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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimizing dicyandiamide pretreatment conditions for enhanced structure and electronic properties of polymeric graphitic carbon nitride

Graphitic carbon nitride, a polymeric semiconductor, possesses a distinctive electronic band structure and exceptional chemical stability, making it a highly promising material for various catalytic applications such as electrocatalysis, photocatalysis, and photo-electrocatalysis. However, its practical applications remain limited due to its low active site density and poor electrical conductivity. In this study, to overcome such limitations, we have conducted a thorough investigation to explore the impact of dicyandiamide (DCDA) precursor pretreatment prior to thermal polymerization to graphitic carbon nitride. The DCDA precursor was subjected to various pretreatment methods including grinding using mortar and pestle, recrystallization through stirring or probe sonication after dissolving in deionized water, and freeze drying, prior to thermal polymerization at 550 °C for 2 hours. The structural and morphological properties of the catalysts prepared were compared and characterized by X-ray diffraction (XRD), attenuated total reflectance–Fourier transform infrared (ATR-IR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) analysis while the electronic band gap properties were determined based on density functional theory (DFT) calculations for a set of crystalline systems having C/N ratios similar to those identified experimentally. In comparison to direct thermal polymerization, pretreated samples rendered the same product yield, diverse morphologies with flat or wrinkle structures, and reduced electrochemical resistance, making them suitable for use in various catalytic processes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Impact of reverse flow induced by a sawtooth anode on the performance of an argon Hall thruster

This study reports the performance comparison of the RAIJIN-66 TAL-type Hall thruster with argon propellant, using two hollow anode designs, one with a simple straight shape and another one with a sawtooth shape. Simulations of the rarefied gas in the sawtooth hollow anode region suggest a successful reversal of the direction of neutral particle flow, with an average increase in the neutral particle density by 15.5% when compared to the straight-shaped hollow anode. This contributes to the improvement in performance across a range of discharge voltages that was measured experimentally. When the thruster is operated with a discharge voltage of 150 V and a flow rate of 70 SCCM, the propellant utilization efficiency with the straight anode is 14%, while with the sawtooth anode it is 25.1%. The anode efficiency reaches in the vicinity of 15% with the sawtooth anode in high voltage conditions, exceeding the efficiencies achieved with the straight anode in the same operating conditions. The optimal magnetic field condition for argon operation and the tuning parameters of the sawtooth anode design for RAIJIN-66 are also discussed.

Satpathy, Dibyesh [Univ. of Tokyo (Japan)] (ORCID:↗
DOE OSTI2022

Numerical modeling based machine learning approach for the optimization of falling - film evaporator in thermal desalination application

Scale formation that drastically increases thermal resistance and reduces freshwater production remains a critical challenge in thermal desalination. Novel designs of falling film evaporator and optimal operating condition hold great promise to mitigate scale formation, and increase heat transfer performance and fresh water production. In this work, CFD simulation based machine learning and multi-objective optimization are performed to identify optimal conditions and tube arrangement for evaporator. Non-dominated sorting genetic algorithm is adopted to determine and analyze the optimal pareto front for multiple objectives in desalination criteria. The errors of training, validation, and testing set are computed to identify an optimal hyperparameter set. For performance ratio, fouling resistance, and water production rate, the average relative error is 2.26%, 3.67%, and 3.24%. At pareto front, both performance ratio and water production rate increase at high temperature with fouling resistance (thermal resistance of the fouling layer) increasing as well. Tradeoffs between mitigating scale formation and enhancing desalination performance are evaluated in optimizations for different objectives. Finally, potential optima are identified and can be applied as guidelines to determine evaporator design and system operating conditions.

42 ENGINEERING↗
DOE OSTI2020

Experimental Performance Evaluation of a Hyper-Branched Polymer Electrolyte for Rechargeable Li-Air Batteries

