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At least 1,045 records · Page 58

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗

A manufacturing process design for producing a membrane-based energy recovery ventilator with high aspect ratio support ribs

Heating, ventilation, and air conditioning systems represent nearly half of building energy usage in the United States. Recent building regulations requiring increased air turnover within buildings will result in even greater air conditioning usage. To alleviate these concerns, membrane-based heat-and-moisture exchangers known as energy recovery ventilators have been developed to reduce the energy expenditure of air conditioning systems by pre-conditioning incoming air through sensible and latent heat transfer with building air exhaust. Here in this work, we propose and validate a manufacturing process design for channel lamination based on surface mount adhesives capable of meeting the process requirements of a membrane-based ventilator. In particular, the device requires rib supports with height-to-width aspect ratios greater than one. The proposed manufacturing process design is capable of meeting this process requirement by stretching the adhesive after initial adhesive tacking. A manufacturing process flow diagram, a machine tool specification and a cost model are proposed for meeting process requirements. A set of design constraints are developed detailing the adhesive requirements necessary for meeting requirements. The cost model is validated by building a sub-scale mesochannel array demonstrating the ability to meet process requirements. Results show that additional work is needed to validate the curing step but that the process is capable of producing a mesochannel array with an acceptable level of variation.

42 ENGINEERING↗

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗

Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review

Pressure-Retarded Osmosis (PRO) is an osmotic process that has been used to harvest energy from salinity gradients using a semi permeable membrane. A comparison between open-loop PRO (OLPRO) and closed-loop PRO (CLPRO) was made regarding their performance and costs. In CLPRO, where the diluted draw solution is re-concentrated in the regeneration system to be reutilized in the process, has recently received an intensive focus as the most viable configuration for a standalone power plant. The choice of the PRO draw solution in CLPRO is crucial to garner a high osmotic pressure as the key for the feasibility of the process. Here, in this review, the draw solutions are critically evaluated in the literature in terms of energy output as well as the method of regeneration used to recirculate them. A set of practical criteria has been suggested to appraise the adequacy of the solution for CLPRO application. It was concluded that NH 3 – CO 2 theoretically can produce 170 W/m 2 of power density. Inorganic draw solutes such as NaCl can generate high power density up to 87 W/m 2 . Organic draw solutes with their remarkably low reverse salt flux (RSF) have promising potential for future application in PRO. Similarly, the regeneration systems of the diluted draw solutions have also been reviewed and discussed. How the energy consumption of the regeneration process affects the feasibility of CLPRO is explained. For the specific case of osmotic heat engines (OHEs), when the energy of the regeneration process is supplied by heat waste, the range of applicability of the heat waste in CLPRO in terms of efficiency is defined and compared to Organic Rankine Cycle (ORC). The results showed that CLPRO has better efficiency than ORC for temperatures T < 80 °C, which makes it a promising process or low-grade heat energy recovery. In addition, a PRO-RO hybrid system coupled with solar power can reduce the net specific energy consumption (SEC) to 0.39 kWh/m 3 . The conditions that regeneration processes should operate under to make PRO viable are discussed in the last section. Overall, the study indicates the key factors for optimizing the performance of CLPRO process.

42 ENGINEERING↗

Solution-Processed Layered Gallium Telluride Thin-Film Photodetectors

Using scalable solution processing, layered gallium telluride (GaTe) nanoflake dispersions are produced in surfactant-free, low-boiling-point, water–ethanol cosolvent mixtures. During exfoliation, chemical degradation of the ambient-reactive GaTe crystals is minimized by using deoxygenated solvents in a sealed tip ultrasonication system. The structural and chemical integrity of the solution-processed GaTe nanoflakes is subsequently confirmed with a comprehensive suite of microscopic and spectroscopic analyses. Furthermore, field-effect transistors and phototransistors based on individual solution-processed GaTe nanoflakes show electronic and optoelectronic properties, respectively, that are comparable to micromechanically exfoliated GaTe. Minimal solution-processing residues from the surfactant-free, low-boiling-point cosolvent dispersion medium coupled with the high intrinsic hole doping of GaTe produces the highest electrical conductivity among solution-processed layered nanoflake thin films without post-treatment. Large-area photodetectors based on these electrically percolating thin films of solution-processed GaTe nanoflakes show a positive correlation between responsivity and illumination intensity, with a high photoconversion gain that is explained by a combination of defect-mediated optical processes and photothermal effects. Finally, overall, this study establishes solution-processed layered GaTe nanoflakes as a leading candidate for high-performance, large-area, thin-film photodetectors.

