A Chemically Orthogonal Hole Transport Layer for Efficient Colloidal Quantum Dot Solar Cells
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99 GENERAL AND MISCELLANEOUS↗
SEARCH · Engineering Papers
Results for “map improvement”
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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Fluorination effect for stabilizing cationic and anionic redox activities in cation-disordered cathode materials
We see that cation-disordered Li-excess cathodes with oxygen redox reactions are promising candidates for high-energy-density Li ion batteries. Nevertheless, the oxygen redox process that is required for the high capacity often comes with the oxygen loss, which leads to severe capacity degradation and voltage decay. In this work, we have successfully synthesized a series of Li-excess cation-disordered cathodes (Li 1.2 Mn 0.4+x Ti 0.4-x O 2-x Fx) (0 ≤ x ≤ 0.2) with different fluorine (F) contents. The electrochemical performance results show that the Li 1.2 Mn 0.55 Ti 0.25 O 1.85 F 0.15 (LMTOF0.15) exhibits the highest reversible capacity (275 mAh g -1 , under 30 mA g -1 ), cyclability, and voltage retentions. The mapping of resonant inelastic X-ray scattering (mRIXS) and differential electrochemical mass spectroscopy (DEMS) results reveal that the fluorination enhances the reversible lattice oxygen redox reaction while suppressing irreversible gas release and surface reactions. The X-ray Absorption Spectroscopy (XAS) during the initial two cycles shows that F-substitution alleviates the reduction of the Mn valence state during the whole (dis)charge processes in the bulk and at the surface of the material, results in higher average discharge voltage. In addition, the introduction of F improves the structural stability and suppresses local lattice distortion of the material. Therefore, LMTOF 0.15 is able to cycle with smaller polarization, less interfacial side reaction and Mn dissolution, and therefore results in enhanced cyclability. This work provides a comprehensive understanding of the fluorination effect on the cationic and anionic redox activities in cation-disordered Li-excess cathodes.
25 ENERGY STORAGE↗
X-ray fluorescence microscopy methods for biological tissues
Abstract Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data-acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pretreatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.
Pushie, M. Jake (ORCID:0000000174945427)↗
Air quality and health impacts of the 2020 wildfires in California
Abstract Background Wildfires in 2020 ravaged California to set the annual record of area burned to date. Clusters of wildfires in Northern California surrounded the Bay Area covering the skies with smoke and raising the air pollutant concentrations to hazardous levels. This study uses the Fire Inventory from the National Center for Atmospheric Research database and the Community Multiscale Air Quality model to estimate the effects of wildfire emissions on air quality during the period from August 16 to October 28 of 2020. In addition, low-cost sensor data for fine particulate matter (PM 2.5 ) from the PurpleAir network is used to enhance modeled PM 2.5 concentrations. The resulting impacts on ozone and PM 2.5 are used to quantify the health impacts caused by wildfires using the Benefits Mapping and Analysis Program – Community Edition. Results Wildfire activity significantly increased direct PM 2.5 emissions and emissions of PM 2.5 and ozone precursors. Direct PM 2.5 emissions surged up to 38 times compared to an average day. Modeling results indicated that wildfires alone led to a rise in ozone daily maximum 8-h average by up to 10 ppb and exceeded PM 2.5 air quality standards in numerous locations by up to 10 times. While modeled PM 2.5 concentrations were lower than measurements, correcting these with PurpleAir data improved the accuracy. The correction using PurpleAir data increased estimates of wildfire-induced mortality due to PM 2.5 exposure by up to 16%. Conclusions The increased hospital admissions and premature mortality attributed to wildfires were found to be comparable to the health impacts avoided by strategies aimed at meeting ozone and PM2.5 air quality standards. This suggests that widespread wildfire emissions can negate years of efforts dedicated to controlling air pollution. The integration of low-cost sensor data proved invaluable in refining the estimates of health impacts from PM2.5 resulting from wildfires.
