple device that delivers higher performance in both metrics simultaneously by adding a perforated aluminum foil sheet suspended over a stagnant air layer to thermally shield the evaporating surface while also facilitating mass transfer. Experimental results demonstrate a simultaneous 10%–25% increase in temperature swing and 2–3× reduction in water consumption rate as compared to conventional evaporative cooling. This improvement is achieved through careful modeling to optimize the coupled heat and mass transfer through the airgap (thermally insulating and vapor permeable) and perforated foil (infrared reflective and vapor permeable).

Chen, Sarah Mizuno↗
DOE OSTI2025

CeO 2 Promoted CuO/MgO-Al 2 O 3 Catalyst with Enhanced Activity and Water-Resistance for CO Oxidation

Copper (Cu)-based catalysts have emerged as cost-effective and sustainable alternatives to noble metal systems (e.g., Pt, Pd) for catalytic CO oxidation. However, their practical application is hindered by insufficient low-temperature activity and rapid deactivation under wet conditions containing moisture. To address these challenges, this work introduces CeO 2 -modified CuO/MgO-Al 2 O 3 (Cu-Ce/MA) catalysts, strategically designed to enhance the catalytic performance and water resistance simultaneously. These catalytic materials were evaluated for CO oxidation under both dry and humid conditions, revealing that CeO 2 modification significantly improves the low-temperature activity. Specifically, the optimal catalyst, Cu-30Ce/MA, achieved a 50% CO conversion temperature (T 50 ) of 151 °C, a marked reduction from 218 °C on Cu/MA reference catalyst. Furthermore, the water resistance improves in a CeO 2 content-dependent manner, with higher CeO 2 loadings imparting greater stability in humid environments. Detailed characterizations demonstrate that CeO 2 promotes the dispersion of CuO and stabilizes Cu sites, while also enhancing the low-temperature reducibility and CO adsorption capacity. Crucially, CeO 2 modification suppresses the competitive H 2 O adsorption, mitigating water-induced deactivation. These synergistic effects collectively rationalize the superior activity and durability of Cu-Ce/MA catalysts. By elucidating the dual role of CeO 2 in optimizing Cu-based systems, this study advances the rational design of cost-effective catalysts for real-world CO emission control, particularly under water-rich industrial conditions.

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

Autonomous Cyber Defense Against Dynamic Multi-strategy Infrastructural DDoS Attacks

Dynamic Infrastructural Distributed Denial of Service (I-DDoS) attacks constantly change attack vectors to congest core backhaul links and disrupt critical network availability while evading end-system defenses. To effectively counter these highly dynamic attacks, defense mechanisms need to exhibit adaptive decision strategies for real-time mitigation. This paper presents a novel Autonomous DDoS Defense framework that employs model-based reinforcement agents. The framework continuously learns attack strategies, predicts attack actions, and dynamically determines the optimal composition of defense tactics such as filtering, limiting, and rerouting for flow diversion. Our contributions include extending the underlying formulation of the Markov Decision Process (MDP) to address simultaneous DDoS attack and defense behavior, and accounting for environmental uncertainties. We also propose a fine-grained action mitigation approach robust to classification inaccuracies in Intrusion Detection Systems (IDS). Additionally, our reinforcement learning model demonstrates resilience against evasion and deceptive attacks. Evaluation experiments using real-world and simulated DDoS traces demonstrate that our autonomous defense framework ensures the delivery of approximately 96 - 98% of benign traffic despite the diverse range of attack strategies.

Dutta, Ashutosh↗
DOE OSTI2020

Quantifying Dynamic On-State Resistance of GaN HEMTs for Power Converter Design via a Survey of Low and High Voltage Devices

Designing and optimizing high switching frequency, ultra-efficient converters requires detailed knowledge of the behavior and parasitic parameters for both active and passive components. Recently, wide bandgap transistors have enabled simultaneous increases in both switching frequency and efficiency due to higher maximum operating junction temperature limits, lower dc on-state resistances, and reduced parasitic inductances and capacitances. Yet, the early acceptance of gallium-nitride (GaN) switches was plagued by detrimental dynamic on-state resistance effects. This complex phenomenon for GaN devices is characterized by deviations in on-state resistance from dc operating characteristics based on design choices such as the magnitude and duration of both voltage and current stress, switching mode, and junction temperature. While device manufacturers have made improvements compared to early generation devices, experimental evidence from a survey of commercial GaN transistors highlight measurable change in on-state resistance still exists due to variations in voltage stress during hard-switching operation. After sharing insights for obtaining low noise measurements, an analysis method along with two metrics are proposed to characterize dynamic on-state resistance measurements for power electronics designers. Furthermore, quantifying the performance of GaN devices with standardized metrics facilitates a fair comparison between different GaN device technologies during converter development, enables manufacturing qualification for GaN switches, and provides a benchmark to catalyze improvement for the next generation of GaN device development.

