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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Next-Generation Cobalt-Free Cathodes – A Prospective Solution to the Battery Industry's Cobalt Problem

Lithium-ion batteries are overreliant on cobalt containing cathodes. Current projections estimate that hundreds of millions of electric vehicles (EVs) will be on the road by 2050, and this ever-growing demand threatens to deplete global cobalt reserves at an alarming rate. Moreover, cobalt supply chain issues have significantly increased cobalt prices throughout the last decade. As such, energy storage research and development need to reduce the reliance on cobalt to meet ever-growing demand for lithium-ion batteries. The present review summarizes the science and technology gaps and potential of numerous cobalt-free Li-ion cathodes including layered, spinel, olivine, and disordered rock-salt systems. Despite the promising performance of these Co-free cathodes, scale-up and manufacturing bottlenecks associated with these materials must also be addressed to enable widespread adoption in commercial batteries. Overall, this review broadly highlights the enormous promise of “zero-cobalt” Li-ion batteries to enable sustainable production of EVs in the coming decades.

25 ENERGY STORAGE↗
DOE OSTI2022

Scalable Direct Recycling of Cathode Black Mass from Spent Lithium‐Ion Batteries

Abstract End of life (EoL) lithium‐ion batteries (LIBs) are piling up at an intimidating rate, which is alarming for environmental health. With further expected rapid growth of LIB use, the magnitude of spent battery accumulation is also expected to grow. LiNi x Co y Mn z O 2 (NCM) cathode materials are a dominant chemistry in high energy LIBs, and make up a huge portion of this waste accumulation. Direct recycling is one of the most promising ways to turn this waste to wealth, but has been limited to lab‐scale, due to lack of robustness, namely the tedious pretreatment required that involves toxic organic solvents. Herein, a process that integrates the pretreatment and relithiation of the cathode black mass is demonstrated. Cathode material from EoL electric vehicle (EV) batteries is treated in a 100 g per batch operation and the regenerated cathode active material demonstrates 100% electrochemical performance recovery, with 91% yield rate, and shows promise for further scale up. This process has the advantages of integration, scalability, and universality, which clears the barricade for direct recycling to move from lab to industry scale with considerable profitability.

Chemistry↗
DOE OSTI2023

Thermochromic Halide Perovskite Windows with Ideal Transition Temperatures

Abstract Urban centers across the globe are responsible for a significant fraction of energy consumption and CO 2 emission. As urban centers continue to grow, the popularity of glass as cladding material in urban buildings is an alarming trend. Dynamic windows reduce heating and cooling loads in buildings by passive heating in cold seasons and mitigating solar heat gain in hot seasons. Here, reduced energy consumption in highly glazed buildings in a mesoscopic building energy model is demonstrated when thermochromic windows are employed. Savings are realized across eight disparate climate zones of the United States. The model is used to determine ideal critical transition temperatures of 20–27.5 °C for thermochromic windows based on metal halide perovskite materials. Ideal transition temperatures are realized experimentally in composite metal halide perovskite films composed of perovskite crystals and an adjacent reservoir phase. The transition temperature is controlled by cointercalating methanol, instead of water, with methylammonium iodide and tailoring the hydrogen‐bonding chemistry of the reservoir phase. Thermochromic windows based on metal halide perovskites represent a clear opportunity to mitigate the effects of energy‐hungry buildings.

14 SOLAR ENERGY↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2025

Dual-Phase Malicious User Detection Scheme for IM-OFDMA Systems Using IQ Imbalance

Physical-layer security techniques have contributed to the achievement of various security objectives in an efficient and lightweight manner. Thus, these techniques have been widely considered for limited-resource networks such as Internet of Things networks. Among the different security objectives, malicious user detection by exploiting physical-layer parameters has demonstrated efficient performance. In this work, malicious user detection in the recently proposed index modulation-based orthogonal frequency division multiple access (IM-OFDMA) is addressed. The proposed malicious user detection scheme exploits the hardware impairments, especially the in-phase and quadrature imbalance parameters, for both legitimate and malicious users to design a dual-phase efficient detection scheme. The proposed scheme accounts for the special characteristics of IM-OFDMA transmission that are different from other multiple-access techniques. The performance of the proposed scheme was evaluated considering detection probability and false alarm probability performance metrics. Moreover, closed-form expressions of these metrics were derived for both phases and were validated by Monte Carlo simulation results under different configurations of IM-OFDMA systems.