54 ENVIRONMENTAL SCIENCES↗
On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation
Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often requires large amounts of simulation data that may not be available in the case of computationally expensive systems. In this paper, to alleviate this issue for uncertainty propagation, we explore the application of transfer learning techniques using training data generated from both high- and low-fidelity models. We explore two strategies for coupling these two datasets during the training procedure, namely, the standard transfer learning and the bi-fidelity-weighted learning. In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data. The high-fidelity data are then used to adapt the parameters of the upper layer(s) of the low-fidelity network, or train a simpler neural network to map the output of the low-fidelity network to that of the high-fidelity model. In the latter approach, the entire low-fidelity network parameters are updated using data generated via a Gaussian process model trained with a small high-fidelity dataset. The parameter updates are performed via a variant of stochastic gradient descent with learning rates given by the Gaussian process model. Using three numerical examples, we illustrate the utility of these bi-fidelity transfer learning methods where we focus on accuracy improvement achieved by transfer learning over standard training approaches.
97 MATHEMATICS AND COMPUTING↗
Dynamic Heat Flow and Current Distribution Analysis in the Bottom Anode of an Electric Arc Furnace Using Fiber-Optic Sensors
A reliable method for monitoring bottom anode wear during DC Electric Arc Furnace (DC-EAF) operation is of critical importance for safe and efficient steel production. Underestimation of bottom wear poses a serious safety risk that must be avoided, while overestimation of bottom wear also poses challenges, as premature anode replacement is expensive and affects EAF productivity. Previously, we demonstrated that fiber-optic sensors can be successfully deployed to create a spatially distributed temperature map to monitor the health of the anode. The present work explores the heat flow and current density distribution in bottom anode pins to predict bottom wear, steel penetration events, and monitor refractory erosion. Small dynamic variations in pin temperature induced by joule heating during arcing also provide a means to observe local current flows in each pin. When mapped, these measurements provide a real-time view of the non-uniform and dynamic current flow in the bottom anode during EAF operation that can affect bottom wear.
Bottom Anode↗
A Fast Algorithm for Computing Zigzag Representatives
Zigzag filtrations of simplicial complexes generalize the usual filtrations by allowing simplex deletions in addition to simplex insertions. The barcodes computed from zigzag filtrations encode the evolution of homological features. Although one can locate a particular feature at any index in the filtration using existing algorithms, the resulting representatives may not be compatible with the zigzag: a representative cycle at one index may not map into a representative cycle at its neighbor. For this, one needs to compute compatible representative cycles along each bar in the barcode. It is known that the barcode for a zigzag filtration with m insertions and deletions can be computed $O(m^ω)$ in time, where $ω < 2.373$ is the matrix multiplication exponent. However, it is not known how to compute the compatible representatives so efficiently. For a non-zigzag filtration, the classical matrix-based algorithm provides representatives in $O(m^3)$ time, which can be improved to $O(m^ω)$. However, no known algorithm for zigzag filtrations computes the representatives with the $O(m^3)$ time bound. We present an $O(m^3 n)$ time algorithm for this problem, where $n ≤ m$ is the size of the largest complex in the filtration.
Persistent homology↗
Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic
In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Assessing Nontrivial Topology in Weyl Semimetals by Dichroic Photoemission
The electronic structure of Weyl semimetals features Berry flux monopoles in the bulk and Fermi arcs at the surface. While angle-resolved photoelectron spectroscopy (ARPES) is successfully used to map the bulk and surface bands, it remains a challenge to explicitly resolve and pinpoint these topological features. Here we combine state-of-the-art photoemission theory and experiments over a wide range of excitation energies for the Weyl semimetals TaAs and TaP. Further, our results show that simple surface-band-counting schemes, proposed previously to identify nonzero Chern numbers, are ambiguous due to pronounced momentum-dependent spectral weight variations and the pronounced surface-bulk hybridization. Instead, our findings indicate that dichroic ARPES provides an improved approach to identify Fermi arcs but requires an accurate description of the photoelectron final state.
36 MATERIALS SCIENCE↗
Refining HPCToolkit for application performance analysis at exascale
As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.
