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Physical properties of molecular clouds for the entire Milky Way disk

This study presents a catalog of 8107 molecular clouds that covers the entire Galactic plane and includes 98% of the {sup 12}CO emission observed within b±5{sup ∘}. The catalog was produced using a hierarchical cluster identification method applied to the result of a Gaussian decomposition of the Dame et al. data. The total H{sub 2} mass in the catalog is 1.2×10{sup 9} M{sub ⊙}, in agreement with previous estimates. We find that 30% of the sight lines intersect only a single cloud, with another 25% intersecting only two clouds. The most probable cloud size is R∼30 pc. We find that M∝ R{sup 2.2±0.2}, with no correlation between the cloud surface density, Σ, and R. In contrast with the general idea, we find a rather large range of values of Σ, from 2 to 300 M{sub ⊙} pc{sup −2}, and a systematic decrease with increasing Galactic radius, R{sub gal}. The cloud velocity dispersion and the normalization σ{sub 0}=σ{sub v}/R{sup 1/2} both decrease systematically with R{sub gal}. When studied over the whole Galactic disk, there is a large dispersion in the line width–size relation and a significantly better correlation between σ{sub v} and Σ R. The normalization of this correlation is constant to better than a factor of two for R{sub gal}<20 kpc. This relation is used to disentangle the ambiguity between near and far kinematic distances. We report a strong variation of the turbulent energy injection rate. In the outer Galaxy it may be maintained by accretion through the disk and/or onto the clouds, but neither source can drive the 100 times higher cloud-averaged injection rate in the inner Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗

Doppler lidar measurement of profiles of turbulence and momentum flux

A short-pulse CO2 Doppler lidar with 150-m range resolution measured vertical profiles of turbulence and momentum flux. Example measurements are reported of a daytime mixed layer with strong mechanical mixing caused by a wind speed of 15 m/sec, which exceeded the speed above the capping inversion. The lidar adapted an azimuth scanning technique previously demonstrated by radar. Scans alternating between two elevation angles allow determination of mean U-squared, V-squared, and W-squared. Expressions were derived to estimate the uncertainty in the turbulence parameters. A new processing method, partial Fourier decomposition, has less uncertainty than the filtering used earlier.

Eberhard, Wynn L.↗

Sol-gel synthesize and characterization of χGd2Ti2O7/SiO2 photocatalyst for ofloxacin decomposition

Highlights: • The surface of SiO{sub 2} sphere is coated with a rough layer of Gd{sub 2}Ti{sub 2}O{sub 7}. • Many physico-chemical properties of Gd{sub 2}Ti{sub 2}O{sub 7} are modified to enhance its activity. • The 30%Gd{sub 2}Ti{sub 2}O{sub 7}/SiO{sub 2} sample has the optimal photocatalytic activity. - Abstract: Gd{sub 2}Ti{sub 2}O{sub 7} was loaded on the surface of quartz spheres by a sol-gel method. The Gd{sub 2}Ti{sub 2}O{sub 7} maintains the pyrochlore phase after loading. Dispersion of Gd{sub 2}Ti{sub 2}O{sub 7} on the SiO{sub 2} spheres reduces crystallite size of Gd{sub 2}Ti{sub 2}O{sub 7}. The surface of SiO{sub 2} sphere is coated with a rough layer of Gd{sub 2}Ti{sub 2}O{sub 7}. The band gap energy of the χGd{sub 2}Ti{sub 2}O{sub 7}/SiO{sub 2} constantly increases with decreasing Gd{sub 2}Ti{sub 2}O{sub 7} loading content, ranging from 3.35 eV to 3.71 eV. The porous structure in the χGd{sub 2}Ti{sub 2}O{sub 7}/SiO{sub 2} composite mainly comes from the Gd{sub 2}Ti{sub 2}O{sub 7} layer. The maximum BET surface area and total pore volume are even larger than that of the unloaded Gd{sub 2}Ti{sub 2}O{sub 7}. The hydroxyl radical generation efficiency during illumination is enhanced after loading Gd{sub 2}Ti{sub 2}O{sub 7} on SiO{sub 2}. The 30%Gd{sub 2}Ti{sub 2}O{sub 7}/SiO{sub 2} sample has the optimal photocatalytic activity. 65.6% of the initial ofloxacin can be decomposed on 30%Gd{sub 2}Ti{sub 2}O{sub 7}/SiO{sub 2} after 30 min of UV light irradiation, as compared to 24.1% on the unloaded Gd{sub 2}Ti{sub 2}O{sub 7}.

