Optimizing the SERS activity of 1,10 Phenanthroline molecule adsorbed on Au-Ag core-shell nano colloid
No abstract prepared.
74 ATOMIC AND MOLECULAR PHYSICS↗
SEARCH · Engineering Papers
Results for “core optimization”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Extragalactic sources in Cosmic Microwave Background maps
We discuss the potential of a next generation space-borne CMB experiment for studies of extragalactic sources with reference to COrE+, a project submitted to ESA in response to the call for a Medium-size mission (M4). We consider three possible options for the telescope size: 1 m, 1.5 m and 2 m (although the last option is probably impractical, given the M4 boundary conditions). The proposed instrument will be far more sensitive than Planck and will have a diffraction-limited angular resolution. These properties imply that even the 1 m telescope option will perform substantially better than Planck for studies of extragalactic sources. The source detection limits as a function of frequency have been estimated by means of realistic simulations taking into account all the relevant foregrounds. Predictions for the various classes of extragalactic sources are based on up-to-date models. The most significant improvements over Planck results are presented for each option. COrE+ will provide much larger samples of truly local star-forming galaxies (by about a factor of 8 for the 1 m telescope, of 17 for 1.5 m, of 30 for 2 m), making possible analyses of the properties of galaxies (luminosity functions, dust mass functions, star formation rate functions, dust temperature distributions, etc.) across the Hubble sequence. Even more interestingly, COrE+ will detect, at |b| > 30°, thousands of strongly gravitationally lensed galaxies (about 2,000, 6,000 and 13,000 for the 1 m, 1.5 m and 2 m options, respectively). Such large samples are of extraordinary astrophysical and cosmological value in many fields. Moreover, COrE+ high frequency maps will be optimally suited to pick up proto-clusters of dusty galaxies, i.e. to investigate the evolution of large scale structure at larger redshifts than can be reached by other means. Thanks to its high sensitivity COrE+ will also yield a spectacular advance in the blind detection of extragalactic sources in polarization: we expect that it will detect up to a factor of 40 (1 m option) or of 160 (1.5 m option) more radio sources than can be detected by Planck and, for the first time, from several tens (1 m option) to a few hundreds (1.5 m option) of star forming galaxies. This will open a new window for studies of the global properties of magnetic fields in star forming galaxies and of their relationships with star formation rates.
79 ASTRONOMY AND ASTROPHYSICS↗
NASA High Efficiency, High OPR Capable Small Core Compressor
As future aircraft become lighter and more aerodynamically efficient, thrust requirements will decrease, reducing the core size of the engine. Furthermore, in the pursuit of improved fuel burn, engine overall pressure ratio and bypass ratio will increase, further driving down engine core size (core size being defined as high-pressure compressor [HPC] exit-corrected flow). These drivers together mean that the core size for future single-aisle aircraft applications will shrink below 3.0 lb/s. Traditionally, this small core compressor size is in the domain of axi-centrifugal designs, machines that are typically less efficient and limited to pressure ratios of ~25 due to stress and thermomechanical fatigue in the centrifugal impeller. In this light, NASA and Pratt & Whtiney (P&W) embarked upon a program to develop technologies to enable an all-axial high-pressure compressor with a core size below 3.0 lb/s and an overall pressure ratio greater than 50. The challenge with an all-axial high-pressure compressor at this core size is the small span at the rear of the compressor. As core size is scaled down, the rotor tip clearances, stator hub seal clearances, fillet sizes and leading edge thicknesses do not scale, leading to significant efficiency penalties. The goal of this program is to recover this lapse and realize the cycle benefits of small core size and high overall pressure ratio. The small core challenges described are mitigated through design optimization and technology insertion, enabling an estimated 5 to 10% fuel burn reduction relative to 2020 best-in-class. Three test rigs run at NASA Glenn Research Center evolved the small core design: a low-speed rig to vet technology and validate tools, and two high speeds rigs, the first to demonstrate an optimized meanline design and the second to validate technology to manage large rotor tip gaps. The efficiency improvement validated with these rigs has unlocked the small core design space, demonstrating that small core compressors can maintain a similar efficiency to current best-in-class large core size compressors. In addition to advancing the state-of-the-art of technology, the program has also advanced the modeling standards for multistage compressors with large clearance-to-span ratios. A best practice modeling standard was developed over the course of the program, incorporating learning from all three rig programs.