Adhianto, Laksono↗
Experimental studies at low $Q^2$ of the spin structure of the nucleon at Jefferson Lab
We summarize the experimental program of Jefferson Lab that studies the nucleon spin structure at low $Q^2$. This program completes the precise experimental mapping of the nucleon spin structure functions $g_1(\nu, Q^2)$ and $g_2(\nu, Q^2)$ and their moments started at SLAC, CERN and DESY at high $Q^2$, and continued at Jefferson Lab at intermediate $Q^2$. The results presented cover the domain where Chiral Effective Field Theory ($\chi$EFT) should describe the strong interaction. They provide a comprehensive set of benchmark measurements for $\chi$EFT. The preliminary conclusion is that nucleon spin structure data are still challenging for $\chi$EFT in spite of the notable improvements in these calculations.
Deur, Alexandre↗
From cytoplasm to lumen—mapping the free pools of protein subunits of three photosynthetic complexes using quantitative mass spectrometry
The phycobilisome (PBS) captures light energy and transfers it to photosystem I (PSI) and photosystem II (PSII). Which and how many copies of protein subunits in PBSs, PSI, and PSII remain unbound in thylakoids are unknown. Here, quantitative mass spectrometry (QMS) was used to quantify substantial pools of free extrinsic subunits of PSII and PSI. Interestingly, the membrane intrinsic PsaL is 3-fold higher than PsaA/B. This scenario complements the static structures of these complexes as revealed by X-ray crystallography and cryo-EM. Furthermore, the ratios of ApcG and photoprotective OCP over PBS indicate a pool of extra ApcG. The 2.5 ratio of CpcG-PBS over CpcL-PBS improves our understanding of these light-harvesting complexes involved in energy capture and photoprotection in cyanobacteria.
cyanobacteria↗
Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison
The replacement of classical force fields (FFs) with novel neural-network-based frameworks is an emergent topic in molecular dynamics (MD) simulations. In contrast to classical FFs, which have proven their capability to provide insights into complex soft matter systems at an atomistic resolution, the machine learning (ML) potentials have yet to demonstrate their applicability for soft materials. However, the underlying philosophy, which is learning the energy of an atom in its surrounding chemical environment, makes this approach a promising tool. In particular for the exploration of novel chemical compounds, which have not been considered in the original parametrization of classical FFs. In this article, we study the performance of the ANI-2x ML model and compare the results with those of two classical FFs, namely, CHARMM27 and the GROMOS96 43a1 FF. We explore the performance of these FFs for bulk water and two model peptides, trialanine and a 9-mer of the α-aminoisobutyric acid, in vacuum and water. The results for water describe a highly ordered water structure, with a structure similar to those using ab initio molecular dynamics simulations. The energy landscape of the peptides described by Ramachandran maps show secondary structure basins similar to those of the classical FFs but differ in the position and relative stability of the basins. Details of the sampled structures show a divergent performance of the different models, which can be related either to the short-ranged nature of the ML potentials or to shortcomings of the underlying data set used for training. These findings highlight the current state of the applicability of ANI-2x ML potential for MD simulations of soft matter systems. Simultaneously, they provide insights for future improvements of current ML potentials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Development of an MKID frequency-to-pixel LED mapper for SPT-3G+
SPT-3G+ is the next-generation camera for the South Pole Telescope (SPT). SPT is designed to measure the cosmic microwave background (CMB) and the mm/sub-mm sky. The planned focal plane consists of 34,000 microwave kinetic inductance detectors (MKIDs), divided among three observing bands centered at 220, 285, and 345 GHz. Each readout line is designed to measure 800 MKIDs over a 500 MHz bandwidth, which places stringent constraints on the accuracy of the frequency placement required to limit resonator collisions that reduce the overall detector yield. To meet this constraint, we are developing a two-step process that first optically maps the resonance to a physical pixel location, and then next trims the interdigitated capacitor (IDC) to adjust the resonator frequency. We present a cryogenic LED apparatus operable at 300 mK for the optical illumination of SPT-3G+ detector arrays. We demonstrate integration of the LED controls with the GHz readout electronics (RF-ICE) to take data on an array of prototype SPT-3G+ detectors. We show that this technique is useful for characterizing defects in the resonator frequency across the detector array and will allow for improvements in the detector yield.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Cryogenic EM Across Length Scales for Li Metal Anode Batteries
An interfacial understanding is necessary for developing strategies to commercialize high-energy density rechargeable lithium metal anode batteries, as currently, the lithium anode/electrolyte interface is unstable with prolonged cycling. We have used several strategies to improve the cycling performance of lithium metal anodes, including reducing the parasitic reactions between lithium metal and the electrolyte, and improving the electrodeposited lithium metal morphology. These strategies have generated unconclusive electrochemical data, that has required the need for nanoscale interfacial characterization of these solid-liquid interfaces. Our team has used the cryogenic transfer workflow developed by Leica in collaboration with cryo-SEM/FIB tools by Thermo Fisher Scientific to cross-section lithium metal anodes and intact coin cell batteries to observe the interfacial structures, lithium morphology, and failure mechanisms relative to changes in electrode contract pressure and electrolyte chemistry. Cross-sectional SEM images and EDS maps of the lithium metal anodes have provided a better understanding of the electrodeposited lithium morphology, quantity of 'dead' lithium metal, and quantity of solid electrolyte interphase material that has formed alongside the lithium metal. In understanding lithium metal battery failure at the system level, we used a cryogenic stage in a laser plasma FIB to cross-section through the coin cell's cap for imaging/mapping the entire battery stack under cryogenic conditions. The tools, methods, and results of these studies will be detailed in this presentation.