36 MATERIALS SCIENCE↗

Energetics of oxidation and formation of uranium monocarbide

To enable better implementation of uranium monocarbide (UC) as an advanced nuclear fuel for future high-temperature reactors, it is essential to have a thorough knowledge of its thermal and thermodynamic properties under reactor operational conditions. In this work, we studied thermal bulk oxidation of UC by simultaneous thermal analysis consisting of thermogravimetric analysis – differential scanning calorimetry coupled with evolved gas mass spectrometry (TGA-DSC-MS), and we examined the thermodynamic stability of UC using high temperature oxide melt drop solution calorimetry. Further, in air, our studied UC sample (which contains ~5 mol% UO 2 ) was found to undergo a step-wise thermal oxidation process consisting of consecutive oxidations and thermal decomposition reactions: 0.95UC·0.05UO 2 → UO 3 ·0.29(C x O y ) + 0.66CO 2 → UO 3 ·0.20(C x O y ) + 0.09CO 2 → UO 3 ·0.03(C x O y ) + 0.17CO 2 → U 3 O 8 + 0.03CO 2 + 0.166O 2 . DSC was further used to determine the enthalpies of reactions associated with this series of oxidation reactions. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption spectroscopy (EXAFS) were performed to characterize both the long- and short-range structures of UC. The standard enthalpy of formation (ΔH° f ) of UC was determined to be –50.7 ± 10.8 kJ/mol·atom, in good agreement with previous values measured by bomb calorimetry. Lastly, the enthalpic landscape of U-C compounds, including UC, U 2 C 3 , and α-UC 1.94 , were established based on the enthalpy normalized per mole atom, which suggests that U-C phases are thermodynamically stable at lower C/U ratios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

WE-AB-209-10: Optimizing the Delivery of Sequential Fluence Maps for Efficient VMAT Delivery

Purpose: To develop an optimization model and solution approach for computing MLC leaf trajectories and dose rates for high quality matching of a set of optimized fluence maps to be delivered sequentially around a patient in a VMAT treatment. Methods: We formulate the fluence map matching problem as a nonlinear optimization problem where time is discretized but dose rates and leaf positions are continuous variables. For a given allotted time, which is allocated across the fluence maps based on the complexity of each fluence map, the optimization problem searches for the best leaf trajectories and dose rates such that the original fluence maps are closely recreated. Constraints include maximum leaf speed, maximum dose rate, and leaf collision avoidance, as well as the constraint that the ending leaf positions for one map are the starting leaf positions for the next map. The resulting model is non-convex but smooth, and therefore we solve it by local searches from a variety of starting positions. We improve solution time by a custom decomposition approach which allows us to decouple the rows of the fluence maps and solve each leaf pair individually. This decomposition also makes the problem easily parallelized. Results: We demonstrate method on a prostate case and a head-and-neck case and show that one can recreate fluence maps to high degree of fidelity in modest total delivery time (minutes). Conclusion: We present a VMAT sequencing method that reproduces optimal fluence maps by searching over a vast number of possible leaf trajectories. By varying the total allotted time given, this approach is the first of its kind to allow users to produce VMAT solutions that span the range of wide-field coarse VMAT deliveries to narrow-field high-MU sliding window-like approaches.