Alaca, Ozgur [ORNL] (ORCID:0000000153713758)↗
DOE OSTI2021

One-loop structure of parton distribution for the gluon condensate and “zero modes”

We present results for one-loop corrections to the recently introduced “gluon condensate” PDF F(x). In particular, we give expression for the gg-part of its evolution kernel. To enforce strict compliance with the gauge invariance requirements, we have used on-shell states for external gluons, and have obtained identical results both in Feynman and light-cone gauges. No “zero mode” δ(x) terms were found for the twist-4 gluon PDF F(x). However a q 2 δ(x) term was found for the ξ = 0 GPD F(x, q 2 ) at nonzero momentum transfer q. Overall, our results do not agree with the original attempt of one-loop calculations of F(x) for gluon states, which sets alarm warning for calculations that use matrix elements with virtual external gluons and for lattice renormalization procedures based on their results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Trace elements and heavy metals in black vultures ( Coragyps atratus ) and turkey vultures ( Cathartes aura ) in the southeastern United States

Many vulture species worldwide are declining at alarming rates due to a variety of anthropogenic causes, including exposure to pollutants and pharmaceuticals through consumption of contaminated carrion. However, little is known about the extent to which vultures are exposed to various contaminants as well as toxicity thresholds for trace elements and heavy metals. Our objective was to quantify levels of trace elements and heavy metals within black vulture (Coragyps atratus) and turkey vulture (Cathartes aura) tissues to determine the extent to which populations in the southeastern United States are exposed to carrion that contains high levels of contaminants. We collected black vulture liver samples and examined differences in trace element and heavy metal concentrations between sexes and age classes (adult and juvenile). Further, we collected 81 blood and 42 feather samples from additional black and turkey vultures and compared differences between species and age classes. We found similar element concentrations between juvenile and adult black vultures with the exception of Cu, where levels 40 were higher in juveniles compared to adults. However, we did observe substantial differences in element concentrations between species for both blood and feather samples, with black vultures generally having higher concentrations of most elements. Our data revealed higher element levels in both species compared to toxicity thresholds found in other bird of prey species, such as blood and liver toxicity threshold suggestions for Pb poisoning in Falconiformes. Further, while average contaminant levels were generally low, extreme outliers were observed for some elements, including Pb, suggesting some individuals were exposed to high levels of potentially toxic elements. In conclusion, more research is needed to better understand contaminant exposure in black and turkey vultures across a broader geographic region, as well as elucidate toxicity thresholds and non-lethal impacts of contaminant exposure in these species.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Bayesian network↗
DOE OSTI2025

The role of catalyst acidity and microstructure on light olefin selectivity in polyethylene deconstruction in short contact time pulse Joule-heated reactors

The growing volume of plastics waste, compounded with a low recycling rate, has led to an alarming amount of plastics ending up in landfills or being incinerated. While pyrolysis offers a route for plastic waste deconstruction, its product distribution is often broad and poorly controlled due to unselective radical chemistry at high temperatures. We recently demonstrated that rapid pulse Joule-heated catalytic cracking over HZSM-5, combined with small fractions of steam, can achieve high selectivity (>80 %) toward C 2 -C 4 olefins, while significantly reducing coking compared to continuous Joule heating. Here, we investigate how acid catalyst properties, such as silica/alumina ratio, zeolite topology, and catalyst porosity, influence light olefin selectivity during polyethylene deconstruction via rapid pulse Joule heating. We demonstrate that silica-to-alumina ratios of ∼30 yield high light olefin selectivity, and small-pore zeolites favor light olefins at the expense of increased coke formation. To mitigate coking, we synthesize HZSM-5 nanosheets and hierarchical zeolites (MFI, FAU, and CHA). Furthermore, these catalysts achieve an ethylene selectivity of approximately 35 %, a twofold increase over prior catalytic pyrolysis. Additionally, co-feeding steam and incorporating hierarchical porosity reduce coke formation and enhance catalyst stability.

Catalytic cracking↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Pragmatic protocols for working cleanly when measuring ice nucleating particles

We report that measuring ice nucleating particles (INPs) is critical for understanding, and modeling, cloud formation, reflectivity, and precipitation patterns. However, because INPs are very rare in the atmosphere, but abundant—sometimes alarmingly so—in the dust that covers all work surfaces, contamination during processing of collected aerosol samples can be a significant hindrance to obtaining accurate measures of INPs. In preparing this technical note, we questioned the cleanliness of every collection and processing step involved in making immersion freezing measurements of INPs re-suspended from filter samples. The aim was to identify, and then minimize, all potential sources of significant contamination, including containers and tools used to store filters and prepare liquid suspensions, gloves, work surfaces, and the polymerase chain reaction (PCR) trays commonly used for sample analysis. While plasticware released few INPs, most gloves readily shed them, but this can be mitigated by using washed cleanroom vinyl or polyethylene gloves. Work surfaces, even those that had been cleaned, were prodigious reservoirs of contaminating INPs, but simply covering them with aluminum foil will provide an INP-free surface. By applying these practices, we developed a method to reduce the background level of INPs on Nuclepore™ polycarbonate filters in our tests to 0 at -25 °C and < 20 at -27 °C, making them suitable for use in all sampling environments.