A hyper-branched polymer (HBP) electrolyte is synthesized for rechargeable lithium-air (Li-air) battery cell and experimentally evaluated its performance in actual battery cell environment. Several real-world battery cells were fabricated with synthesized HBP electrolyte, pure lithium metal as anode and an oxygen permeable air cathode to evaluate reproducibility of the rechargeable Li-air battery cell. The effect of various conditions such as various HBP based electrolytes, discharge current -0.1~0.5 mA, cathode preparation processes and carbon contents on the battery cell performance were experimentally evaluated using the fabricated battery cells under dry air condition. Detailed HBP electrolyte synthesis procedures and experimental performance evaluation of Li-air battery cell for various conditions are presented. The experimental results showed that different conditions and processes significantly affect the Li-air battery performance. Upon taking into account the effect of different conditions and processes, optimized HBP electrolyte materials, cathode process and conditions were determined. Several Li-air battery cells were fabricated with optimized conditions and optimized battery cell materials to determine the reproducibility and performance consistency. Experimental results showed that over 55–65 h of discharge occurred over 2.5 V terminal cell voltage with all three optimized Li-air battery cells. It implied that the optimized Li-air battery cells were reproducible and were able to hold charge over 2.5 V for more than 2 days. Experimental results of the Li-air battery cell with further refined optimized materials revealed that the battery cell can discharge more than 10 days (i.e., more than 250 h) at or above 2.0 V. The experimental results also showed that the Li-air battery discharge time got shorter as the discharge-charge cycle increases due to increase in internal resistances of battery cell materials. The experimental results confirmed that the lithium-air battery cell can be reproduced without loss of performance and can hold charge more than 10 days at or over 2.0 V. The investigation results obtained may usher a pathway to manufacture a long-life rechargeable Li-air battery cell in the near future.

25 ENERGY STORAGE↗
DOE OSTI2023

Mild organosolv pretreatment of sugarcane bagasse with acetone/phenoxyethanol/water for enhanced sugar production

This study conducted mild organosolv pretreatment of sugarcane bagasse with acetone/phenoxyethanol/water (APW) solutions to improve sugar production in the subsequent enzymatic hydrolysis step. Here, the Box–Behnken design was used to optimize pretreatment conditions based on lignin removal. Further examination of the data revealed that lignin removal was positively correlated (R 2 > 0.95) with the combined severity factor (CSF). Pretreatment under the optimal conditions (125 °C–120 min, 0.17 M H 2 SO 4 , liquid–solid ratio of 15) led to 98.1% lignin removal and 74.5% cellulose digestibility compared to a low digestibility of 9.3% with raw sugarcane bagasse (SCB). Moreover, the APW process showed effective fractionation of pine, corn stalk and bamboo, and lignin removal was over 90%. The recovered lignin was characterized by 2D-HSQC NMR and 31P NMR, suggesting that the pretreatment resulted in breakage of β-O-4, total phenolic OH and H-units increased. However, the enzymatic hydrolysis efficiency (EHE) of samples with a lower lignin content (<4%) varied significantly. Correlations between various substrate-related factors of the pretreated SCB and EHE were analyzed to understand this observation. The results showed that the surface area of cellulose, lateral order index, CrI, and specific surface area were the predominant factors for enzymatic hydrolysis rather than lignin properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physiological roles of an Acinetobacter -specific σ factor

ABSTRACT The Gram-negative pathogen Acinetobacter baumannii is considered an “urgent threat” to human health due to its propensity to become antibiotic resistant. Understanding the distinct regulatory paradigms used by A. baumannii to mitigate cellular stresses may uncover new therapeutic targets. Many γ-proteobacteria use the extracytoplasmic function (ECF) σ factor, RpoE, to invoke envelope homeostasis networks in response to stress. Acinetobacter species contain the poorly characterized ECF “SigAb”; however, it is unclear if SigAb has the same physiological role as RpoE. Here, we show that SigAb is a metal stress-responsive ECF that appears unique to Acinetobacter species and distinct from RpoE-like ECFs. We combine promoter mutagenesis, motif scanning, and chromatin immunoprecipitation-sequencing (ChIP-seq) to define the direct SigAb regulon, which consists of genes encoding SigAb itself, the stringent response mediator, RelA, and the uncharacterized small RNA, “SabS.” However, RNA-seq of strains overexpressing SigAb revealed a large, indirect regulon containing hundreds of genes. Metal resistance genes are key elements of the indirect regulon, as CRISPRi knockdown of sigAb or sabS resulted in increased copper sensitivity and excess copper-induced SigAb-dependent transcription. Furthermore, we found that two uncharacterized genes in the sigAb operon, “ aabA ” and “ aabB ,” have anti-SigAb activity. Finally, employing a targeted Tn-seq approach that uses CRISPR-associated transposons, we show that sigAb , aabA , and aabB are important for fitness even during optimal growth conditions. Our work reveals new physiological roles for SigAb and SabS, provides a novel approach for assessing gene fitness, and highlights the distinct regulatory architecture of A. baumannii . IMPORTANCE Acinetobacter baumannii is a hospital-acquired pathogen, and many strains are resistant to multiple antibiotics. Understanding how A. baumannii senses and responds to stress may uncover novel routes to treat infections. Here, we examine how the Acinetobacter -specific transcription factor, SigAb, mitigates stress. We find that SigAb directly regulates only a small number of genes, but indirectly controls hundreds of genes that have substantial impacts on cell physiology. We show that SigAb is required for maximal growth, even during optimal conditions, and is acutely required during growth in the presence of elevated copper. Given that copper toxicity plays roles in pathogenesis and on copper-containing surfaces in hospitals, we speculate that SigAb function may be important in clinically relevant contexts.

