Extraction of Next-to-Next-to-Leading-Order Parton Distribution Functions from Lattice QCD Calculations
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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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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Synthesizing Lithium Sorbents Using Acid Mine Drainage Solids
In this study, we prepared HMO from coal mine drainage (CMD) solids and examined the practicability of recovering Li from oil and gas produced waters and geothermal brines. We aim to innovate high-value added products from the existing extract REEs and CMs processing chain of legacy byproducts associated with coal production. CMD solids, which are precipitates collected from exhausted limestone gravels of passive CMD treatment beds, are enriched in rare earth elements (REEs) and other critical minerals (CMs), and therefore, considered as a viable alternative source for REEs/CMs to supply the Nation’s demands. A patented extraction process developed by NETL (TREE) has been demonstrated as a cost-effective and efficient approach to recover REEs/CMs from CMD solids. Here, we selected residuals of CMD solids from the TREE process that are enriched in manganese (Mn) (>20% w/w) and successfully prepared LMO using a solid-state method modified from Yoshizuka et al. (2002).
Cheng, Chin-Min↗
High Throughput Laser Processing for Enhanced Battery Performance and Manufacturing
For widespread adoption of electric vehicles, lithium-ion batteries (LiBs) need to achieve energy densities of >275 Wh/kg, cell costs less than $100/kWh, and charge to more than 80% capacity within 15 minutes. Recently, it has been shown that introducing a secondary pore network in thick battery electrodes enables higher extracted capacity without a severe reduction in charge/discharge rate performance. Secondary pore networks consisting of ordered arrays of microstructures reduce tortuosity of Li+-ion transport deep into the thick electrodes, diminishing detrimental concentration gradients within the cell. Ultrafast (femtosecond)-pulsed laser ablation is a promising method to introduce such micro pores or channels in thick battery electrodes as it allows for precise control of pattern geometries, results in minimal damage to the electrode, and can be introduced into existing roll-to-roll electrode manufacturing lines. We have explored laser patterning to create secondary pore networks in a variety of battery electrode materials for both anode and cathode applications and characterized the laser-material interaction parameters. In addition, advanced materials characterization techniques (SEM-EDS, XRD) were used to explore whether ultrafast laser ablation affects the remaining electrode materials' morphology and structure. Furthermore, we have translated lab-scale laser ablation to high-throughput roll-to-roll processing at industrially relevant scales. Finally, we worked with Argonne National Laboratory, developers of the battery cost-analysis package BatPac, to estimate the additional manufacturing costs (CapEx and OpEx) for incorporation of laser-ablation processing to current battery electrode manufacturing. We estimate that ultrafast laser processing will add only -$1kWh, which for considerable improvements in cell performance, creates a compelling case for industry adoption.
battery↗
Investigation of enhanced CO 2 storage in deep saline aquifers by WAG and brine extraction in the Minnelusa sandstone, Wyoming
Geological CO 2 sequestration in deep saline aquifers has been extendedly investigated to reach the goal of carbon neutral as large amount of CO 2 can be reduced in a short time. Here, this study investigates the feasibility of water-alternating-gas (WAG) injection and brine extraction on enhancing CO 2 storage in a target deep saline aquifer, Minnelusa Sandstone in the Powder River Basin (PRB) of Wyoming. An integrated numerical model is developed to account for four scenarios: (1) CO 2 continuous injection (CI) through one injection well, (2) WAG injection through one injection well, (3) CO 2 CI through one injection well and brine extraction through one producer, (4) WAG injection through one injection well and brine extraction through one producer. Simulation results suggest that WAG injection and brine extraction corporately make it possible to enhance CO 2 injectivity while securing CO 2 storage safety. For instance, WAG injection considerably reduces structural trapping contribution while enhances dissolved and residual trapping contributions. As for brine extraction, it can decrease the maximum averaged reservoir pressure by 37% and 22% for CI and WAG injection, respectively. Besides, sensitive analyses of the operational parameters for the fourth scenarios are performed. Results reveal that, when the amount of total CO 2 injection has been predetermined, CO 2 injection time and rate within each period of WAG should be small whereas it is preferable to have large water injection time and rate. This is to secure desirable dissolved and residual trapping contributions (store CO 2 with safety). Sensitive degree results confirm that CO 2 injection rate, water injection time, and CO 2 injection time of WAG injection exhibit significant effects on CO 2 storage whereas the impacts of producer bottom-hole pressure (BHP), water injection rate, and CO 2 -water injection time are relatively weaker. This study not only sheds light on achieving double-win goal: enhance CO 2 injectivity and store CO 2 with safety, but also can be a critical reference for other CO 2 storage in deep saline aquifers.
