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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A universal etching method for synthesizing high-performance single crystal cathode materials

LiNi x MnyCo 1-x-y O 2 (NMC) is considered the most appealing cathode material due to its high energy density and low cost. However, the stability and safety concerns, caused by the degradation of polycrystalline cathode materials during cycling, have restricted their practical applications. To overcome this shortcoming, converting polycrystalline cathode to high-performance single-crystal cathode materials becomes an appealing solution. In this work, a universal etching approach is firstly developed to synthesize single-crystal cathode materials. The rate performance of the obtained single-crystal NMC111 is 10–15% more than that of polycrystalline NMC111 whereas the capacity retention of single-crystal NMC111 is enhanced by ~12% after 300 cycles at 0.5C. The obtained single-crystal NMC622 exhibits a pronounced improvement in rate performance, especially at high rates (~28.6% better at 5C and ~129% better at 10C) and has a comparable cycle performance compared to polycrystalline NMC622. Altogether, the findings propose an alternative approach to generate single-crystal particles with high energy density and cycle stability for the next generation lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Direct Laser Writing of Multimetal Bifunctional Catalysts for Overall Water Splitting

Water electrolysis is of interest as a sustainable way to produce clean hydrogen and oxygen fuel and help mitigate the rising problems of climate change while meeting global energy demands. High-efficiency, stable, and earth-abundant bifunctional catalysts are needed to enable more effective electrochemical cells for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Here, we investigate the synthesis, composition, performance, and mechanism of multimetal catalysts serving dual functionality in both OER and HER of water electrolysis. Through a laser synthesis method, we synthesized heterogeneous catalysts of nanocrystalline multimetallic alloy pockets embedded within an amorphous oxide matrix. We evaluated the performance and composition of a range of mixed transition-metal oxide materials for both OER and HER, ultimately synthesizing a Cr 0.01 Fe 0.27 Co 0.34 Ni 0.38 O x /C y catalyst that has a stable, high-rate, and competitive overall water splitting performance of 1.76 V at 100 mA cm –2 in an alkaline medium. Using density functional theory to gain insight as the active site and mechanism, we propose that the inclusion of a minor amount of Cr increases the degeneracy of energetic states that lowers the cost of forming the O 2 p–d bond and H 1 s–d bond due to the hybridization of s, p, and d orbitals from Cr. Using a two-electrode water electrolysis cell with a constant potential of 1.636 V to mimic the setup for fuel production, we found the catalyst to be stable at 14–15 mA cm –2 for 40 h. Furthermore, this laser synthesis method allowing for facile and rapid synthesis of complex multimetal systems demonstrates how doping a Fe, Co, and Ni heterogeneous amorphous/nanocrystalline structure with small amounts of Cr is important for bifunctional catalytic behavior, particularly for increasing HER functionality in advancing our understanding for future electrocatalytic design.

08 HYDROGEN↗
DOE OSTI2021

Synthesis of Core@Shell Cu-Ni@Pt-Cu Nano-Octahedra and Their Improved MOR Activity

Fabrication of 3d metal-based core@shell nanocatalysts with engineered Pt-surfaces provides an effective approach for improving the catalytic performance. The challenges in such preparation include shape control of the 3d metallic cores and thickness control of the Pt-based shells. Herein, we report a colloidal seed-mediated method to prepare octahedral CuNi@Pt-Cu core@shell nanocrystals using CuNi octahedral cores as the template. By precisely controlling the synthesis conditions including the deposition rate and diffusion rate of the shell-formation through tuning the capping ligand, reaction temperature, and heating rate, uniform Pt-based shells can be achieved with a thickness of < 1 nm. Furthermore, the resultant carbon-supported CuNi@Pt-Cu core@shell nano-octahedra showed superior activity in electrochemical methanol oxidation reaction (MOR) compared with the commercial Pt/C catalysts and carbon-supported CuNi@Pt-Cu nano-polyhedron counterparts, demonstrating that both the lattice strain and shape effects arising from the as-prepared CuNi@Pt-Cu octahedral nanocatalysts play a crucial role in MOR performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

BISON validation of FeCrAl cladding mechanical failure during simulated reactivity-initiated accident conditions*

