Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “pans”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Microstructure changes during failure of PVDF-based photovoltaic backsheets

The backsheet layer of a solar module provides a safety and environmental barrier to the high voltages running through the photovoltaic (PV) cells and electrical contacts within the core of the module. However, in the past decade, backsheet cracking has become one of the most observed failure modes in PV module field surveys. In this work, the degradation of polyvinylidene fluoride (PVDF)-based backsheets is explored. Backsheet samples are either exposed to accelerated laboratory aging (UV light, heat, and moisture) or collected from the field. Fourier transform infrared and Raman spectroscopy, fragmentation testing, atomic force and scanning electron microscopy, and small-angle neutron scattering are combined to develop an understanding of how chemistry and microstructure evolve during aging. Chemical degradation, surface pitting, polymer phase changes, and anisotropic polymer domains are all observed in aged backsheet samples. The results provide insight into the degradation mechanisms that lead to cracking and field failure of PVDF-based backsheets. The comparison of aged PVDF-based backsheets helps to lay the groundwork for limiting polymer-based failure modes in PV modules.

PVDF↗
DOE OSTI2022

Quantitative methods and modeling to assess COVID–19–interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a “batch” effect to the mixed effects model may be used when a significant “batch” effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

An Ion-Imprinting Derived Strategy to Synthesize Single-Atom Iron Electrocatalysts for Oxygen Reduction

Carbon-based single-atom catalysts (CSACs) have recently received extensive attention in catalysis research. However, the preparation process of CSACs involves a high-temperature treatment, during which metal atoms are mobile and aggregated into nanoparticles, detrimental to the catalytic performance. Herein, an ion-imprinting derived strategy is proposed to synthesize CSACs, in which isolated metal-nitrogen-carbon (Me-N 4 -C x ) moiety covalently binds oxygen atoms in Si-based molecular sieve frameworks. Such a feature makes Me-N 4 -C x moiety well protected/confined during the heat treatment, resulting in the final material enriched with single-atom metal active sites. As a proof of concept, a single-atom Fe-N-C catalyst is synthesized by using this ion-imprinting derived strategy. Finally, experimental results and theoretical calculations demonstrate high concentration of single FeN 4 active sites distributed in this catalyst, resulting in an outstanding oxygen reduction reaction (ORR) performance with a half-wave potential of 0.908 V in alkaline media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ultrafast Early Warning of Heart Attacks through Plasmon‐Enhanced Raman Spectroscopy using Collapsible Nanofingers and Machine Learning

Abstract As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart‐attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)‐driven system with a simple process, low‐cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart‐attack attempts seconds ahead of actual attacks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Latticed-Confined Conversion Chemistry of Battery Electrode

The electrochemical conversion reaction, usually featured by multiple redox processes and high specific capacity, holds great promise in developing high energy rechargeable battery technologies. However, the complete structural change accompanied by spontaneous atomic migration and volume variation during the charge/discharge cycle leads to electrode disintegration and performance degradation, therefore severely restricting the application of conventional conversion-type electrodes. Herein, latticed-confined conversion chemistry is proposed, where the “intercalation-like” redox behavior is realized on the electrode with a “conversion-like” high capacity. By delicately formulating the high-entropy compounds, the pristine crystal structure can be preserved by the inert lattice framework, thus enabling an ultra-high initial Coulombic efficiency of 92.5% and a long cycling lifespan over a thousand cycles after the quasistatic charge–discharge cycle. This lattice-confined conversion chemistry unfolds a ubiquitous insight into the localized redox reaction and sheds light on developing high-performance electrodes toward next-generation high-energy rechargeable batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Correlating Rate-Dependent Transition Metal Dissolution between Structure Degradation in Li-Rich Layered Oxides

Understanding the mechanism of the rate-dependent electrochemical performance degradation in cathodes is crucial to developing fast charging/discharging cathodes for Li-ion batteries. Here, in this work, taking Li-rich layered oxide Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 as the model cathode, the mechanisms of performance degradation at low and high rates are comparatively investigated from two aspects, the transition metal (TM) dissolution and the structure change. Quantitative analyses combining spatial-resolved synchrotron X-ray fluorescence (XRF) imaging, synchrotron X–ray diffraction (XRD) and transmission electron microscopy (TEM) techniques reveal that low-rate cycling leads to gradient TM dissolution and severe bulk structure degradation within the individual secondary particles, and especially the latter causes lots of microcracks within secondary particles, and becomes the main reason for the fast capacity and voltage decay. In contrast, high-rate cycling leads to more TM dissolution than low-rate cycling, which concentrates at the particle surface and directly induces the more severe surface structure degradation to the electrochemically inactive rock-salt phase, eventually causing a faster capacity and voltage decay than low-rate cycling. These findings highlight the protection of the surface structure for developing fast charging/discharging cathodes for Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