58 GEOSCIENCES↗
Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$
Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. Here this paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.
14 SOLAR ENERGY↗
Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction
Abstract We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM) for uncertainty quantification. By sampling the weights and biases distributions of the BNN in the backward mapper of the VAIM, BNN-VAIM is able to estimate prediction uncertainty associated with each individual solution obtained for an ill-posed inverse problem. We first demonstrate the uncertainty quantification capability of BNN-VAIM in a toy inverse problem. Then, we apply BNN-VAIM to the inverse problem of extracting 8 CFFs from the unpolarized DVCS cross section.
Instruments & Instrumentation↗
Development of a Supported Emulsion Liquid Membrane System for Propionic Acid Separation in a Microgravity Environment
Perstractive fermentation is a good way to increase the productivity of bioreactors. Using Propionibacteria as the model system, the feasibility of using supported emulsion liquid membrane (SELM) for perstractive fermentation is assessed in this study. Five industrial solvents were considered as the solvent for preparing the SELM. The more polar a solvent is, the higher the partition coefficient. However, toxicity of a solvent also increases with its polarity. CO-1055 (industrial decanol/octanol blend) has the highest partition coefficient toward propionic acid among the solvents that has no molecular toxicity toward Propionibacteria. A preliminary extraction study was conducted using tetradecane as solvent in a hydrophobic hollow fiber contactor. The result confirmed that SELM eliminates the equilibrium limitation of conventional liquid-liquid extraction, and allows the use of a non-toxic solvent with low partition coefficient.
Li, Jin↗
Machine Vision for Video-Based Material Strain Extraction
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
GeoAI advances in specific landform mapping
Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.
machine learning↗
Surface extraction from micro-computed tomography data for additive manufacturing
Not Available
Shen, Weijun↗
Ultrafast Electron Transfer in Au-Cyanobacteria Hybrid for Solar to Chemical Production
The rise of inorganic-biohybrid organisms for solar-to-chemical production has spurred mechanistic investigations into the dynamics of the biotic-abiotic interface to drive the development of next-generation hybrid systems. The model system, cyanobacteria-gold nanoparticle hybrids, combines a light harvester with a photosynthetic bacterium to drive the reduction of CO2 to glycerol with improved efficiency and increased glycerol production by 14.6%, in comparison to cyanobacteria only. In this work, we report insights into this unique photochemical behavior and propose a charge-transfer pathway from Au nanoparticle to cyanobacteria. Transient absorption (TA) spectroscopy revealed that photoexcited electron transfer rates are on the order of a few ps to the potential electron acceptor in photosystem II. This work represents a promising platform to utilize a conventional spectroscopic methodology to extract insights from more complex biotic-abiotic hybrid systems.
charge transfer↗
Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey
We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.
(galaxies:) quasars: general↗
Electrospun Nanofiber Electrodes for High and Low Humidity PEMFC Operation
MEAs with nanofiber mat electrodes containing Pt/C catalyst and Nafion binder were fabricated and evaluated. The electrodes were prepared by electrospinning a solution of catalyst powder, salt-form Nafion (with Na + , Li + , or Cs + as the sulfonic acid counterion), and a carrier polymer of either polyethylene oxide or poly(acrylic acid). The carrier polymer was extracted prior to MEA testing by a hot water soaking step. The resulting fibers were 15%–17% porous, with a core–shell-like morphology (a coating of primarily Nafion on the fiber surface). MEAs with anode/cathode catalyst loadings of 0.1 mg Pt cm −2 each and a Nafion 211 membrane produced high power at both high and low relative humidity (RH) conditions in H 2 /air fuel cell tests, e.g., a maximum power density of 919 mW cm −2 at 100% RH and 832 mW cm −2 at 40% RH for a test at 80 °C and 200 kPa abs . The presence of nm-size pores within the fibers trapped water via capillary condensation during low RH feed gas testing, thus maintaining a high proton conductivity of the Nafion binder in the anode and cathode while minimizing/eliminating ionic isolation of catalyst particles in low water content, poorly conductive binder.