Here, a reactivity-initiated accident (RIA) is a postulated design basis accident in light water reactors (LWRs) in which a rapid reactivity insertion induces a fission rate increase and a fuel pellet temperature rise. During RIA, the fuel pellet thermally expands and may cause pellet-cladding mechanical interaction (PCMI). Separate effects PCMI tests were performed on C26M FeCrAl cladding tube samples, introducing biaxial stress via a well understood modified burst test (MBT) system. A high-speed camera in the MBT system captured the projections, covering a 360° view of the cladding deformation and enabling a digital image correlation (DIC) method to quantify the surface strains with high fidelity. Representative hot zero-power RIA mechanical loading conditions were applied to the sample, and the test duration ranged from 20 to 500 ms at an average temperature of 573 K. BISON finite element–based fuel performance code modeling was performed against the high-fidelity DIC data produced from the MBTs. Validation calculations were conducted with 2D models and systematically compared with test data of the burst time, burst pressure, burst hoop strains, and hoop strain rates. Based on the behaviors from the separate effects test, BISON calculations satisfactorily predicted the cladding deformation behaviors. Sensitivity analysis was conducted to identify highly influential mechanical properties responsible for the cladding failure behavior during the MBT experiments. The results highlight the significance of the cladding's mechanical strength in governing cladding strain, followed by the significance of the mechanical interactions between the pellet and the cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Budget Constrained Machine Learning for Early Prediction of Adverse Outcomes for COVID-19 Patients

Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan. Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features. Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin. Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Cadena Pico, JoseE.↗
DOE OSTI2025

Impact of Irradiation on Corrosion Performance of Hybrid Organic/Inorganic Coatings on Austenitic Stainless Steel

The effects of gamma radiation on the performance of two corrosion-resistant coatings applied to stainless-steel 304L (SS304L) surfaces are presented. Specifically, the ability of the coatings to mitigate corrosion of SS304L surfaces as a function of the dose received (0–1300 Mrad) and dose rate (176 compared to 1054 rad/s) is evaluated using electrochemical methods, spectroscopy, and microscopy. Coating A, an organic/inorganic hybrid coating consisting of a two-part silica ceramic component and a polymer linker was evaluated in comparison to Coating B, which utilized Coating A as a topcoat for a commercial, off-the-shelf, Zn-rich primer. Post irradiation, Coating A demonstrated some corrosion protection following exposure to low levels of gamma radiation, but coating degradation occurred with an increased exposure dose and resulted in isolated regions of corrosion initiation. For Coating B, greater corrosion resistance was observed compared to Coating A due to the sacrificial nature of the Zn at elevated doses of gamma radiation. No effect of the dose rate (for the single dose examined) was observed for either coating. It is proposed for Coating B that as the polymer coating thermally degrades above 250 °C (bond scission of the polymer occurs), the remaining Zinc layer adhered to the SS304L post-irradiation enables enhanced corrosion resistance as compared to Coating A, which displays solely polymer degradation. The results presented herein establish an understanding of coating behavior with radiation exposure, specifically the relationship between corrosion coating performance and radiation dose, and can inform ageing and lifetime management for various applications.

degradation↗
DOE OSTI2020

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. Finally, the results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Biobased Poly(dodecylene Furanoate) with Inherent Advantages in Performance and Circularity

Biobased polymers are gaining traction toward more sustainable flexible-film packaging, yet overcoming trade-offs between their performance properties and end-of-life (EoL) options still remains a challenge. Here, it is shown that biobased poly(dodecylene 2,5-furanoate) (PDDF), synthesized via both step-growth polycondensation and chain-growth ring-opening polymerization methods, exhibits advantages not only in gas barrier properties but also in EoL options due to its biodegradability and closed-loop chemical circularity. Specifically, PDDF displays significantly lower oxygen and carbon dioxide permeability than commercial poly(butylene adipate-co-terephthalate) (PBAT) and linear low-density polyethylene , alongside a markedly higher modulus (by ≈3 ×) and reduced water vapor transmission rate compared to PBAT. This superior performance is attributed to the inherently rigid, polar, H-bonding furan rings that enhance chain interaction, packing and crystallinity and thus reduce free volume impeding gas diffusion, while the long hydrophobic dodecylene segments inhibit water permeation. Furthermore, PDDF can be recycled back to its cyclic monomer by base-catalyzed depolymerization or diester and diol monomers by simple methanolysis. These superior barrier properties, coupled with biodegradation and closed-loop circularity, highlight the potential of the biobased PDDF as a more sustainable alternative for packaging.