Bacon, Emily E. (ORCID:0000000180907689)↗
DOE OSTI2026

Strategies for Enhancing Battery Life Under Fast Charging: Insights from NMC-Based Cell Cycling

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer Gr/NMC811 cells under various conditions, including depths of discharge (DODs of 68%, 84%, and 100%), upper charge cutoff voltages (4.1–4.2 V), and post-charge rest periods (2–30 min), using a 20 min fast charging protocol for up to 500 cycles (up to 150,000 miles of EV use assuming 3.3 mi/kWh vehicle level energy efficiency). Surprisingly, higher DODs under fast charging improved battery life and performance compared to lower DODs. Reducing the upper charge cut-off voltage helped mitigate degradation. A brief 2 min rest period after charging further reduced aging effects. The primary aging modes were loss of lithium inventory and cathode active material. Although minor lithium plating was observed within 500 cycles, it did not affect performance significantly. These findings suggest that, with optimized conditions, cells can sustain hundreds of fast charge cycles—equivalent to over 100,000 miles of EV use—without significant adverse effects on performance or longevity.

25 - ENERGY STORAGE↗
DOE OSTI2025

Nitroarene Photoactivation Promotes Oxidative Deconstruction of Olefinic Polymers

Photoactivation of nitroarenes has been recently reported to induce the transformation of alkenyl bonds into carbonyl functionalities. Capitalizing on this unique photochemical mechanism, this study explores the use of nitroarenes to achieve oxidative cleavage of olefinic polymers under visible light irradiation. The degradation of various olefinic polymers, including commercially available polybutadiene, polynorbornene, both linear and cyclic poly(phenylacetylene), as well as backbone-modified polyacrylates with alkenyl functionality was investigated. To elucidate the efficacy of this methodology, a series of nitroarene derivatives bearing variable substituents were screened for their degradative efficiency on polybutadiene. Varying nitroarene stoichiometry, reaction temperature, and pos-treaction workup conditions were investigated to optimize degradation conditions. Furthermore, the results demonstrated that photoexcited nitroarenes enable efficient oxidative degradation of olefinic polymers in a safe and sustainable manner, providing a novel strategy for mild macromolecular deconstruction.

Bond cleavage↗
DOE OSTI2024

Machine learning enhanced characterization and optimization of photonic cured MAPbI 3 for efficient perovskite solar cells

Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC conditions for fabricating methylammonium lead iodide (MAPbI 3 ) films by quantitatively comparing their ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI3 film with an absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However, when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI 3 and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI 3 , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the optimization of PC processing conditions for MAPbI 3 films.

14 SOLAR ENERGY↗
DOE OSTI2020

Development of an autothermal formate-based hydrogen generator: From optimization of formate dehydrogenation conditions to thermal integration with Fuel Cells

Formate received significant attention for storing H2 in chemical bonds using the concept of H2 carriers. In this report, hydrogen generation from formate was optimized systematically by varying reaction variables. Initial mass activity with a turnover frequency of 3200 molH2 molPd–1 h–1 and 92% H2 yield were obtained in sodium formate (7 M 2.0 mL) dehydrogenation over Pd(3 wt %)/C at 80 °C. Influence of formate cations (Na, K, and NH4) on dehydrogenation was also elucidated, presenting that the fastest initial reaction kinetics was achieved with ammonium formate, whereas the highest H2 yield was obtained with potassium formate (PF) in multiple catalyst recycle tests. Finally, an on-site power generation system was integrated, where a proton exchange membrane fuel cell (PEMFC) was operated in conjunction with H2 produced from the custom developed semibatch dehydrogenation reactors. The system operation was demonstrated with continuous feeding of PF to generate H2 and power on demand without an external heat source by utilizing waste heat produced from the PEMFC in a highly efficient manner.

