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DOE OSTI2019

Carbon Dioxide Enhanced Oil Recovery Life Cycle (CELiC) Model

The Carbon Dioxide (CO 2 ) Enhanced Oil Recovery (EOR) Life Cycle (CELiC) Model calculates life cycle greenhouse gas (GHG) emissions for a CO 2 -EOR system, where the user can select one of three sources of the injected CO 2 : (1) extracted from a natural dome, (2) captured from a coal-fired power plant, or (3) captured from a natural gas power plant. The model has several parameters and options to allow the assessment of the system for a wide-array of products—electricity, pipeline CO 2 , crude oil, and refined fuels. The model is also capable of deterministic (i.e., point estimate) and stochastic (i.e., probabilistic) analyses and finally a deterministic time-series analysis that shows the changing GHG emissions for the CO 2 -EOR system over time.

03 NATURAL GAS↗

DOE OSTI2023

Community Resilience Indicator Analysis: Commonly Used Indicators from Peer-Reviewed Research (Updated for Research Published 2003-2021)

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need to establish a data-driven basis for prioritizing locations for TA investment and guiding local emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory (Argonne) with identifying commonly used indicators of community resilience across the landscape of published peer-reviewed research. FEMA and Argonne completed the first Community Resilience Indicator Analysis (CRIA) in 2018 and repeated the process in 2022. The CRIA process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the CRIA research team to ensure the methodologies are: (1) Quantitative, (2) Data and methodology are publicly available, (3) Calculated at the county level or lower, (4) Examine generalized hazard risk (rather than a singular hazard), and (5) Focused on pre-disaster community conditions. After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visual display, conducts a correlation analysis and creates a composite index, the FEMA Community Resilience Index (FEMA CRI). In 2018, the CRIA identified eight resilience and vulnerability assessment methodologies and 20 commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA CRI in 2018 was created from these 20 indicators and was produced for at the county level. The 2022 CRIA updated the literature review to expand the list of methodologies examined and followed the same process, resulting in an analysis of 14 methodologies published between 2003 and 2021 and 22 indicators identified as commonly used (indicators used in five or more of the 14 methodologies). In 2022, the research team produced the FEMA CRI at the county and the census tract levels. To make the CRIA data more accessible and more actionable, each individual indicator and the FEMA CRI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT). RAPT enables emergency managers and community partners to quickly visualize relative differences in potential resilience by county, tribe and census tract. By reviewing the data for each of these 22 indicators individually, emergency managers can gain insights for targeted outreach strategies, planning, mitigation investments and response and recovery operations. Communities, regional governments and others can use this data to better understand potential challenges to resilience. As the social science field of examining and validating indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and community partners with additional data and tools to inform planning, mitigation, response and recovery. It is important to understand that the role of the emergency manager is not to change or to “improve” the data, but to plan appropriately for the community characteristics reflected in the data. These datasets are community characteristics that researchers have identified as important considerations for resilience. For example, people with disabilities may have greater challenges to be resilient to disasters. If a community has a high population of people with disabilities, the emergency manager(s) may need to create tailored preparedness outreach programs and strategies to ensure those residents have support if evacuation is necessary. Rather than label these indicators as an absolute measure of resilience, FEMA considers “potential challenges to resilience” a better frame to understand these indicators. Everyone is vulnerable to disasters. While scholars theorize that certain characteristics may make an individual or a household more socially vulnerable, the data does not reflect measures that individuals and/or communities have taken to address potential challenges, such as emergency management planning and outreach or household preparedness measures. To aid emergency managers in understanding how to use these indicators, calling them potential challenges to resilience supports a more positive and strategic application of the data in all phases of emergency management.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