Poly(dodecylene 2,5-furanoate) (PDDF)↗
DOE OSTI2023

Enter Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics

Identifying small molecules (e.g., metabolites) is key towards driving scientific advancement in metabolomics, and gas chromatography–mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g. retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exists, none characterize the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending the traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size, however FDR estimation error typically worsens as the set of reference compounds diminishes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Revisiting nucleosynthesis in globular clusters: The case of NGC 2808 and the role of He and K

Context. Motivated by recent reports concerning the observation of limited enrichment in He but excess K in stars of globular clusters, we revisit the H-burning conditions that lead to the chemical properties of multiple stellar populations in these systems. Aims. In particular, we are interested in correlations of He and K with other elements, such as O, Na, Al, Mg and Si, reported in stars of NGC 2808. Methods. We performed calculations of nucleosynthesis at constant temperature and density, exploring the temperature range of 25 to 200 × 10 6 K (25 to 200 MK), using a detailed nuclear reaction network and the most up-to-date nuclear reaction rates. Results. We find that Mg is the most sensitive “thermometer” of hydrostatic H-burning conditions, pointing to a temperature range of 70–80 MK for NGC 2808, while He is a lesser – but not negligible – constraint. Potassium can be produced at the levels reported for NGC 2808 at temperatures > 180 MK and Si at T > 80 MK. However, in the former temperature range Al and Na are totally destroyed and no correlation can be obtained, in contrast to the reported observations. Thus, none of the putative polluter sources proposed so far seem to satisfy the ensemble of nucleosynthesis constraints.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Improved age- and gender-specific radiation risk models applied on cohorts of Swedish patients

The aim of this study is to implement lifetime attributable risk (LAR) predictions for radiation induced cancers for Swedish cohorts of patients of various age and sex, undergoing diagnostic investigations by nuclear medicine methods. Methods: Calculations are performed on Swedish groups of patients with Paget's disease and with bone metastases from prostatic cancer and diagnosed with bone scintigraphy with an administration of 500 MBq 99m Tc-phosphonate. Results: The inclusion of patient survival rates into the calculations lowers the induced radiation cancer risk, as it takes into account that cohorts of patients have shorter predicted survival times than the general population. Conclusion: LAR estimations could be valuable for referring physicians, nuclear medicine physicians, nurses, medical physicists, radiologists, and oncologists and as well as ethical committees for risk estimates for specific subgroups of patients. Caution is however advised with respect to application of LAR predictions to individuals (because of individual sensitivities, circumstances, etc.).

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Analysis and Compensation of Tx and Rx IQ Imbalances in Uplink IM-OFDMA Systems

Index modulation-based orthogonal division multiple access (IM-OFDMA) has recently been proposed as a potential technique for future wireless communication systems due to its superior spectral efficiency and error performance over conventional multiple access schemes. However, its performance is still under investigation by researchers in light of a variety of scenarios and assumptions. Following this direction, in this Article, the individual and joint effects of the transmitter (Tx) and receiver (Rx) in-phase and quadrature imbalances (IQI) on the bit-error-rate (BER) performance of the uplink IM-OFDMA scheme are investigated by considering whether each user's RF front-end is identical or not. Moreover, to reduce the detrimental effect of IQI, a preamble-based estimation and compensation method is proposed for IM-OFDMA. Closed-form expressions for the average BER of IM-OFDMA are obtained by considering the physical effect of the Tx and Rx IQI. Also, using Monte Carlo simulations, the derived expressions are verified under different system configurations. Furthermore, analytical and simulation results reveal that Tx and Rx IQI cause an error floor in the BER performance of IM-OFDMA schemes. However, the proposed estimation and compensation method not only reduces the impact of IQI but also leads to a better BER performance compared to the case of no IQI, which mainly refers to the frequency diversity caused by the IQI.