Axial Compressors↗
Hot Zero and Full Power Validation of PHISICS RELAP-5 Coupling
PHISICS is a reactor analysis toolkit developed in the last 3 years at the Idaho National Laboratory that has been also coupled with the thermo-hydraulic plant simulator RELAP5-3D. PHISICS is aimed to provide an optimal trade off between needed computational resources and accuracy in the range of 10~100 cores. In fact this range has been identified as the next 5 to 10 years average computational capability available to nuclear engineer designing and optimizing nuclear reactor cores. Different publication has been already presented [1] showing test of the single modules composing the PHISICS package. Lately the Idaho National Laboratory had the opportunity to access to plant data for the first cycle of a PWR including Hot Zero Power (HZP) and Hot Full Power (HFP). This data provided the opportunity to validate the transport solver, the interpolation capability for mixed macro and micro cross section and the criticality search option of the PHISICS package. In the following we will firstly recall briefly the structure of the different PHISICS modules and then we will illustrate the modeling process and some preliminary results.
97 MATHEMATICS AND COMPUTING↗
Physics basis for design of 3D coils in tokamaks
Recent progress in 3D tokamak modeling is now leveraged to create a conceptual design of new external 3D field coils for the DIII-D tokamak. In this work generalized perturbed equilibrium code is used to determine optimally efficient spectrum for driving total, core, and edge neoclassical toroidal viscosity torque. These fundamental modes of 3D control are shown to have consistent outboard structures across a wide variety of plasma scenarios and machines. Given these target spectra, the currents and 3D geometry of multiple coils can be optimized to increase efficient drive for the physics of interest without undesired secondary effects. Here, this nonlinear optimization is demonstrated using the flexible optimized coils using space-curves code. The optimized coils are individually distorted in space, creating toroidal 'arrays' containing a variety of shapes that often wrap around a significant poloidal extent of the machine. Importantly, efficient coupling can be maintained even when enforcing large distances between coils and the plasma during the geometric optimization of coil designs. Finally, the physics-driven optimization presented here thus provides a practical path to utilizing coils built on the exterior of the vacuum chamber in future reactors to obtain the powerful 3D field benefits demonstrated on current machines with close, internal coils.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Local Electric Field Effects on Water Dissociation in Bipolar Membranes Studied Using Core–Shell Catalysts
The local electric field strength is thought to affect the rate of water dissociation (WD) in bipolar membranes (BPMs) at the catalyst–nanoparticle surfaces. Here, we study core–shell nanoparticles, where the core is metallic, semiconducting, or insulating, to understand this effect. The nanoparticle cores were coated with a WD catalyst layer (TiO2 or HfO2) via atomic layer deposition (ALD), and the morphology was imaged with transmission electron microscopy. Irrespective of the core material, these core–shell catalysts displayed comparable WD overpotentials at optimal mass loading, despite the hypothesized differences in the electric field strength across the catalyst particle suggested by continuum electrostatic simulations. Substantial atomic interdiffusion between the core and shell was ruled out by X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and diffuse reflectance optical measurements. However, the optimal mass loading of catalyst was roughly 1 order of magnitude higher for the conductive and high dielectric core materials than for the low dielectric insulating cores. These findings are consistent with the hypothesis that electric field screening within the core material focuses the electric field drop between particles such that larger film thicknesses can be tolerated. Collectively, these data support the idea that it is the local electric field at the molecular level that controls proton-transfer rates and that the metal core/dielectric-shell constructs introduced here modulate that field. Further materials and synthetic design may enable optimization of the electric field strength across the proton-transfer trajectory at the material surface.