GAS KINEMATICS AND STAR FORMATION IN THE FILAMENTARY IRDC G34.43+0.24

We performed a multiwavelength study toward the infrared dark cloud (IRDC) G34.43+0.24. New maps of {sup 13}CO J = 1–0 and C{sup 18}O J = 1–0 were obtained from the Purple Mountain Observatory (PMO) 13.7 m radio telescope. At 8 μm (Spitzer-IRAC), IRDC G34.43+0.24 appears to be a dark filament extended by 18′ along the north–south direction. Based on the association with the 870 μm and C{sup 18}O J = 1–0 emission, we suggest that IRDC G34.43+0.24 should not be 18′ in length, but extend to 34′. IRDC G34.43+0.24 contains some massive protostars, UC H ii regions, and infrared bubbles. The spatial extend of IRDC G34.43+0.24 is about 37 pc, assuming a distance of 3.7 kpc. IRDC G34.43+0.24 has a linear mass density of ∼1.6 × 10{sup 3} M{sub ⊙} pc{sup −1}, which is roughly consistent with its critical mass to length ratio. The turbulent motion may help stabilize the filament against the radial collapse. Both infrared bubbles N61 and N62 show a ringlike structure at 8 μm. In particular, N61 has a double-shell structure that has expanded into IRDC G34.43+0.24. The outer shell is traced by 8 μm and {sup 13}CO J = 1–0 emission, while the inner shell is traced by 24 μm and 20 cm emission. We suggest that the outer shell (9.9 × 10{sup 5} years) is created by the expansion of H ii region G34.172+0.175, while the inner shell (4.1 ∼ 6.3 × 10{sup 5} years) may be produced by the energetic stellar wind of its central massive star. From the GLIMPSE I catalog, we selected some Class I sources with an age of ∼10{sup 5} years. These Class I sources are clustered along the filamentary molecular cloud.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Ultraviolet emission lines of Si ii in quasars—investigating the “Si ii disaster”

The observed line intensity ratios of the Si ii λ1263 and λ1307 multiplets to that of Si ii λ1814 in the broad-line region (BLR) of quasars are both an order of magnitude larger than the theoretical values. This was first pointed out by Baldwin et al., who termed it the “Si ii disaster,” and it has remained unresolved. We investigate the problem in the light of newly published atomic data for Si ii. Specifically, we perform BLR calculations using several different atomic data sets within the CLOUDY modeling code under optically thick quasar cloud conditions. In addition, we test for selective pumping by the source photons or intrinsic galactic reddening as possible causes for the discrepancy, and we also consider blending with other species. However, we find that none of the options investigated resolve the Si ii disaster, with the potential exception of microturbulent velocity broadening and line blending. We find that a larger microturbulent velocity (∼500 km s{sup −1}) may solve the Si ii disaster through continuum pumping and other effects. The CLOUDY models indicate strong blending of the Si ii λ1307 multiplet with emission lines of O i, although the predicted degree of blending is incompatible with the observed λ1263/λ1307 intensity ratios. Clearly, more work is required on the quasar modeling of not just the Si ii lines but also nearby transitions (in particular those of O i) to fully investigate whether blending may be responsible for the Si ii disaster.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

PHOTOMETRIC SELECTION OF A MASSIVE GALAXY CATALOG WITH z ≥ 0.55

We present the development of a photometrically selected massive galaxy catalog, targeting Luminous Red Galaxies (LRGs) and massive blue galaxies at redshifts of z⩾0.55. Massive galaxy candidates are selected using infrared/optical color–color cuts, with optical data from the Sloan Digital Sky Survey (SDSS) and infrared data from “unWISE” forced photometry derived from the Wide-field Infrared Survey Explorer (WISE). The selection method is based on previously developed techniques to select LRGs with z>0.5, and is optimized using receiver operating characteristic curves. The catalog contains 16,191,145 objects, selected over the full SDSS DR10 footprint. The redshift distribution of the resulting catalog is estimated using spectroscopic redshifts from the DEEP2 Galaxy Redshift Survey and photometric redshifts from COSMOS. Restframe U − B colors from DEEP2 are used to estimate LRG selection efficiency. Using DEEP2, the resulting catalog has an average redshift of z = 0.65, with a standard deviation of σ=2.0, and an average restframe of U−B=1.0, with a standard deviation of σ=0.27. Using COSMOS, the resulting catalog has an average redshift of z = 0.60, with a standard deviation of σ=1.8. We estimate 34% of the catalog to be blue galaxies with z⩾0.55. An estimated 9.6% of selected objects are blue sources with redshift z<0.55. Stellar contamination is estimated to be 1.8%.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Unveiling the First Black Holes With JWST:Multi-wavelength Spectral Predictions