47 OTHER INSTRUMENTATION↗

Sensitivity studies for the main r process: β-decay rates

The pattern of isotopic abundances produced in rapid neutron capture, or r-process, nucleosynthesis is sensitive to the nuclear physics properties of thousands of unstable neutron-rich nuclear species that participate in the process. It has long been recognized that the some of the most influential pieces of nuclear data for r-process simulations are β-decay lifetimes. In light of experimental advances that have pushed measurement capabilities closer to the classic r-process path, we revisit the role of individual β-decay rates in the r process. We perform β-decay rate sensitivity studies for a main (A > 120) r process in a range of potential astrophysical scenarios. We study the influence of individual rates during (n, γ)-(γ, n) equilibrium and during the post-equilibrium phase where material moves back toward stability. We confirm the widely accepted view that the most important lifetimes are those of nuclei along the r-process path for each astrophysical scenario considered. However, we find in addition that individual β-decay rates continue to shape the final abundance pattern through the post-equilibrium phase, for as long as neutron capture competes with β decay. Many of the lifetimes important for this phase of the r process are within current or near future experimental reach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Large deviations in stochastic heat-conduction processes provide a gradient-flow structure for heat conduction

We consider three one-dimensional continuous-time Markov processes on a lattice, each of which models the conduction of heat: the family of Brownian Energy Processes with parameter m (BEP(m)), a Generalized Brownian Energy Process, and the Kipnis-Marchioro-Presutti (KMP) process. The hydrodynamic limit of each of these three processes is a parabolic equation, the linear heat equation in the case of the BEP(m) and the KMP, and a nonlinear heat equation for the Generalized Brownian Energy Process with parameter a (GBEP(a)). We prove the hydrodynamic limit rigorously for the BEP(m), and give a formal derivation for the GBEP(a). We then formally derive the pathwise large-deviation rate functional for the empirical measure of the three processes. These rate functionals imply gradient-flow structures for the limiting linear and nonlinear heat equations. We contrast these gradient-flow structures with those for processes describing the diffusion of mass, most importantly the class of Wasserstein gradient-flow systems. The linear and nonlinear heat-equation gradient-flow structures are each driven by entropy terms of the form -log ρ; they involve dissipation or mobility terms of order ρ² for the linear heat equation, and a nonlinear function of ρ for the nonlinear heat equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Work flow of signal processing data of ground penetrating radar case of rigid pavement measurements

The signal processing of Ground Penetrating Radar (GPR) requires a certain work flow to obtain good results. Even though the Ground Penetrating Radar data looks similar with seismic reflection data, but the GPR data has particular signatures that the seismic reflection data does not have. This is something to do with coupling between antennae and the ground surface. Because of this, the GPR data should be treated differently from the seismic signal data processing work flow. Even though most of the processing steps still follow the same work flow of seismic reflection data such as: filtering, predictive deconvolution etc. This paper presents the work flow of GPR processing data on rigid pavement measurements. The processing steps start from raw data, de-Wow process, remove DC and continue with the standard process to get rid of noises i.e. filtering process. Some radargram particular features of rigid pavement along with pile foundations are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