60 APPLIED LIFE SCIENCES↗

Catalysts for Efficient Production of Carbon Nanotubes

Several metal alloys have shown promise as improved catalysts for catalytic thermal decomposition of hydrocarbon gases to produce carbon nanotubes (CNTs). Heretofore almost every experiment on the production of carbon nanotubes by this method has involved the use of iron, nickel, or cobalt as the catalyst. However, the catalytic-conversion efficiencies of these metals have been observed to be limited. The identification of better catalysts is part of a continuing program to develop means of mass production of high-quality carbon nanotubes at costs lower than those achieved thus far (as much as $100/g for purified multi-wall CNTs or $1,000/g for single-wall CNTs in year 2002). The main effort thus far in this program has been the design and implementation of a process tailored specifically for high-throughput screening of alloys for catalyzing the growth of CNTs. The process includes an integral combination of (1) formulation of libraries of catalysts, (2) synthesis of CNTs from decomposition of ethylene on powders of the alloys in a pyrolytic chemical-vapor-decomposition reactor, and (3) scanning- electron-microscope screening of the CNTs thus synthesized to evaluate the catalytic efficiencies of the alloys. Information gained in this process is put into a database and analyzed to identify promising alloy compositions, which are to be subjected to further evaluation in a subsequent round of testing. Some of these alloys have been found to catalyze the formation of carbon nano tubes from ethylene at temperatures as low as 350 to 400 C. In contrast, the temperatures typically required for prior catalysts range from 550 to 750 C.

Sun, Ted X.↗

Developing an Accurate CFD Based Gust Model for the Truss Braced Wing Aircraft

The increased flexibility of long endurance aircraft having high aspect ratio wings necessitates attention to gust response and perhaps the incorporation of gust load alleviation. The design of civil transport aircraft with a strut or truss-braced high aspect ratio wing furthermore requires gust response analysis in the transonic cruise range. This requirement motivates the use of high fidelity nonlinear computational fluid dynamics (CFD) for gust response analysis. This paper presents the development of a CFD based gust model for the truss braced wing aircraft. A sharp-edged gust provides the gust system identification. The result of the system identification is several thousand time steps of instantaneous pressure coefficients over the entire vehicle. This data is filtered and downsampled to provide the snapshot data set from which a reduced order model is developed. A stochastic singular value decomposition algorithm is used to obtain a proper orthogonal decomposition (POD). The POD model is combined with a convolution integral to predict the time varying pressure coefficient distribution due to a novel gust profile. Finally the unsteady surface pressure response of the truss braced wing vehicle to a one-minus-cosine gust, simulated using the reduced order model, is compared with the full CFD.

Bartels, Robert E.↗

Research Progress towards Understanding the Unique Interfaces between Concentrated Electrolytes and Electrodes for Energy Storage Applications

The electrolyte is an indispensable component in all electrochemical energy storage and conversion devices with batteries being a prime example. While most research efforts have been pursued on the materials side, the progress for the electrolyte is slow due to the decomposition of salts and solvents at low potentials, not to mention their complicated interactions with the electrode materials. The general properties of bulk electrolytes such as ionic conductivity, viscosity, and stability all affect the cell performance. However, for a specific electrochemical cell in which the cathode, anode, and electrolyte are optimized, it is the interface between the solid electrode and the liquid electrolyte, generally referred to as the solid electrolyte interphase (SEI), that dictates the rate of ion flow in the system. The commonly used electrolyte is within the range of 1–1.2 m based on the prior optimization experience, leaving the high concentration region insufficiently recognized. Recently, electrolytes with increased concentration (>1.0 m ) have received intensive attention due to quite a few interesting discoveries in cells containing concentrated electrolytes. The formation mechanism and the nature of the SEI layers derived from concentrated electrolytes could be fundamentally distinct from those of the traditional SEI and thus enable unusual functions that cannot be realized using regular electrolytes. In this article, we provide an overview on the recent progress of high concentration electrolytes in different battery chemistries. The experimentally observed phenomena and their underlying fundamental mechanisms are discussed. New insights and perspectives are proposed to inspire more revolutionary solutions to address the interfacial challenges.