Growing supermassive black holes (∼10{sup 9} M{sub ⊙}) that power luminous z>6 quasars from light seeds—the remnants of the first stars—within a Gyr of the Big Bang poses a timing challenge. The formation of massive black hole seeds via direct collapse with initial masses ∼10{sup 4}--10{sup 5} M{sub ⊙} alleviates this problem. Viable direct-collapse black hole formation sites, the satellite halos of star-forming galaxies, merge and acquire stars to produce a new, transient class of high-redshift objects, obese black hole galaxies (OBGs). The accretion luminosity outshines that of the stars in OBGs. We predict the multi-wavelength energy output of OBGs and growing Pop III remnants at z = 9 for standard and slim disk accretion, as well as high and low metallicities of the associated stellar population. We derive robust selection criteria for OBGs—a pre-selection to eliminate blue sources, followed by color–color cuts ([F{sub 090W}−F{sub 220W}]>0;−0.3<[F{sub 200W}−F{sub 444W}]<0.3) and the ratio of X-ray flux to rest-frame optical flux (F{sub X}/F{sub 444W}≫1). Our cuts sift out OBGs from other bright, high- and low-redshift contaminants in the infrared. OBGs with predicted M{sub AB}<25 are unambiguously detectable by the Mid-Infrared Instrument (MIRI), on the upcoming James Webb Space Telescope (JWST). For parameters explored here, growing Pop III remnants with predicted M{sub AB}<30 will likely be undetectable by JWST. We demonstrate that JWST has the power to discriminate between initial seeding mechanisms.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts in the DES -- Calibration of the Weak Lensing Source Redshift Distributions

We present the calibration of the Dark Energy Survey Year 1 (DES Y1) weak lensing source galaxy redshift distributions from clustering measurements. By cross-correlating the positions of source galaxies with luminous red galaxies selected by the redMaGiC algorithm we measure the redshift distributions of the source galaxies as placed into different tomographic bins. These measurements constrain any such shifts to an accuracy of $\sim0.02$ and can be computed even when the clustering measurements do not span the full redshift range. The highest-redshift source bin is not constrained by the clustering measurements because of the minimal redshift overlap with the redMaGiC galaxies. We compare our constraints with those obtained from $\texttt{COSMOS}$ 30-band photometry and find that our two very different methods produce consistent constraints.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Clean Energy Innovation Ecosystems Discovery - Measuring Innovation Through Data Analytics (MIDAS)

As part of the Mission Innovation rollout at the Paris COP 21, 20 countries announced to double their R&D budgets over 5 years to accelerate lab-to-market clean energy innovations. These innovations must address technical urgency and navigate complex institutional, social and financial environments. To inform budget and program development, policymakers need to understand existing clean energy innovation ecosystems (IE) and their characteristics at the local, state, and regional levels within the US. Researchers at ORNL have implemented a ground-breaking application of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The 'Measuring Innovation through Data Analytics' (MIDAS) application uses data to determine where clean energy innovation ecosystems exist and the underlying scoring algorithm "ranks" the strength of the ecosystem for several clean energy technologies. The selection of the data sources is driven by the fact that a clean energy IE can be identified as an intersection of nascent clean technology efforts and financing mechanisms and related large companies located in a geographically concentrated area with an enabling environment to encourage commercialization and networking assets to facilitate communication. MIDAS is a machine-assisted methodology that provides the user with a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems. This software application and the underlying datasets have the potential to address many important policy questions. The initial broad list of U.S. clean energy ecosystems, with geographic area, technology focus, and list and types of involved organizations can help describe regional tech activities and capabilities. Other potential applications include understanding the effect of private sector investments on ecosystem activity, and trends analysis.