ENRICHMENT OF r-PROCESS ELEMENTS IN DWARF SPHEROIDAL GALAXIES IN CHEMO-DYNAMICAL EVOLUTION MODEL

The rapid neutron-capture process (r-process) is a major process for the synthesis of elements heavier than iron-peak elements, but the astrophysical site(s) of the r-process has not yet been identified. Neutron star mergers (NSMs) are suggested to be a major r-process site according to nucleosynthesis studies. Previous chemical evolution studies, however, required unlikely short merger times of NSMs to reproduce the observed large star-to-star scatters in the abundance ratios of r-process elements to iron: the [Eu/Fe] of extremely metal-poor stars in the Milky Way (MW) halo. This problem can be solved by considering chemical evolution in dwarf spheroidal galaxies (dSphs), which would be building blocks of the MW and have lower star formation efficiencies than the MW halo. We demonstrate the enrichment of r-process elements in dSphs by NSMs using an N-body/smoothed particle hydrodynamics code. Our high-resolution model reproduces the observed [Eu/Fe] due to NSMs with a merger time of 100 Myr when the effect of metal mixing is taken into account. This is because metallicity is not correlated with time ∼300 Myr from the start of the simulation due to the low star formation efficiency in dSphs. We also confirm that this model is consistent with observed properties of dSphs such as radial profiles and metallicity distribution. The merger time and the Galactic rate of NSMs are suggested to be ≲300 Myr and ∼10{sup −4} year{sup −1}, respectively, which are consistent with the values suggested by population synthesis and nucleosynthesis studies. This study supports the argument that NSMs are the major astrophysical site of the r-process.

79 ASTRONOMY AND ASTROPHYSICS↗

PRODUCTION OF ALL THE r-PROCESS NUCLIDES IN THE DYNAMICAL EJECTA OF NEUTRON STAR MERGERS

Recent studies suggest that binary neutron star (NS-NS) mergers robustly produce heavy r-process nuclei above the atomic mass number A ∼ 130 because their ejecta consist of almost pure neutrons (electron fraction of Y {sub e} < 0.1). However, the production of a small amount of the lighter r-process nuclei (A ≈ 90-120) conflicts with the spectroscopic results of r-process-enhanced Galactic halo stars. We present, for the first time, the result of nucleosynthesis calculations based on the fully general relativistic simulation of a NS-NS merger with approximate neutrino transport. It is found that the bulk of the dynamical ejecta are appreciably shock-heated and neutrino processed, resulting in a wide range of Y {sub e} (≈0.09-0.45). The mass-averaged abundance distribution of calculated nucleosynthesis yields is in reasonable agreement with the full-mass range (A ≈ 90-240) of the solar r-process curve. This implies, if our model is representative of such events, that the dynamical ejecta of NS-NS mergers could be the origin of the Galactic r-process nuclei. Our result also shows that radioactive heating after ∼1 day from the merging, which gives rise to r-process-powered transient emission, is dominated by the β-decays of several species close to stability with precisely measured half-lives. This implies that the total radioactive heating rate for such an event can be well constrained within about a factor of two if the ejected material has a solar-like r-process pattern.

79 ASTRONOMY AND ASTROPHYSICS↗

THE ORIGINS OF LIGHT AND HEAVY R-PROCESS ELEMENTS IDENTIFIED BY CHEMICAL TAGGING OF METAL-POOR STARS

Growing interests in neutron star (NS) mergers as the origin of r-process elements have sprouted since the discovery of evidence for the ejection of these elements from a short-duration γ-ray burst. The hypothesis of a NS merger origin is reinforced by a theoretical update of nucleosynthesis in NS mergers successful in yielding r-process nuclides with A > 130. On the other hand, whether the origin of light r-process elements are associated with nucleosynthesis in NS merger events remains unclear. We find a signature of nucleosynthesis in NS mergers from peculiar chemical abundances of stars belonging to the Galactic globular cluster M15. This finding combined with the recent nucleosynthesis results implies a potential diversity of nucleosynthesis in NS mergers. Based on these considerations, we are successful in the interpretation of an observed correlation between [light r-process/Eu] and [Eu/Fe] among Galactic halo stars and accordingly narrow down the role of supernova nucleosynthesis in the r-process production site. We conclude that the tight correlation by a large fraction of halo stars is attributable to the fact that core-collapse supernovae produce light r-process elements while heavy r-process elements such as Eu and Ba are produced by NS mergers. On the other hand, stars in the outlier, composed of r-enhanced stars ([Eu/Fe] ≳ +1) such as CS22892-052, were exclusively enriched by matter ejected by a subclass of NS mergers that is inclined to be massive and consist of both light and heavy r-process nuclides.