25 ENERGY STORAGE↗
Development of an autonomous solvent extraction system to isolate astatine-211 from dissolved cyclotron bombarded bismuth targets
Abstract Cyclotron-produced astatine-211 ( 211 At) shows tremendous promise in targeted alpha therapy (TAT) applications due to its attractive half-life and its 100% α-emission from nearly simultaneous branched alpha decay. Astatine-211 is produced by alpha beam bombardment of naturally monoisotopic bismuth metal ( 209 Bi) via the ( α, 2n ) reaction. In order to isolate the small mass of 211 At (specific activity = 76 GBq·µg −1 ) from several grams of acid-dissolved Bi metal, a manual milliliter-scale solvent extraction process using diisopropyl ether (DIPE) is routinely performed at the University of Washington. As this process is complex and time consuming, we have developed a fluidic workstation that can perform the method autonomously. The workstation employs two pumps to concurrently deliver the aqueous and organic phases to a mixing tee and in-line phase mixer. The mixed phases are routed to a phase settling reservoir, where they gravity settle. Finally, each respective phase is withdrawn into its respective pump. However, development of a phase boundary sensor, placed in tandem with the phase settling reservoir, was necessary to communicate to the system when withdrawal of the denser aqueous phase was complete (i.e., the intersection of the two phases was located). The development and optimization of the autonomous solvent extraction system is described, and the 211 At yields from several ~1.1 GBq-level 211 At processing runs are reported.
211At↗
Revealing short- and long-range Li-ion diffusion in Li 2 MnO 3 from finite-temperature dynamical mean field theory
Li 2 MnO 3 is a key component of Li-excess layered cathodes of the form (1 − x), LiMO 2 + x, Li 2 MnO 3 (M = Mn, Ni, Co, …), yet its role in setting Li-ion transport limitations remains under debate. Here, in this study, we combine DFT+U, finite-temperature DFT+DMFT with a continuous-time quantum Monte Carlo impurity solver, and nudged-elastic-band (NEB) calculations to study Li + migration in paramagnetic Li 2 MnO 3 in the presence of a single Li vacancy. Evaluating DMFT total energies along the DFT+U NEB geometries reveals that dynamical correlations strongly renormalize the lowest-barrier processes, reducing the activation energies to E a = 0.18 eV for the shortest-range hop and E a = 0.50 eV for the next-lowest (transport-controlling) step. The 0.18 eV barrier quantitatively reproduces the short-range activation energy from µ+SR, while the 0.50 eV barrier is consistent with the long-range transport scale extracted from ac-impedance measurements. This single-vacancy, paramagnetic DMFT description thus provides a unified interpretation of local and macroscopic probes without invoking clustered vacancy configurations or strong extrinsic disorder, consistent with nearly stoichiometric Li 2 MnO 3 powders. More broadly, our results highlight finite-temperature dynamical correlations as an essential ingredient for predicting ionic migration energetics in correlated oxide electrodes.
Lee, Alex Taekyung [University of Illinois, Chicag↗
Tritium Extraction from FLiBe using a Permeation Against Vacuum (PAV): Impact of Surface Potentials, Currents, and Coupling PAV to Sacrificial Anodes
Savannah River National Laboratory (SRNL) is exploring a concept of pairing a LiF-BeF2 (FLiBe) immersed Permeation Against Vacuum (PAV) tritium extraction system with a sacrificial anode. Several fusion concepts utilize FLiBe for tritium breeding, and breeding of tritium would occur from neutron interactions with the Li component, creating corrosive TF and/or T2. In absence of a redox buffered system, the TF would be able to attack containment materials until neutralized. One way to neutralize TF is to contact it with an active metal such as Be or Li which would preferentially combine with the fluorine in the TF to form BeF2 or LiF, respectively, and resulting in T2. When the active metal (e.g., Be) is oxidized, it will provide electrons, when T in TF is reduced and forms T2, it needs electrons. SRNL proposes to electrically link these two reactions and with the PAV to help drive the reactions faster than on their own and potentially increase T2 dissociation. In a soluble redox salt buffered system, it is expected that with sufficient buffering, T2, may form more rapidly and the core components would be more protected, but the soluble redox salt buffer would need to be occasionally regenerated, which could be accomplished by again contacting the salt with an active metal such as Be or Li, although in this case the cathodic reaction would be electrons coming from the shift in oxidative state of the redox buffer. The exploration of potential and current effects on the PAV and impacts of redox agents will be discussed.
Olson, Luke C.↗
Emergent Nanostructure and Ion Transport in Polyzwitterion/Polyanion Blends
We investigated blends of poly(1-(3-sulfonatopropyl)-2-vinylpyridinium) (P2VPPS) and poly(lithium (trifluoromethane)sulfonimide methacrylate) (poly(MTFSI)Li) at varying molar ratios to gain a mechanistic understanding of ionic conductivity in a miscible polyzwitterion/polyanion system. This dataset contains the raw numerical data corresponding to the figures in the manuscript. The data files include the following information: (1) Experimental Data – includes X-ray and neutron scattering measurements, broadband dielectric spectroscopy (BDS) data, extracted DC conductivity values, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results, extracted glass transition temperatures, etc. (2) CGMD Data – includes molecular dynamics (MD) trajectory files and computed structural correlations. All data files are organized/named according to the figure numbers in the manuscript.
36 MATERIALS SCIENCE↗