Chinthavali, Supriya↗

DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗

DOE OSTI2022

Progress on Machine Learning for the SNS High Voltage Converter Modulators

The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.

Pappas, Chris↗

DOE OSTI2016

SU-F-J-183: Interior Region-Of-Interest Tomography by Using Inverse Geometry System

Purpose: The inverse geometry computed tomography (IGCT) composed of multiple source and small size detector has several merits such as reduction of scatter effect and large volumetric imaging within one rotation without cone-beam artifact, compared to conventional cone-beam computed tomography (CBCT). By using this multi-source characteristics, we intend to present a selective and multiple interior region-of-interest (ROI) imaging method by using a designed source on-off sequence of IGCT. Methods: All of the IGCT sources are operated one by one sequentially, and each projection in the shape of narrow cone-beam covers its own partial volume of full field of view (FOV) determined from system geometry. Thus, through controlling multi source operation, limited irradiation within ROI is possible and selective radon space data for ROI imaging can be acquired without additional X-ray filtration. With this feature, we designed a source on-off sequence for multi ROI-IGCT imaging, and projections of ROI-IGCT were generated by using the on-off sequence. Multi ROI-IGCT images were reconstructed by using filtered back-projection algorithm. All these imaging process of our study has been performed by utilizing digital phantom and patient CT data. ROI-IGCT images of the phantom were compared to CBCT image and the phantom data for the image quality evaluation. Results: Image quality of ROI-IGCT was comparable to that of CBCT. However, the distal axial-plane from the FOV center, large cone-angle region, ROI-IGCT showed uniform image quality without significant cone-beam artifact contrary to CBCT. Conclusion: ROI-IGCT showed comparable image quality and has the capability to provide multi ROI image within a rotation. Projection of ROI-IGCT is performed by selective irradiation, hence unnecessary imaging dose to non-interest region can be reduced. In this regard, it seems to be useful for diagnostic or image guidance purpose in radiotherapy such as low dose target localization and patient alignment. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Dark Energy Survey Year 3 results: Optimizing the lens sample in a combined galaxy clustering and galaxy-galaxy lensing analysis

We investigate potential gains in cosmological constraints from the combination of galaxy clustering and galaxy-galaxy lensing by optimizing the lens galaxy sample selection using information from Dark Energy Survey (DES) Year 3 data and assuming the DES Year 1 metacalibration sample for the sources. We explore easily reproducible selections based on magnitude cuts in i-band as a function of (photometric) redshift, zphot, and benchmark the potential gains against those using the well-established redMaGiC [E. Rozo et al., Mon. Not. R. Astron. Soc. 461, 1431 (2016)MNRAA40035-871110.1093/mnras/stw1281] sample. We focus on the balance between density and photometric redshift accuracy, while marginalizing over a realistic set of cosmological and systematic parameters. Our optimal selection, the MagLim sample, satisfies i<4zphot+18 and has ∼30% wider redshift distributions but ∼3.5 times more galaxies than redMaGiC. Assuming a wCDM model (i.e. with a free parameter for the dark energy equation of state) and equivalent scale cuts to mitigate nonlinear effects, this leads to 40% increase in the figure of merit for the pair combinations of Ωm, w, and σ8, and gains of 16% in σ8, 10% in Ωm, and 12% in w. Similarly, in ΛCDM, we find an improvement of 19% and 27% on σ8 and Ωm, respectively. We also explore flux-limited samples with a flat magnitude cut finding that the optimal selection, i<22.2, has ∼7 times more galaxies and ∼20% wider redshift distributions compared to MagLim, but slightly worse constraints. We show that our results are robust with respect to the assumed galaxy bias and photometric redshift uncertainties with only moderate further gains from increased number of tomographic bins or the inclusion of bin cross-correlations, except in the case of the flux-limited sample, for which these gains are more significant.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