79 ASTRONOMY AND ASTROPHYSICS↗

Data processing and analysis with the autoPROC toolbox

Typical topics and problems encountered during data processing of diffraction experiments are discussed and the tools provided in the autoPROC software are described. A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhelmed by the tables, plots and numbers that the different data-processing programs and software packages present to them. Here, some of the more common problems that a user has to deal with when processing a set of images that will finally make up a processed data set are shown, concentrating on difficulties that may often show up during the first steps along the path of turning the experiment (i.e. data collection) into a model (i.e. interpreted electron density). Difficulties such as unexpected crystal forms, issues in crystal handling and suboptimal choices of data-collection strategies can often be dealt with, or at least diagnosed, by analysing specific data characteristics during processing. In the end, one wants to distinguish problems over which one has no immediate control once the experiment is finished from problems that can be remedied a posteriori. A new software package, autoPROC, is also presented that combines third-party processing programs with new tools and an automated workflow script that is intended to provide users with both guidance and insight into the offline processing of data affected by the difficulties mentioned above, with particular emphasis on the automated treatment of multi-sweep data sets collected on multi-axis goniostats.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weight asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. Here, we conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP and CC). On all the tested applications and datasets, Frog is able to significantly outperform existing GPU-based graph processing systems except Gunrock and MapGraph. MapGraph gets better performance than Frog when running BFS on RoadNet-CA. The comparison between Gunrock and Frog is inconclusive. Frog can outperform Gunrock more than 1.04X when running PageRank and SSSP, while the advantage of Frog is not obvious when running BFS and CC on some datasets especially for RoadNet-CA.

97 MATHEMATICS AND COMPUTING↗

SU-E-T-497: Semi-Automated in Vivo Radiochromic Film Dosimetry Using a Novel Image Processing Algorithm

Purpose: To validate an automated image processing algorithm designed to detect the center of radiochromic film used for in vivo film dosimetry against the current gold standard of manual selection. Methods: An image processing algorithm was developed to automatically select the region of interest (ROI) in *.tiff images that contain multiple pieces of radiochromic film (0.5x1.3cm{sup 2}). After a user has linked a calibration file to the processing algorithm and selected a *.tiff file for processing, an ROI is automatically detected for all films by a combination of thresholding and erosion, which removes edges and any additional markings for orientation. Calibration is applied to the mean pixel values from the ROIs and a *.tiff image is output displaying the original image with an overlay of the ROIs and the measured doses. Validation of the algorithm was determined by comparing in vivo dose determined using the current gold standard (manually drawn ROIs) versus automated ROIs for n=420 scanned films. Bland-Altman analysis, paired t-test, and linear regression were performed to demonstrate agreement between the processes. Results: The measured doses ranged from 0.2-886.6cGy. Bland-Altman analysis of the two techniques (automatic minus manual) revealed a bias of -0.28cGy and a 95% confidence interval of (5.5cGy,-6.1cGy). These values demonstrate excellent agreement between the two techniques. Paired t-test results showed no statistical differences between the two techniques, p=0.98. Linear regression with a forced zero intercept demonstrated that Automatic=0.997*Manual, with a Pearson correlation coefficient of 0.999. The minimal differences between the two techniques may be explained by the fact that the hand drawn ROIs were not identical to the automatically selected ones. The average processing time was 6.7seconds in Matlab on an IntelCore2Duo processor. Conclusion: An automated image processing algorithm has been developed and validated, which will help minimize user interaction and processing time of radiochromic film used for in vivo dosimetry.