25 ENERGY STORAGE↗

Visible-light-driven Bi 2 O 3 /WO 3 composites with enhanced photocatalytic activity

Semiconductor heterojunctions (composites) have been shown to be effective photocatalytic materials to overcome the drawbacks of low photocatalytic efficiency that results from electron–hole recombination and narrow photo-response range. We prepared a novel visible-light-driven Bi 2 O 3 /WO 3 composite photocatalyst by hydrothermal synthesis. The composite was characterized by scanning transmission electron microscopy (STEM), scanning electron microscopy (SEM), powder X-ray diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) surface area, Raman spectroscopy, photoluminescence spectroscopy (PL) and electrochemical impedance spectroscopy (EIS) to better understand the structures, compositions, morphologies and optical properties. Bi 2 O 3 /WO 3 heterojunction was found to exhibit significantly higher photocatalytic activity towards the decomposition of Rhodamine B (RhB) and 4-nitroaniline (4-NA) under visible light irradiation compared to that of Bi 2 O 3 and WO 3 . A tentative mechanism for the enhanced photocatalytic activity of the heterostructured composite is discussed based on observed activity, band position calculations, photoluminescence, and electrochemical impedance data. Our study provides a new strategy for the design of composite materials with enhanced visible light photocatalytic performance.

36 MATERIALS SCIENCE↗

Modeling Simulation and Optimization of a Pressure Relief Feature in a Hermetically Sealed Volume Exposed to Abnormal Thermal Environments

This project investigates the behavior of a venting feature on a hermetically sealed volume under abnormal thermal environments. A pressure profile is used to simulate internal pressure build up due to thermal decomposition of foam caused by temperatures approaching 800K. For the purposes of his scoping study a small can composed of 304L stainless steel is used to emulate the problem at hand and simplify the model. The venting feature can be described as an elliptical thinned area with X-like scoring marks on the circumferential surface of the can where the highest stresses are located without the feature. The feature variables include elliptical size, depth, and quantity to optimize the venting feature geometry to vent at a desired pressure range. Cubit, SIERRA: Solid Mechanics, and Ensight modeling programs are used to simulate and analyze several iterations of this feature to determine the most optimal geometry. The model indicates through the simulations that the feature does in fact vent in a predictable manner and can be used in a variety of pressure vessel applications where uncontrolled over pressurizations are undesirable. Future work includes model validation tests, looking at different geometries, and using a more accurate failure criterion.

42 ENGINEERING↗

Bench-scale synthesis of nanoscale materials

A novel flow-through hydrothermal method used to synthesize nanoscale powders is introduced by Pacific Northwest Laboratory. The process, Rapid Thermal Decomposition of precursors in Solution (RTDS), uniquely combines high-pressure and high-temperature conditions to rapidly form nanoscale particles. The RTDS process was initially demonstrated on a laboratory scale and was subsequently scaled up to accommodate production rates attractive to industry. The process is able to produce a wide variety of metal oxides and oxyhydroxides. The powders are characterized by scanning and transmission electron microscopic methods, surface-area measurements, and x-ray diffraction. Typical crystallite sizes are less than 20 nanometers, with BET surface areas ranging from 100 to 400 sq m/g. A description of the RTDS process is presented along with powder characterization results. In addition, data on the sintering of nanoscale ZrO2 produced by RTDS are included.