TH-F-209-00: Pitfalls: Reliability and Performance of Diagnostic X-Ray Sources

Purpose: Performance and reliability of medical X-ray tubes for imaging are crucial from an ethical, clinical and economic perspective. This lecture will deliver insight into the aspects to consider during the decision making process to invest in X-ray imaging equipment. Outdated metric still hampers realistic product comparison. It is time to change this and to comply with latest standards, which consider current technology. Failure modes and ways to avoid down-time of the equipment shall be discussed. In view of the increasing number of interventional procedures and the hazards associated with ionizing radiation, toxic contrast agents, and the combination thereof, the aspect of system reliability is of paramount importance. Methods: A comprehensive picture of trends for different modalities (CT, angiography, general radiology) has been drawn and led to the development of novel X-ray tube technology. Results: Recent X-ray tubes feature enhanced reliability and unprecedented performance. Relevant metrics for product comparison still have to be implemented in practice. Conclusion: The speed of scientific and industrial development of new diagnostic and therapeutic X-ray sources remains tremendous. Still, users suffer from gaps between desire and reality in day-to-day diagnostic routine. X-ray sources are still limiting cutting-edge medical procedures. Side-effects of wear and tear, limitations of the clinical work flow, costs, the characteristics of the X-ray spectrum and others topics need to be further addressed. New applications and modalities, like detection-based color-resolved X-ray and phase-contrast / dark-field imaging will impact the course of new developments of X-ray sources. Learning Objectives: Understand the basic requirements on medical diagnostic X-ray sources per modality Learn to select the optimal equipment employing state-of-the-art metric Know causes of failures, depending on the way X-ray sources are operated Understand methods to remediate critical situations Understand the meaning of different warranty models I am employee of Royal Philips; R. Behling, No external funding. I am employee of Royal Philips.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

TH-F-209-01: Pitfalls: Reliability and Performance of Diagnostic X-Ray Sources

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2013

The antibody mining toolbox: An open source tool for the rapid analysis of antibody repertoires

In vitro selection has been an essential tool in the development of recombinant antibodies against various antigen targets. Deep sequencing has recently been gaining ground as an alternative and valuable method to analyze such antibody selections. The analysis provides a novel and extremely detailed view of selected antibody populations, and allows the identification of specific antibodies using only sequencing data, potentially eliminating the need for expensive and laborious low-throughput screening methods such as enzyme-linked immunosorbant assay. The high cost and the need for bioinformatics experts and powerful computer clusters, however, have limited the general use of deep sequencing in antibody selections. Here, we describe the AbMining ToolBox, an open source software package for the straightforward analysis of antibody libraries sequenced by the three main next generation sequencing platforms (454, Ion Torrent, MiSeq). The ToolBox is able to identify heavy chain CDR3s as effectively as more computationally intense software, and can be easily adapted to analyze other portions of antibody variable genes, as well as the selection outputs of libraries based on different scaffolds. The software runs on all common operating systems (Microsoft Windows, Mac OS X, Linux), on standard personal computers, and sequence analysis of 1–2 million reads can be accomplished in 10–15 min, a fraction of the time of competing software. Use of the ToolBox will allow the average researcher to incorporate deep sequence analysis into routine selections from antibody display libraries.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Simulations of cm-wavelength Sunyaev-Zel'dovich galaxy cluster and point source blind sky surveys and predictions for the RT32/OCRA-f and the Hevelius 100-m radio telescope