60 APPLIED LIFE SCIENCES↗

WE-H-BRC-04: Implement Lean Methodology to Make Our Current Process of CT Simulation to Treatment More Efficient

Purpose: Our goal is to implement lean methodology to make our current process of CT simulation to treatment more efficient. Methods: In this study, we implemented lean methodology and tools and employed flowchart in excel for process-mapping. We formed a group of physicians, physicists, dosimetrists, therapists and a clinical physics assistant and huddled bi-weekly to map current value streams. We performed GEMBA walks and observed current processes from scheduling patient CT Simulations to treatment plan approval. From this, the entire workflow was categorized into processes, sub-processes, and tasks. For each process we gathered data on touch time, first time quality, undesirable effects (UDEs), and wait-times from relevant members of each task. UDEs were binned per frequency of their occurrence. We huddled to map future state and to find solutions to high frequency UDEs. We implemented visual controls, hard stops, and documented issues found during chart checks prior to treatment plan approval. Results: We have identified approximately 64 UDEs in our current workflow that could cause delays, re-work, compromise the quality and safety of patient treatments, or cause wait times between 1 – 6 days. While some UDEs are unavoidable, such as re-planning due to patient weight loss, eliminating avoidable UDEs is our goal. In 2015, we found 399 issues with patient treatment plans, of which 261, 95 and 43 were low, medium and high severity, respectively. We also mapped patient-specific QA processes for IMRT/Rapid Arc and SRS/SBRT, involving 10 and 18 steps, respectively. From these, 13 UDEs were found and 5 were addressed that solved 20% of issues. Conclusion: We have successfully implemented lean methodology and tools. We are further mapping treatment site specific workflows to identify bottlenecks, potential breakdowns and personnel allocation and employ tools like failure mode effects analysis to mitigate risk factors to make this process efficient.

60 APPLIED LIFE SCIENCES↗

DOE Hydrogen Sorption Center of Excellence: Synthesis and Processing of Single-Walled Carbon Nanohorns for Hydrogen Storage and Catalyst Supports