formate dehydrogenation, optimization of reaction ↗
DOE OSTI2020

Modulating thermal load through lightweight residential building walls using thermal energy storage and controlled precooling strategy

Precooling is a recognized technique for reducing cooling energy in buildings during peak hours by shifting load to off-peak hours. This technique is particularly effective in buildings with high thermal mass, because of their large thermal energy storage capacity, and in commercial buildings due to their variable electricity pricing based on time-of-use rates. Precooling in residential buildings has been a matter of limited interest in the past because of their low thermal mass and typically uniform electricity pricing rate. While previous studies on precooling primarily focused on cost savings, an important aspect of precooling is the thermal load modulation, which could be very effective in managing peak demand in lightweight residential buildings integrated with thermal energy storage systems. In this study, we examine different precooling strategies to manage the heat gains in lightweight building walls integrated with phase-change materials. We create nine different precooling profiles by controlling the interior temperature, and then evaluate the influence of the precooling profiles on four key building energy performance parameters: total heat gain, peak heat gain, maximum heat gain during peak hours, and time at which peak occurs. To thoroughly understand the fundamental physics, we first consider hypothetical climates and obtain the optimal precooling strategy required to achieve maximum peak shedding and shifting while minimizing the total heat gains. We then extend the model to Baltimore, Maryland, and estimate the benefits of the optimized precooling strategy under real conditions. The optimal precooling strategy proposed in this study can shift the peak heat gain by up to 14 hours, thereby reducing the heat gain during peak period by up to 95%, at the expense of a 23% increase in the total heat gains.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Carbon molecular-sieve membranes developed from a Tröger’s base polymer and possessing superior gas-separation performance

Carbon molecular-sieve membranes possess tremendous practical advantages over unary polymer membranes by providing high gas-separation performance levels, coupled with excellent mechanical and chemical stability. Improving their overall effectiveness greatly expands the competitiveness of this class of membranes. In the present study, carbon membranes are fabricated from a Tröger’s base polymer as the precursor. Further, by optimizing the carbonization conditions, the gas-separation performance of the resultant membranes are significantly enhanced. Under optimized conditions, a H 2 permeability of up to 1135 Barrer is achieved, with a corresponding H 2 /CH 4 selectivity of 1170 and a CO 2 /CH 4 selectivity of 238. While increasing the operating temperature slightly reduces the selectivity, it still remains in the high-separation region. Overall, the measured separation performance levels for H 2 -related separations, i.e., H 2 /CH 4 , H 2 /N 2 and H 2 /CO 2 , all substantially exceed the Robeson upper bound. Moreover, the CO 2 /CH 4 separation efficacy also lies above the 2019 upper bound, indicating that the carbon membranes developed in the present work are versatile and promising for many different gas-separation applications.

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

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Numerical framework for integrated additive manufacturing-compression molding (AM-CM) of thermoplastic composites

Additive manufacturing-compression molding (AM-CM) has emerged as a transformative technology in advanced composite manufacturing. Additive manufacturing (AM) offers high design flexibility and the ability to produce complex geometries with precisely aligned fibers in the preferred orientation. Compression molding (CM) enhances composite materials by providing excellent dimensional stability, reduced porosity, high production rates, and a smooth surface finish. Despite these advantages, extensive integrated analysis is required to optimize processing conditions for improved fiber orientation distribution (FOD) and porosity control. Here, this study develops a comprehensive numerical model to simulate the AM-CM manufacturing process. The model isolates the effects of both the AM and CM phases while also capturing their integration. Additionally, it accounts for heat transfer, temperature-dependent viscosity, and fiber orientation in the extruded fiber-filled polymer, accurately representing material behavior during processing. This approach enables the analysis of interactions between deposited beads of complex strand shapes and their interface regions after full compression. Moreover, the model predicts key parameters such as polymer flowability, fiber orientation, and temperature evolution in AM-CM parts. By optimizing processing conditions, it facilitates a controlled and predictable microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Topology optimization of compliant mechanisms under transient thermal conditions

This work considers multi-material topology optimization of compliant mechanisms under transient thermal and quasi-static mechanical conditions wherein thermally actuated devices are optimized for different operating conditions. The materials are modeled using finite strain thermo-hyperelasticity and a two way coupling between the energy balance and equilibrium equations is investigated. The design updates are generated from the gradient-based method of moving asymptotes optimizer and the sensitivities are computed using the time dependent adjoint sensitivity analysis. Results show the impact of designing for short versus long actuation times.

42 ENGINEERING↗