14 SOLAR ENERGY↗
DOE OSTI2020

High-speed fiber-based spectrometer for plasma Thomson scattering

Here we present a novel concept for a Thomson scattering diagnostic, based on a high-speed fiber optic spectrometer. The high-speed fiber optic spectrometer presented here translates a spectral measurement from the frequency domain into the time domain, thus requiring the use of only a single photodetector for spectral acquisition. The high temporal precision offered by the instrument gives rise to a number of advantages over traditional spectrometers, such as nearly background-free measurements and multiple uses of the same injected beam. Multiple uses of the same beam would enable greatly increased measurement rates, in the range of 10–100 MHz. The spectral range and resolution of the fiber spectrometer can be easily tailored to be optimized for the light source and experimental conditions by selecting different lengths of fiber, thus allowing for the proposed technique to exhibit high dynamic range when measuring many points simultaneously. Finally, due to the temporal separation of the background from the signal, these improvements are possible without the need for increased average input laser power.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Spatial‐Uniformity–Driven Bayesian Optimization for Rapid Development of Printed Perovskite Solar Cells

Printed metal halide perovskites can enable rapid, roll-to-roll manufacturing of a broad class of optoelectronics—flexible solar cells and imagers among them—while promising cost and speed advantages over incumbent silicon. However, though current methods offer high throughput and patterning capabilities, perovskite films’ spatial heterogeneity remains a challenge for large-area devices. Here, a spatial-uniformity-driven Bayesian optimization (BO) approach is leveraged to accelerate the development of printed perovskite solar cells and improve large-area device performance. Using a BO surrogate model, a 6D design space of ink chemistry and printing physics is explored via extensive iterative experimentation (≈100) informed by an objective function capturing spatial photoluminescence (PL) variance. It is discovered that optimizing for uniformity drives rapid advances in photovoltaic performance, yielding ≈20% power conversion efficiency (PCE) for small area (0.134 cm 2 ) devices and > 16% for large area (1 cm 2 ) devices. This machine-learning approach simultaneously enables rheological comparison of ink formulations that accelerate the leveling of Saffman-Taylor artifacts and improve film uniformity. Here, this showcases uniformity-driven BO as an efficient approach for uncovering the key printing physics and mitigating spatial heterogeneity to enable device scaling beyond small cell areas.

14 SOLAR ENERGY↗
DOE OSTI2020

Fibril Network Strategy Enables High‐Performance Semitransparent Organic Solar Cells

Abstract The development of semitransparent organic solar cells (ST‐OSCs) represents a significant step toward the commercialization of OSCs. However, the trade‐off between power conversion efficiency (PCE) and average visible transmittance (AVT) restricts further improvements of ST‐OSCs. Herein, it is demonstrated that a fibril network strategy can enable ST‐OSCs with a high PCE and AVT simultaneously. A wide‐bandgap polymer PBT1‐C‐2Cl that can self‐assemble into a fibril nanostructure is used as the donor and a near‐infrared small molecule Y6 is adopted as the acceptor. It is found that a tiny amount of PBT1‐C‐2Cl in the blend can form a high speed pathway for hole transport due to the well distributed fibril nanostructure, which increases the transmittance in the visible region. Meanwhile, the acceptor Y6 guarantees sufficient light absorption. Using this strategy, the optimized ST‐OSCs yield a high PCE of 9.1% with an AVT of over 40% and significant light utilization efficiency of 3.65% at donor/acceptor ratio of 0.25:1. This work demonstrates a simple and effective approach to realizing high PCE and AVT of ST‐OSCs simultaneously.

Xie, Yuanpeng↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Determining the Solubility Behavior of Kogarkoite in Simulated Nuclear Waste

Kogarkoite (Na 3 FSO 4 ) is a sparingly soluble fluoride–sulfate double salt that has been identified in high level nuclear waste sludge at the Hanford Site and, more recently, in sludge batch compilation samples at the Savannah River Site (SRS). Due to its complex dissolution behavior, which exhibits an inverse dependence on sodium ion activity, the presence of this mineral poses significant challenges to waste retrieval and processing. Incomplete dissolution during sludge washing can lead to the retention of fluoride and sulfate in the high-level waste feed, potentially causing the formation of corrosive, immiscible molten salt layers, known as "glass gall,” in vitrification melters. Current efforts to optimize flowsheet parameters and wash-water volumes are hindered by the absence of a commercially available, certified reference material, which prevents the accurate calibration of analytical methods and the verification of dissolution kinetics. To address this critical gap, this research focuses on the laboratory synthesis of pure Kogarkoite to serve as a standard for comprehensive solubility and washing performance testing. A coupled synthesis and simulant campaign was executed using an evaporative crystallization protocol designed to replicate the dynamic concentration effects observed in tank farm operations. Thirteen simulant matrices were prepared by dissolving systematically varied ratios of sodium fluoride (NaF) and sodium sulfate (Na 2 SO 4 ) in deionized water under three distinct caustic regimes: 0.0 g (control), 4.0 g (~1 M), and 12.0 g (~3 M) sodium hydroxide (NaOH). While thermodynamic equilibrium models suggest that high-caustic environments should favor the stability of the double salt7, results from this evaporative study at 25 0 C revealed a distinct kinetic divergence. Simulants with high hydroxide loading predominantly yielded large, blocky crystals of sodium sulfate decahydrate (Na 2 SO 4 .10H 2 O). Successful synthesis of pure Kogarkoite was achieved exclusively in specific NaOH-free compositional windows, where the precipitate manifested as fine, opaque granular aggregates. Ion chromatography (IC) analysis confirmed phase purity through the simultaneous stoichiometric depletion of both fluoride and sulfate from the supernatant. This successful synthesis establishes a reproducible route to generate bulk Kogarkoite, enabling the subsequent phase of quantitative dissolution testing using inhibited water to optimize sludge-batch assembly.

Sarker, Md Sharif [Florida International Univ. (FI↗