The objective of the project was to exploit the unique morphology, tunable porosity and excellent metal supportability of single-walled carbon nanohorns (SWNHs) to optimize hydrogen uptake and binding energy through an understanding of metal-carbon interactions and nanoscale confinement. SWNHs provided a unique material to understand these effects because they are carbon nanomaterials which are synthesized from the 'bottom-up' with well-defined, sub-nm pores and consist of single-layer graphene, rolled up into closed, conical, horn-shaped units which form ball-shaped aggregates of {approx}100-nm diameter. SWNHs were synthesized without metal catalysts by the high-temperature vaporization of solid carbon, so they can be used to explore metal-free hydrogen storage. However, SWNHs can also be decorated with metal nanoparticles or coatings in post-processing treatments to understand how metals augment hydrogen storage. The project first explored how the synthesis and processing of SWNHs could be modified to tailor pore sizes to optimal size ranges. Nanohorns were rapidly synthesized at 20g/hr rates by high-power laser vaporization enabling studies such as neutron scattering with gram quantities. Diagnostics of the synthesis process including high-speed videography, fast pyrometry of the graphite target, and differential mobility analysis monitoring of particle size distributions were applied in this project to provide in situ process control of SWNH morphology, and to understand the conditions for different pore sizes. We conclude that the high-temperature carbon-vaporization process to synthesize SWNHs is scalable, and can be performed by electric arc or other similar techniques as economically as carbon can be vaporized. However, the laser vaporization approach was utilized in this project to permit the precise tuning of the synthesis process through adjustment of the laser pulse width and repetition rate. A result of this processing control in the project was to eliminate the large (2-3 nm) internal pores of typical SWNHs which were found not to store hydrogen effectively. Post processing treatments of the as-synthesized SWNHs focused on pore size, surface area, and metal decoration in order to understand the effects of each on measured hydrogen uptake. Wet chemistry and gas-phase oxidation treatments were developed throughout the life of the project to adjust the interstitial and slit pore sizes of the as-produced SWNHs, and increase the surface area to a maximum value of 2200 m2/g. In addition, wet chemistry approaches were used to develop methods to decorate the nanohorns with small Pt and Pd nanoparticles for metal-assisted hydrogen storage. Finally, oxygen-free decoration of SWNHs with alkaline earth metals (Ca) was developed using pulsed laser deposition and vacuum evaporation in order to produce surface coatings with high static electric fields sufficient to polarize and bind dihydrogen. Decoration of SWNHs with Pt and Pd nanoparticles resulted in enhanced binding energy (NREL, 36 kJ/mol), as well as enhancement in the room temperature uptake of 0.6 wt.% (for undecorated, oxidized, pure-C SWNHs at 20 bar), to 1.6 wt% for Pt- and Pd-decorated SWNHs at 100 bar, comparable to MOF-177 materials. NIST neutron scattering on gram quantity Pt- and Pd-decorated SWNHs showed clear evidence for 'spillover' type losses of molecular hydrogen and determined the onset temperature for this effect to be between 150K < T < 298K.High (2142 m2/g) surface area SWNH materials with variable pore sizes and metal-decorated SWNHs were demonstrated with metals (Pt, Pd) resulting in increased excess storage (3.5 wt. % at 77K). Compression results in bulk SWNH samples with density 1.03 g/cm3, and 30 g/L volumetric capacity. In summary, SWNHs were found to be unique carbon nanomaterials which can be produced continuously at high rates from vaporization of pure carbon. Their inherent pore structure exhibits significant room temperature hydrogen storage in sub-nm pores, and their morphology serves as an excellent metal catalyst support for small (2-3 nm nanoparticles). Pt- and Pd-nanoparticle-decorated SWNHs exhibit clear evidence for metal-assisted hydrogen storage which is activated at T>150 K, permitting additional room-temperature storage up to 1.8 wt.% at 100 bar. One of the key results of the project were theoretical predictions for doped, decorated, and filled nanostructures with distributed charge to maintain high static electric fields sufficient to polarize and bind hydrogen. This concept indicates a promising new direction for hydrogen storage materials.

08 HYDROGEN↗

Report on Characterization and Processing of MDD Powder

Uranium oxide powers most civilian nuclear reactors worldwide. A large infrastructure based on a well-established technology is in place to support this strategic component of the energy industry. Because uranium oxide fuels are used so ubiquitously, it is expected that ceramic fuel pellets will continue to be used. A better understanding of the properties of the starting materials, the processing methods used to fabricate fuel pellets and how the properties of pellets change in service, are important aspects being studied via experiments, models and simulations. A close integration of these approaches is essential if we are to find new ways to optimize both the fuel composition and structure for the purpose of improving performance, e.g., designed microstructures, reducing process losses, e.g. by net shape sintering, and enabling reprocessing of used fuel; e.g., incorporation of transuranics. Ceramic oxide fuel pellets are typically cold pressed and sintered from a powder feedstock. Consequently, a complete understanding of pellet fabrication requires a thorough knowledge of the process from powder synthesis through quality control and acceptance. In this study, uranium oxide powder synthesized by Modified Direct Denitration (MDD) is evaluated. Use of powders synthesized by novel, simplified approaches such as MDD are both a challenge and an opportunity. The MDD synthesis process offers an opportunity to simplify the fabrication process potentially reducing process losses. MDD also provides a simple path to incorporate transuranics from used fuel reprocessing with minimal handling. The challenge is to demonstrate and ultimately prove the reliability and reproducibility of simplified processing with the performance of fuel pellets experiencing in-pile service. This report summarizes a processing study of uranium oxide pellets made from MDD uranium oxide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