Sarma, Prasad V↗
Thermoelectric characteristic of the rough InN/GaN core-shell nanowires
An analysis to model the thermoelectric (TE) effects of the rough InN/GaN core-shell nanowires (NWs) with wire diameter ranging from 25 nm to 100 nm is proposed. The elastic continuum model is employed to calculate the phonon dispersion relation curves and the related phonon group velocity. Within the framework of Boltzmann transport equations and relaxation time approximation, the electrical conductivity, Seebeck coefficient, electronic thermal conductivity, and the lattice thermal conductivity is obtained. Simulation results indicate that TE properties of the rough InN/GaN core-shell NWs are strongly affected by the surface roughness and the diameter of NWs. The optimized condition of the proposed rough InN/GaN core-shell TE NWs is studied in this paper and the highest ZT obtained in the calculation is 0.8598 at 300 K and 1.713 at 1000 K.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Multi-core processing and scheduling performance in CMS
Commodity hardware is going many-core. We might soon not be able to satisfy the job memory needs per core in the current single-core processing model in High Energy Physics. In addition, an ever increasing number of independent and incoherent jobs running on the same physical hardware not sharing resources might significantly affect processing performance. It will be essential to effectively utilize the multi-core architecture. CMS has incorporated support for multi-core processing in the event processing framework and the workload management system. Multi-core processing jobs share common data in memory, such us the code libraries, detector geometry and conditions data, resulting in a much lower memory usage than standard single-core independent jobs. Exploiting this new processing model requires a new model in computing resource allocation, departing from the standard single-core allocation for a job. The experiment job management system needs to have control over a larger quantum of resource since multi-core aware jobs require the scheduling of multiples cores simultaneously. CMS is exploring the approach of using whole nodes as unit in the workload management system where all cores of a node are allocated to a multi-core job. Whole-node scheduling allows for optimization of the data/workflow management (e.g. I/O caching, local merging) but efficient utilization of all scheduled cores is challenging. Dedicated whole-node queues have been setup at all Tier-1 centers for exploring multi-core processing workflows in CMS. We present the evaluation of the performance scheduling and executing multi-core workflows in whole-node queues compared to the standard single-core processing workflows.
Hernandez, J. M.↗
DESIGN CHARACTERISTICS OF THE IDAHO NATIONAL LABORATORY HIGH-[TEMPERATURE GAS-COOLED TEST REACTOR
A point design for a graphite-moderated, high-temperature, gas-cooled test reactor (HTG TR) has been developed by Idaho National Laboratory (INL) as part of a United States (U.S.) Department of Energy (DOE) initiative to explore and potentially expand the existing U.S. test reactor capabilities. This paper provides a summary of the design and its main attributes. The 200 MW HTG TR is a thermal-neutron spectrum reactor composed of hexagonal prismatic fuel and graphite reflector blocks. Twelve fuel columns (96 fuel blocks total and 6.34 m active core height) are arranged in two hexagonal rings to form a relatively compact, high-power density, annular core sandwiched between inner, outer, top, and bottom graphite reflectors. The HTG-TR is designed to operate at 7 MPa with a coolant inlet/outlet temperature of 325°C/650°C, and utilizes TRISO particle fuel from the DOE AGR Program with 425 ?m uranium oxycarbide (UCO) kernels and an enrichment of 15.5 wt% 235U. The primary mission of the HTG TR is material irradiation and therefore the core has been specifically designed and optimized to provide the highest possible thermal and fast neutron fluxes. The highest thermal neutron flux (3.90E+14 n/cm2s) occurs in the outer reflector, and the maximum fast flux levels (1.17E+14 n/cm2s) are produced in the central reflector column where most of the graphite has been removed. Due to high core temperatures under accident conditions, all the irradiation test facilities have been located in the inner and outer reflectors where fast flux levels decline. The core features a large number of irradiation positions with large test volumes and long test lengths, ideal for thermal neutron irradiation of large test articles. The total available test volume is more than 1100 liters. Up to four test loop facilities can be accommodated with pressure tube boundaries to isolate test articles and test fluids (e.g., liquid metal, liquid salt, light water) from the helium primary coolant system.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Sensitivity analysis and optimization of the nuclear fuel cycle