42 ENGINEERING↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Numerical simulation of a full-loop circulating fluidized bed under different operating conditions

Both experimental and computational studies of the fluidization of high-density polyethylene (HDPE) particles in a small-scale full-loop circulating fluidized bed are conducted. Experimental measurements of pressure drop are taken at different locations along the bed. The solids circulation rate is measured with an advanced Particle Image Velocimetry (PIV) technique. The bed height of the quasi-static region in the standpipe is also measured. Comparative numerical simulations are performed with a Computational Fluid Dynamics solver utilizing a Discrete Element Method (CFD-DEM). This paper reports a detailed and direct comparison between CFD-DEM results and experimental data for realistic gas-solid fluidization in a full-loop circulating fluidized bed system. The comparison reveals good agreement with respect to system component pressure drop and inventory height in the standpipe. In addition, the effect of different drag laws applied within the CFD simulation is examined and compared with experimental results.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Morphological Modulation of TiO 2 Nanotube via Optimal Anodization Condition for Solar Water Oxidation

With the depletion of fossil fuels and the rising global demand for energy, photoelectrochemical (PEC) water splitting presents a promising solution to avert an energy crisis. Titanium dioxide (TiO 2 ), an n -type semiconductor, has gained popularity as a photoanode due to its remarkable PEC properties. Nevertheless, inherent challenges such as a wide band gap (~3.2 eV), charge recombination, and slow oxygen evolution reaction (OER) rates at the surface limit its practical application by constraining light absorption. To overcome these limitations, we have developed TiO 2 nanotubes (NTs) using a facile anodization method. This study examines the impact of anodization growth parameters on solar water oxidation performance. Specifically, TiO 2 NTs with modified anodization time (referred to as TiO 2 -6) showed a 3.5-fold increase in photocurrent density compared to the as-grown TiO 2 NTs. Furthermore, electrochemical analyses, such as electrochemical impedance spectroscopy (EIS), indicated a significant decrease in charge transfer resistance following the adjustment of on-off anodization time. Additionally, the TiO 2 -6 photoanode demonstrated a higher electrochemically active surface area (ECSA) than other samples. Therefore, optimal nanostructuring parameters are crucial for enhancing the PEC properties of TiO 2 NTs. Overall, our findings offer valuable insights for fabricating high-quality TiO 2 NTs photoanodes, contributing to developing efficient PEC systems for sustainable energy production.

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

Advanced low blaze angle x-ray gratings via nanoimprint replication and plasma etch

We developed a new method of making ultra-low blaze angle diffraction gratings for x-ray applications. The method is based on reduction of the blaze angle of a master grating by nanoimprint replication followed by a plasma etch. A master blazed grating with a relatively large blaze angle is fabricated by anisotropic wet etching of a Si single crystal substrate. The surface of the master grating is replicated by a polymer material on top of a quartz substrate by nanoimprinting. Then a second nanoimprinting is performed using the 1st replica as a mold to replicate the saw-tooth surface into a resist layer on top of a Si grating substrate. A reactive ion etch is used to transfer the grating grooves into the Si substrate. The plasma etch provides reduction of the groove depth by a factor defined by the ratio of the etch rates for the resist and Si. We demonstrate reduction of the blaze angle of a master grating by a factor of 5 during fabrication of a 200 lines/mm blazed grating with a blaze angle of 0.2°. We investigated the quality and performance of the fabricated low blaze angle gratings and evaluate process accuracy and reproducibility. The new blaze angle reduction method preserves the planarity of the optical surface of the grating substrate and at the same time provides improvement in the grating groove quality during the reduction process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Improving the cross correlation method of indirect flow measurement by water injection and modeling - 245

This paper presents a technique to improve the Cross Correlation Function (CCF) method for indirect flow measurement. Unlike other methods, the CCF method does not need a specific instrument or tool for flow measurements. Instead, the delay time between two sensors can be derived by applying the CCF. A water flow loop was constructed to investigate the CCF method for flow measurement. Because the inherent process temperature variations were small when compared to significant amounts of measurement and process noise, inaccurate flow measurements were obtained. The performance of the CCF method could be improved by increasing the system signal-to-noise ratio, which is the process noise divided by the measurement noise. For this reason, a periodic injection of different temperature water was used to increase the process temperature variation. The results were favorable. However, several design variables needed to be selected. To assist, a CFD simulation was utilized to optimize the distance between sensors and injection point, the rate of injection flow, and other related variables. The results show that CCF optimization of these variables produces better flow measurement performance over a range of flow velocities. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