Buehler, M. F.↗

CORPSE model with litter decomposition parameters derived from the LIDET dataset

This is a version of the CORPSE model (Carbon, Organisms, Rhizosphere and Protection in the Soil Environment, Sulman et al. 2014) that uses litter decomposition parameters derived from a modified Monte Carlo simulation using the LIDET litter decomposition dataset (Long-term Intersite Decomposition Experiment Team, Harmon 2013). The code also includes the Baseline parameters, and the eight other best parameter sets identified in a modified Monte Carlo simulation. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of files:The folder "Input Files" contains one folder for each LIDET site with data necessary to run the model. Note that "(site)" in the filenames below indicates where the LIDET site code appears (see Table 1 for site codes). Data streams include: CORPSE_full_spinup_litter.csv, CORPSE_full_spinup_rhizo.csv, CORPSE_full_spinup_bulk.csv, litterbag_init_100g_6spp.csv: initial C and N (kg C or N/m2) pool values for each soil layer, the litterbag_init_100_6spp.csv file is for the litterbag layer and is the same file for all sites. All initial C and N files have the same columns (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 soilT (site) DOY274start.csv: Average daily soil temperature (oC) interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). soilT (site) DOY274start.csv: Average daily soil volumetric water content (VWC) scalar interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). litter production.csv: Average daily litter production values for each site, data sources listed in Table S3 of related publication. litter (site) CN.csv: C:N ratio for each species from LIDET dataset (Table 2, Harmon 2013). (site).csv: Table indicating number of observations for each species decomposed at each site. Instructions: Save the model code ("CORPSE_LIDET.R") and "Input Files" folder in the same folder. Also make a folder for the model output (e.g., "results_Baseline") in the same folder. Set the working directory (setwd) in the model code to the folder with the files saved in step #1. Select the parameter set to use for the litter and litterbag compartments, comment out all other parameter sets. Run code. Output will be saved in the folder made in step 1. Output destination can be changed as necessary in code section called "Running the model." Table 1 LIDET sites and site codes used in model files. Site Code - Site AND - H.J. Andrews Experimental Forest BNZ - Bonanza Creek Experimental Forest BSF - Blodgett Research Forest CDR - Cedar Creek Natural History Area CPR - Central Plains Experimental Range HBR - Hubbard Brook Experimental Forest HFR - Harvard Forest JUN - Juneau KBS - Kellogg Biological Station KNZ - Konza Prairie Research Natural Area NWT - Niwot Ridge/Green Lakes Valley OLY - Olympic National Park OLY Conifer forest SEV - Sevilleta National Wildlife Refuge SMR - Santa Margarita Ecological Reserve UFL - University of Florida VCR - Virginia Coast Reserve Table 2 LIDET species and species codes used in model files (6 common species). Species - Species Code Sugar maple (Acer saccharum) - ACSA Drypetes (Drypetes glauca) - DRGL Red pine (Pinus resinosa) - PIRE Chestnut oak (Quercus prinus) - QUPR Western redcedar (Thuja plicata) - THPL Wheat (Triticum aestivum) - TRAE References:Bonan, G. B., Hartman, M. D., Parton, W. J., & Wieder, W. R. (2013). Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology, 19(3), 957-974. https://doi.org/https://doi.org/10.1111/gcb.12031 Harmon, M. (2013). LTER Intersite Fine Litter Decomposition Experiment (LIDET), 1990 to 2002. Long-Term Ecological Research. Forest Science Data Bank, Corvallis, OR. [Data set]. Accessed http://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023. https://doi.org/10.6073/pasta/f35f56bea52d78b6a1ecf1952b4889c5. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., & Pacala, S. W. (2014). Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change, 4, 1099 - 1102. https://doi.org/10.1038/nclimate2436

Juice, Stephanie↗

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗

Implementation and Characterization of Three-Dimensional Particle-in-Cell Codes on Multiple-Instruction-Multiple-Data Massively Parallel Supercomputers

A three-dimensional electrostatic particle-in-cell (PIC) plasma simulation code has been developed on coarse-grain distributed-memory massively parallel computers with message passing communications. Our implementation is the generalization to three-dimensions of the general concurrent particle-in-cell (GCPIC) algorithm. In the GCPIC algorithm, the particle computation is divided among the processors using a domain decomposition of the simulation domain. In a three-dimensional simulation, the domain can be partitioned into one-, two-, or three-dimensional subdomains ("slabs," "rods," or "cubes") and we investigate the efficiency of the parallel implementation of the push for all three choices. The present implementation runs on the Intel Touchstone Delta machine at Caltech; a multiple-instruction-multiple-data (MIMD) parallel computer with 512 nodes. We find that the parallel efficiency of the push is very high, with the ratio of communication to computation time in the range 0.3%-10.0%. The highest efficiency (> 99%) occurs for a large, scaled problem with 64(sup 3) particles per processing node (approximately 134 million particles of 512 nodes) which has a push time of about 250 ns per particle per time step. We have also developed expressions for the timing of the code which are a function of both code parameters (number of grid points, particles, etc.) and machine-dependent parameters (effective FLOP rate, and the effective interprocessor bandwidths for the communication of particles and grid points). These expressions can be used to estimate the performance of scaled problems--including those with inhomogeneous plasmas--to other parallel machines once the machine-dependent parameters are known.