NiFe Nanoparticle Nest Supported on Graphene as Electrocatalyst for Highly Efficient Oxygen Evolution Reaction

Abstract Designing cost‐efffective electrocatalysts for the oxygen evolution reaction (OER) holds significant importance in the progression of clean energy generation and efficient energy storage technologies, such as water splitting and rechargeable metal–air batteries. In this work, an OER electrocatalyst is developed using Ni and Fe precursors in combination with different proportions of graphene oxide. The catalyst synthesis involved a rapid reduction process, facilitated by adding sodium borohydride, which successfully formed NiFe nanoparticle nests on graphene support (NiFe NNG). The incorporation of graphene support enhances the catalytic activity, electron transferability, and electrical conductivity of the NiFe‐based catalyst. The NiFe NNG catalyst exhibits outstanding performance, characterized by a low overpotential of 292.3 mV and a Tafel slope of 48 mV dec −1 , achieved at a current density of 10 mA cm − 2 . Moreover, the catalyst exhibits remarkable stability over extended durations. The OER performance of NiFe NNG is on par with that of commercial IrO 2 in alkaline media. Such superb OER catalytic performance can be attributed to the synergistic effect between the NiFe nanoparticle nests and graphene, which arises from their large surface area and outstanding intrinsic catalytic activity. The excellent electrochemical properties of NiFe NNG hold great promise for further applications in energy storage and conversion devices.

Chemistry↗
DOE OSTI2026

Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning

Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗
DOE OSTI2024

Composite resonances at a 10 TeV muon collider

We investigate the reach for resonances of the composite Higgs models at a 10 TeV μ + μ – collider with up to 10 ab –1 luminosity. The strong dynamics sector is modeled by the minimal coset SO(5)/SO(4), where vector resonances are in (3, 1) of SO(4) and fermions are in (2, 2). Various production and decay channels are studied. For the spin-1 resonances, the projections are made based on the radiative return and vector boson fusion production channels. The muon collider can cover most of the kinematically allowed mass range and can measure the coupling gρ to percent level. For the fermionic resonances (i.e. the top partners), pair production easily covers the resonance mass below 5 TeV, while single production extends the reach to 6 TeV for a small ξ = 0.015.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Comprehensive characterization of hepatocyte-derived extracellular vesicles identifies direct miRNA-based regulation of hepatic stellate cells and DAMP-based hepatic macrophage IL-1ß and IL-17 upregulation in alcoholic hepatitis mice.

Extracellular vesicles (EVs) have been growingly recognized as biomarkers and mediators of alcoholic liver disease (ALD) in human and mice. Here we characterized hepatocyte-derived EVs (HC-EVs) and their cargo for their biological functions in a novel murine model that closely resembles liver pathology observed in patients with alcoholic hepatitis (AH), the most severe spectrum of ALD. The number of circulating EVs and HC-EVs were significantly increased by 10-fold in AH mice compared to control mice. The miRNA(miR)-seq analysis detected 20 up-regulated and 4 down-regulated miRNAs (P < 0.001-0.05) in AH-HC-EVs. Treatment of murine primary hepatic stellate cells (HSCs) with AH-HC-EVs induced ?-SMA (P < 0.05) and Col1a1 (P < 0.001). Smad7, Nr1d2, and Rora genes which regulate HSC activation were predicted targets of 17 miRs up-regulated in AH-HC-EVs. Among them were miR-27a and miR-181 which upon transfection in HSCs, repressed Nr1d2, the HSC quiescence marker. AH-HC-EVs were also enriched in organelle proteins and mitochondrial DNA (10-fold, P < 0.05) and upregulated IL-1??and IL-17 production by hepatic macrophages (HMs) from AH mice in a TLR9-dependent manner. These results demonstrate HC-EV release is intensified in AH and suggest that AH-HC-EVs orchestrate liver fibrogenesis by directly targeting HSC quiescence transcripts via a unique set of miRNAs and by amplifying HSC activation via DAMP-based induction of profibrogenic IL-1? and IL-17 by HMs.

AH, hepatocyte-derived EVs, miRNAs, DAMPs, HSC act↗
DOE OSTI2021

Flame stability analysis of flame spray pyrolysis by artificial intelligence

Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles through the combustion of an atomized precursor solution; this process has applications in catalysts, battery materials, and pigments. Current limitations revolve around understanding how to consistently achieve a stable flame and the reliable production of nanoparticles. Machine learning and artificial intelligence algorithms that detect unstable flame conditions in real time may be a means of streamlining the synthesis process and improving FSP efficiency. In this study, the FSP flame stability is first quantified by analyzing the brightness of the flame's anchor point. This analysis is then used to label data for both unsupervised and supervised machine learning approaches. The unsupervised learning approach allows for autonomous labeling and classification of new data by representing data in a reduced dimensional space and identifying combinations of features that most effectively cluster it. The supervised learning approach, on the other hand, requires human labeling of training and test data but is able to classify multiple objects of interest (such as the burner and pilot flames) within the video feed. The accuracy of each of these techniques is compared against the evaluations of human experts. Both the unsupervised and supervised approaches can track and classify FSP flame conditions in real time to alert users of unstable flame conditions. This research has the potential to autonomously track and manage flame spray pyrolysis as well as other flame technologies by monitoring and classifying the flame stability.