characterization↗
Atomic-Scale View of Redox Induced Changes for Monolayer MoO x on α-TiO 2 (110) with Chemical-State Sensitivity
Supported molybdenum oxide (MoO x ) plays an important role in catalytic transformations from alcohol dehydrogenation to transesterification. During these reactions, Mo and oxygen surface species undergo structural and chemical changes. A detailed, chemical-state specific, atomic-scale structural analysis of the catalyst under redox conditions is important for improving catalytic properties. In this study, monolayer of MoO x grown on α-TiO 2 (110) by atomic-layer deposition is analyzed by X-ray standing wave (XSW) excited X-ray photoelectron spectroscopy (XPS). The chemical shifts for Mo 2p 3/2 and O 1s peaks are used to distinguish Mo 6+ from Mo 4+ and surface O from bulk O. Excitation of XPS by XSW allows pin-pointing the location of these surface species relative to the underlying substrate lattice. Finally, measured 3D composite atomic density maps for the oxidized and reduced interfaces compare well with our density functional theory models, and collectively create a unique view of the redoxdriven dynamics for this complex catalytic structure.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama
Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.
54 ENVIRONMENTAL SCIENCES↗
Cross-correlation of the extragalactic gamma-ray background with the thermal Sunyaev-Zel’dovich effect in the cosmic microwave background
Cosmic rays in galaxy clusters are unique probes of energetic processes operating with large-scale structures in the Universe. Precise measurements of cosmic rays in galaxy clusters are essential for improving our understanding of nonthermal components in the intracluster medium (ICM) as well as the accuracy of cluster mass estimates in cosmological analyses. In this paper, we perform a cross-correlation analysis with the extragalactic gamma-ray background and the thermal Sunyaev-Zeldovich (tSZ) effect in the cosmic microwave background. The expected cross-correlation signal would contain rich information about the cosmic-ray-induced gamma-ray emission in the most massive galaxy clusters at $\textit{z}$ ~ 0.1 – 0.2 . We analyze the gamma-ray background map with 8 years of data taken by the Large Area Telescope onboard Fermi satellite and the publicly available tSZ map by Planck. We confirm that the measured cross-correlation is consistent with a null detection, and thus it enables us to put the tightest constraint on the acceleration efficiency of cosmic-ray protons at shocks in and around galaxy clusters. In this work, we find the acceleration efficiency must be below 5% with a 2σ confidence level when the hydrostatic mass bias of clusters is assumed to be 30%, and our result is not significantly affected by the assumed value of the hydrostatic mass bias. Our constraint implies that the nonthermal cosmic-ray pressure in the ICM can introduce only a≲3% level of the hydrostatic mass bias, highlighting that cosmic rays alone do not account for the mass bias inferred by the Planck analyses. Finally, we discuss future detectability prospects of cosmic-ray-induced gamma rays from the Perseus cluster for the Cherenkov Telescope Array.
79 ASTRONOMY AND ASTROPHYSICS↗