We investigate the effectiveness of blind surveys for radio sources and galaxy cluster thermal Sunyaev-Zel'dovich effects (TSZEs) using the four-pair, beam-switched OCRA-f radiometer on the 32-m radio telescope in Poland. The predictions are based on mock maps that include the cosmic microwave background, TSZEs from hydrodynamical simulations of large scale structure formation, and unresolved radio sources. We validate the mock maps against observational data, and examine the limitations imposed by simplified physics. We estimate the effects of source clustering towards galaxy clusters from NVSS source counts around Planck-selected cluster candidates, and include appropriate correlations in our mock maps. The study allows us to quantify the effects of halo line-of-sight alignments, source confusion, and telescope angular resolution on the detections of TSZEs. We perform a similar analysis for the planned 100-m Hevelius radio telescope (RTH) equipped with a 49-beam radio camera and operating at frequencies up to 22 GHz.We find that RT32/OCRA-f will be suitable for small-field blind radio source surveys, and will detect 33{sup +17}{sub −11} new radio sources brighter than 0.87 mJy at 30 GHz in a 1 deg{sup 2} field at > 5σ CL during a one-year, non-continuous, observing campaign, taking account of Polish weather conditions. It is unlikely that any galaxy cluster will be detected at 3σ CL in such a survey. A 60-deg{sup 2} survey, with field coverage of 2{sup 2} beams per pixel, at 15 GHz with the RTH, would find <1.5 galaxy clusters per year brighter than 60 μJy (at 3σ CL), and would detect about 3.4 × 10{sup 4} point sources brighter than 1 mJy at 5σ CL, with confusion causing flux density errors ∼< 2% (20%) in 68% (95%) of the detected sources.A primary goal of the planned RTH will be a wide-area (π sr) radio source survey at 15 GHz. This survey will detect nearly 3 × 10{sup 5} radio sources at 5σ CL down to 1.3 mJy, and tens of galaxy clusters, in one year of operation with typical weather conditions. Confusion will affect the measured flux densities by ∼< 1.5% (16%) for 68% (95%) of the point sources. We also gauge the impact of the RTH by investigating its performance if equipped with the existing RT32 receivers, and the performance of the RT32 equipped with the RTH radio camera.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Biotemplate synthesis of carbon nanostructures using bamboo as both the template and the carbon source

Graphical abstract: - Highlights: • A new method for the in situ growth of carbon nanostructures was demonstrated. • The bamboo was selected as both the green carbon source and the biotemplate. • Four distinct structural types of carbon nanostructure have been identified. • The corresponding growth mechanism of each carbon nanostructure was proposed. - Abstract: A series of carbon nanostructures were prepared via a biotemplate method by catalytic decomposition of bamboo impregnated with ferric nitrate. The natural nanoporous bamboo was used as both the green carbon source and the template for the in situ growth of carbon nanostructures. Scanning electron microscope, field emission transmission electron microscope and energy dispersive X-ray spectroscope were used to characterize the product. Four distinct structural types of carbon nanostructures have been identified, namely nanofibers, hollow carbon nanospheres, herringbone and bamboo-shaped nanotubes. The effect of reaction temperature (from 600 to 900 °C) on the growth behavior of carbon nanostructures was investigated and the corresponding growth mechanism was proposed. At low temperature the production of nanofibers was favored, while higher temperature led to bamboo-shaped nanostructures.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Redox-flow batteries employing oligomeric organic active materials and size-selective microporous polymer membranes

Intermittent energy sources, including solar and wind, require scalable, low-cost, multi-hour energy storage solutions to be effectively incorporated into the grid. Redox-flow batteries offer a solution, but suffer from rapid capacity fade and low Coulombic efficiency due to the high permeability of redox-active species across the battery's membrane. Here we show that active-species crossover can be arrested by scaling the membrane's pore size to molecular dimensions and in turn increasing the size of the active material to be above the membrane's pore-size exclusion limit. When oligomeric redox-active organic molecules were paired with microporous polymer membranes, the rate of active-material crossover was either completely blocked or slowed more than 9,000-fold compared to traditional separators at minimal cost to ionic conductivity. In the case of the latter, this corresponds to an absolute rate of ROM crossover of less than 3 μmol cm−2 day−1 (for a 1.0 M concentration gradient), which exceeds performance targets recently set forth by the battery industry. This strategy was generalizable to both high and low-potential ROMs in a variety of electrolytes, highlighting the importance of macromolecular design in implementing next-generation redox-flow batteries.

Helms, Brett A.↗