42 ENGINEERING↗
DOE OSTI2024

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 10 21 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ~0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Estimating and Evaluating Roughness Length and Displacement Height in Heterogeneous Urban Environments

The roughness length (z 0 ) and displacement height (z d ) are essential surface-layer parameters in numerical models (e.g., weather, climate, wall-modeled LES, etc.). This work evaluates the consistency of z 0 and z d estimates from morphometric and anemometric methods using data from two eddy-covariance flux towers (AmeriFlux US-INg and US-INc) in Indianapolis, IN. Results show inconsistencies in estimated z 0 and z d values depending on the chosen method. The two evaluated anemometric methods estimate non-physical values of z d when compared to roughness elements surrounding both towers. Additionally, predictions of mean wind speed using surface-layer similarity theory with morphometric estimates exhibit a bias during near-neutral and stable conditions relative to observations. The overestimation of mean wind speed by surface layer similarity theory is consistent with previous observational and modeling studies in urban areas, suggesting that the application of similarity theories to urban environments may have limitations. Differentiation of vegetation from built structures appears to impact morphometric z 0 and z d estimates, particularly where vegetation is abundant; however, it has little impact on correcting biases in the similarity theory. Specifically, we find that existing similarity theories using morphometric estimates underestimate integral velocity and length scales, and the degree of underestimation depends on the stability conditions. Accounting for the degree of anisotropy in surface-layer turbulence helps reduce the biases between similarity theories and observations during unstable conditions, but not in near-neutral cases. Future work is needed to identify the cause of such biases for near-neutral conditions.

Aerodynamic roughness length↗
DOE OSTI2022

Calibration of cohesive parameters for fracture test simulations of sub-sized bend specimens of AM steels

In this work, crack extensions in sub-sized side-grooved bend specimens of AM steels are examined with changing cohesive parameters. Two-dimensional FE analyses are first conducted to simulate crack extensions by nodal release. The simulation results indicate the near-tip maximum opening stress and separation work rate increase and then slightly decrease for the bend specimens. The hydrogen-charged bend specimens have lower near-tip maximum opening stresses and average separation work rates than those of the uncharged bend specimens. The changing near-tip maximum opening stresses and the separation work rates with increasing crack extension are determined as references for the changing cohesive strengths and energies, respectively. Two-dimensional FE analyses with different changing cohesive parameters are then performed. The simulation results with calibrated cohesive parameters can match well with the test results. The hydrogen-charged specimens have the lower changing cohesive strength vs crack growth curves compared with those of the uncharged specimens. The hydrogen-charged specimens have either lower or similar changing cohesive energy vs crack growth curves compared with those of the uncharged specimens. Finally, a three-dimensional FE analysis is conducted to simulate crack extension by nodal release in one bend specimen to examine the stress triaxialities in front of the growing crack fronts at different locations from the center plane to the planes near the side groove front with increasing crack extension to identify possible physical reasons for the changing cohesive parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detection of early-universe gravitational-wave signatures and fundamental physics

Detection of a gravitational-wave signal of non-astrophysical origin would be a landmark discovery, potentially providing a significant clue to some of our most basic, big-picture scientific questions about the Universe. In this white paper, we survey the leading early-Universe mechanisms that may produce a detectable signal—including inflation, phase transitions, topological defects, as well as primordial black holes—and highlight the connections to fundamental physics. We review the complementarity with collider searches for new physics, and multimessenger probes of the large-scale structure of the Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

3D Modelling of the Lithium Injection Experiment in H-Mode Plasma of EAST

Investigations of lithium transport in H-mode plasma of EAST tokamak have been performed by the three-dimensional fluid code EMC3-EIRENE. The simulation results show that the toroidally-localized lithium injection at the low field side (LFS) leads to a toroidally non-axisymmetric distribution of the Li 1+ and Li 2+ ions. The field line tracing approach is utilized to carry out a detailed study on the distributions of the Li 1+ and Li 2+ ions. It is found that Li 1+ ions mainly reside at the LFS while Li 2+ ions can transport from LFS to the high field side. The relationship between mean free path and connection length for Li 1+ and Li 2+ ions has been discussed. Here, the 3D line-integration images of deuterium and lithium emission spectra modelled by EMC3-EIRENE are in qualitative consistency with the experiment results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