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

Energy use of residential safety, security, and health devices

Miscellaneous electrical loads (MELs) are responsible for a significant fraction of energy consumption in buildings. This paper addresses one category of MELs: Safety, Security, and Health Devices (SSHDs). Common SSHDs include: electrical life safety equipment, smoke alarms, radon mitigation fans, and home oxygen concentrators. The installation or use of these devices is dictated by building codes, health providers, insurance companies, and other entities—none of which would ordinarily consider energy efficiency a priority. For this project, the most important residential devices were explored in terms of their governing regulations, functions, technologies, and energy use. The power consumption of 41 life safety SSHDs were measured. Individual power consumption of life safety devices is very small but many are typically required in every home. Devices performing the same functions have wide ranges in power use. Opportunities for reducing SSHD energy use were investigated, the results are summarized, and recommendations are made for further actions that could be taken to reduce SSHD energy use. Finally, SSHDs identified in this paper are responsible for at least one percent of current U.S. residential electricity consumption, but that number will climb steadily as existing buildings are upgraded and new types of SSHDs appear.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Upcycling disposable face masks into fuel range iso-alkanes through hydropyrolysis coupled with vapor-phase hydrocracking

The COVID-19 pandemic has resulted in an alarming accumulation of plastic waste. Herein, an integrated hydropyrolysis and hydrocracking process was performed to upcycle disposable masks into fuel-range iso-alkanes over carbon supported ruthenium (Ru/C). Experimental results indicated that catalyst type significantly affected product distribution during the hydropyrolysis and vapor-phase hydrocracking of disposable masks. Compared with zeolites-induced catalytic cascade process where up to ~25.9 wt% yield of aromatic hydrocarbons such as toluene and xylenes were generated, a ~82.7 wt% yield of desirable iso-alkanes with a high C 5 –C 12 gasoline selectivity of 95.5% was obtained over Ru/C under 550 °C hydropyrolysis temperature and 300 °C hydrocracking temperature at 0.2 MPa H 2 . Further, the cascade hydropyrolysis and hydrocracking process also exhibited high adaptability and flexibility in upcycling single-use syringes, food packaging, and plastic bags, generating 79.1, 81.6, and 80.3 wt% yields of fuel range iso/n-alkanes, respectively. This catalytic cascade hydrotreating process provides an efficient and effective approach to convert pandemic-derived plastic waste into gasoline-range fuel products.

09 BIOMASS FUELS↗
DOE OSTI2025

Hurricanes and turbulent floods threaten arsenic-contaminated coastal soils and vulnerable communities

Coastal environments, particularly those adjacent to Superfund sites, are at increased risk of contaminant release during natural disasters, posing serious threats to nearby communities. To investigate this issue, we employed an advanced laboratory flood simulator to impose arsenic-contaminated sediments to turbulent flooding events. We further monitored changes in arsenic collocation and solid-phase speciation using advanced synchrotron radiation-based techniques to understand the impacts of flooding on arsenic mobility. Our results demonstrate that turbulent conditions significantly enhance the resuspension of arsenic-rich sediments, resulting in increased arsenic release into the water. This mobilization is driven by the erosion of the reduced sediments and the redox-mediated transformation and dissolution of Fe and Mn (oxyhydr) oxides, which promote the release of As(III). We found that arsenic speciation on resuspended particles is closely tied to shear stress, with As(V) prevailing at low stress and the more toxic As(III) dominating at higher stress levels. In the post-erosion phase, solid-phase As(III) decreased while dissolved As(III) increased, indicating ongoing desorption. The persistence of multiple arsenic species on resuspended particles marks them as potential long-range transport vectors. Thus, the environmental impact of flooding and sediment resuspension extends beyond the event itself, raising longer-term concerns for arsenic mobility. Our comprehensive geospatial analysis revealed substantial overlap between arsenic-contaminated soils and regions at high risk of flooding and hurricanes across the conterminous United States. This overlap disproportionately impacts economically disadvantaged and marginalized communities. Approximately 40 million Americans reside within 10 kilometers of these high-risk contaminated zones, with nearly 28 million exposed to hurricane threats and around 18 million vulnerable to flooding risks. Alarmingly, over 40% of those affected by hurricanes and 33% of those impacted by flooding belong to underrepresented minority and low-income populations. These findings highlight the urgent need for targeted mitigation strategies to protect public health and address environmental justice concerns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A data-driven sensor placement approach for detecting voltage violations in distribution systems

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. Here, in this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.

24 POWER TRANSMISSION AND DISTRIBUTION↗