Lyster, P. M.↗

Flame Synthesis of Single- and Multi-Walled Carbon Nanotubes and Nanofibers

Metal-catalyzed carbon nanotubes are highly sought for a diverse range of applications that include nanoelectronics, battery electrode material, catalysis, hydrogen storage media and reinforcing agents in polymer composites. These latter applications will require vast quantities of nanotubes at competitive prices to be economically feasible. Moreover, reinforcing applications may not require ultrahigh purity nanotubes. Indeed, functionalization of nanotubes to facilitate interfacial bonding within composites will naturally introduce defects into the tube walls, lessening their tensile strength. Current methods of aerosol synthesis of carbon nanotubes include laser ablation of composite targets of carbon and catalyst metal within high temperature furnaces and decomposition of a organometallics in hydrocarbons mixtures within a tube furnace. Common to each approach is the generation of particles in the presence of the reactive hydrocarbon species at elevated temperatures. In the laser-ablation approach, the situation is even more dynamic in that particles and nanotubes are borne during the transient cooling phase of the laser-induced plasma for which the temperature far exceeds that of the surrounding hot gases within the furnace process tube. A shared limitation is that more efficient methods of nanoparticle synthesis are not readily incorporated into these approaches. In contrast, combustion can quite naturally create nanomaterials such as carbon black. Flame synthesis is well known for its commercial scalability and energy efficiency. However, flames do present a complex chemical environment with steep gradients in temperature and species concentrations. Moreover, reaction times are limited within buoyant driven flows to tens of milliseconds. Therein microgravity can greatly lessen temperature and spatial gradients while allowing independent control of flame residence times. In preparation for defining the microgravity experiments, the work presented here focuses on the effect of catalyst particle size and reactant gas in 1g.

VanderWal, R. L.↗

Crystallite-growth, phase transition, magnetic properties, and sintering behaviour of nano-CuFe{sub 2}O{sub 4} powders prepared by a combustion-like process

The synthesis of nano-crystalline CuFe{sub 2}O{sub 4} powders by a combustion-like process is described herein. Phase formation and evolution of the crystallite size during the decomposition process of a (CuFe{sub 2})—precursor gel were monitored up to 1000 °C. Phase-pure nano-sized CuFe{sub 2}O{sub 4} powders were obtained after reaction at 750 °C for 2 h resulting in a crystallite size of 36 nm, which increases to 96 nm after calcining at 1000 °C. The activation energy of the crystallite growth process was calculated as 389 kJ mol{sup −1}. The tetragonal⇄cubic phase transition occurs between 402 and 419 °C and the enthalpy change (ΔH) was found to range between 1020 and 1229 J mol{sup −1} depending on the calcination temperature. The optical band gap depends on the calcination temperature and was found between 2.03 and 1.89 eV. The shrinkage and sintering behaviour of compacted powders were examined. Dense ceramic bodies can be obtained either after conventional sintering at 950 °C or after a two-step sintering process at 800 °C. Magnetic measurements of both powders and corresponding ceramic bodies show that the saturation magnetization rises with increasing calcination-/sintering temperature up to 49.1 emu g{sup −1} (2.1 µ{sub B} fu{sup −1}), whereas the coercivity and remanence values decrease. - Graphical abstract: A cheap one-pot synthesis was developed to obtain CuFe{sub 2}O{sub 4} nano-powders with different crystallite sizes (36–96 nm). The optical band gaps, phase transition temperatures and enthalpies were determined depending on the particle size. The sintering behaviour of nano CuFe{sub 2}O{sub 4} was studied in different sintering procedures. The magnetic behaviour of the nano-powders as well as the corresponding ceramic bodies were investigated. - Highlights: • Eco-friendly and simple synthesis for nano CuFe{sub 2}O{sub 4} powder using starch as polymerization agent. • Monitoring the phase evolution and crystallite growth kinetics during the synthesis. • Determination of the optical band gap, phase transition temperature, and enthalpy change. • Sintering behaviour of nano-sizes CuFe{sub 2}O{sub 4}. • Magnetic behaviour of CuFe{sub 2}O{sub 4} powders and ceramics depending on their particle size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

25 ENERGY STORAGE↗