A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and in core and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme. (authors)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Overview and Current Status of Analyses of Potential LEU Design Concepts for TREAT
Neutronic and thermal-hydraulic analyses have been performed to evaluate the performance of different low-enriched uranium (LEU) fuel design concepts for the conversion of the Transient Reactor Test Facility (TREAT) from its current high-enriched uranium (HEU) fuel. TREAT is an experimental reactor developed to generate high neutron flux transients for the testing of nuclear fuels. The goal of this work was to identify an LEU design which can maintain the performance of the existing HEU core while continuing to operate safely. A wide variety of design options were considered, with a focus on minimizing peak fuel temperatures and optimizing the power coupling between the TREAT core and test samples. Designs were also evaluated to ensure that they provide sufficient reactivity and shutdown margin for each control rod bank. Analyses were performed using the core loading and experiment configuration of historic M8 Power Calibration experiments (M8CAL). The Monte Carlo code MCNP was utilized for steady-state analyses, and transient calculations were performed with the point kinetics code TREKIN. Thermal analyses were performed with the COMSOL multi-physics code. Using the results of this study, a new LEU Baseline design concept is being established, which will be evaluated in detail in a future report.
Connaway, H. M.↗
PuReMD-GPU: A reactive molecular dynamics simulation package for GPUs
We present an efficient and highly accurate GP-GPU implementation of our community code, PuReMD, for reactive molecular dynamics simulations using the ReaxFF force field. PuReMD and its incorporation into LAMMPS (Reax/C) is used by a large number of research groups worldwide for simulating diverse systems ranging from biomembranes to explosives (RDX) at atomistic level of detail. The sub-femtosecond time-steps associated with ReaxFF strongly motivate significant improvements to per-timestep simulation time through effective use of GPUs. This paper presents, in detail, the design and implementation of PuReMD-GPU, which enables ReaxFF simulations on GPUs, as well as various performance optimization techniques we developed to obtain high performance on state-of-the-art hardware. Comprehensive experiments on model systems (bulk water and amorphous silica) are presented to quantify the performance improvements achieved by PuReMD-GPU and to verify its accuracy. In particular, our experiments show up to 16× improvement in runtime compared to our highly optimized CPU-only single-core ReaxFF implementation. PuReMD-GPU is a unique production code, and is currently available on request from the authors.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Design Optimization of Medium-Frequency Transformer for DAB Converters With DC Bias Capacity
Medium-frequency transformer (MFT) dc bias is critical for the safe operation of dual-active-bridge (DAB) converters. To analyze the dc bias, expressions of dc bias are derived for SiC MOSFET DAB converters. It shows that the dc bias is a current source in steady state; thus, a dc bias current capacity is defined and used as a design constraint in the optimization of MFT. The dc bias current capacity is related to core materials, geometric parameters, and flux density, which is analyzed with analytical expression. Optimization equations with geometric variables are derived, which can be solved theoretically or numerically for both interleaved winding (IW) and separated winding (SW) MFT without and/or with leakage inductance design. Based on the optimization equations, different MFT designs for a 150-kW SiC MOSFET DAB converter are compared on the aspects of core materials, winding structures, and without/with leakage inductance design. The combination of IW structure MFT with auxiliary phase shift inductors is chosen, and the assembled prototype is tested with the 150-kW DAB converter. Here, the designed dc bias capacity, temperature rise, and efficiency are verified.
42 ENGINEERING↗
Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM
In this paper, we study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4,000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.
97 MATHEMATICS AND COMPUTING↗
Design characteristics of the Idaho National Laboratory high-temperature gas-cooled test reactor - 18703
A point design for a graphite-moderated, high-temperature, gas-cooled test reactor (HTG-TR) has been developed by Idaho National Laboratory as part of a U.S. Department of Energy initiative to explore and potentially expand existing U.S. test reactor capabilities. This paper provides a summary of the design and its main attributes. The 200-MW HTG-TR is a thermal-neutron spectrum reactor composed of hexagonal prismatic fuel and graphite reflector blocks. The HTG-TR is designed to operate at 7 MPa with a coolant inlet/outlet temperature of 325/650 deg. C and utilizes tri-structural isotropic particle fuel with an enrichment of 15.5-wt% {sup 235}U. The primary mission of the HTG-TR is material irradiation, and therefore the core has been specifically designed and optimized to provide the highest possible thermal and fast-neutron fluxes. The highest thermal-neutron flux (3.90 E+14 n/cm{sup 2}/s) occurs in the outer reflector, and the maximum fast-flux levels (1.17 E+14 n/cm{sup 2}s) are produced in the central reflector column, where most of the graphite has been removed. The core features a large number of irradiation positions with large test volumes and long test lengths, providing an ideal environment for thermal-neutron irradiation of large test articles. The total available test volume is more than 1,100 L. Up to four test loop facilities can be accommodated with pressure tube boundaries to isolate test articles and test fluids (e.g., liquid metal, liquid salt, and light water) from the helium primary coolant system. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
CTF Parallel Performance Improvements
CTF is a modernized version of COBRA-TF (Coolant Boiling in Rod Arrays-Two Fluid), which is a thermal hydraulic (T/H) sub-channel code based on a two-phase, two-fluid model that formulates the conservation equations of mass, energy, and momentum for three fields of vapor, continuous liquid, and entrained liquid droplets. Under the Consortium for Advanced Simulation of Light Water Reactors (CASL) program sponsored by the U.S. Department of Energy, CTF is being further developed and improved for modeling and simulation of light water reactors. The code improvements for predicting T/H responses of pressurized water reactor (PWR) cores are focused on its applications to specific challenge problems deemed significant to the industry, such as departure from nucleate boiling (DNB) and crud-induced power shift. The code is also being used for T/H feedback in normal operating condition depletions in PWR and boiling water reactor designs in the reactor core simulator, Virtual Environment for Reactor Applications Core Simulator (VERA-CS), being developed by CASL. CTF modifications under the CASL program include software optimization, new closure models, and parallelization for modeling full reactor core T/H responses. The purpose of this work was to improve the parallel performance of CTF. CTF was originally parallelized via a domain-decomposition strategy using the Message Passing Interface (MPI), in which the basis of a solution domain was a physical assembly in the model. In other words, a user who wanted to perform a parallel solution with CTF had to provide the code one processor per assembly in the model (e.g., 56 cores for a 56 assembly quarter-core model, or 193 cores for a 193 assembly full-core model). This design was rigid, demanding a very specific number of processors for a model; furthermore, when CTF was run in VERA-CS-which typically uses thousands of processors- many processors sat idle during the CTF solve because CTF was unable to use any more processors than there are physical assemblies. This was not necessarily a problem if the CTF execution speed was very fast, but the average time spent in a CTF solve for prototypical PWR operating conditions was significant (i.e., over 20 % of the simulation wall time was being spent in CTF for a standard core depletion simulation). Larger, more computationally intensive simulations performed in support of the CASL DNB challenge problem demonstrated a need for improvements in this original parallelization approach. The specific goal of this work was to allow CTF to use more processors than there are physical assemblies in the model, which would not only speed up CTF and improve the overall efficiency of VERA-CS but also give users more-flexible options for executing their simulations. (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms
Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.
97 MATHEMATICS AND COMPUTING↗
Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs
Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.
Sivonxay, Eric [Lawrence Berkeley National Laborat↗