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At least 865 records · Page 48

Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs)

The Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs) are a set of prototype computational tools for simulating and optimizing midstream natural gas pipeline system operations subject to location and time-dependent hydrogen blending. The models can accurately resolve dynamic gas flows through large-scale pipeline networks using non-ideal gas equations of state. The codes can be used as decision support for planning and design decisions involving intra-day energy flow schedules as well as spatiotemporal economic values of natural gas, hydrogen, and net energy delivered to consumers while ensuring that pipeline hydraulic limitations, gas compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. The inputs to the codes are a model of the pipeline system as well as time-series data that specify boundary conditions on the network. For optimization, the code module requires price and quantity offers for natural gas and hydrogen and price and quantity bids for energy, which are used as time-dependent constraints in an optimal control problem. The outputs are time-series data that provide a predictive simulation of gas flows, mass fractions, and pressures, or with additional degrees of freedom give an approximately optimal solution for gas injections/withdrawals, compressor settings, and sensitivities to the objective function that provide locational values of energy.

Zlotnik, Anatoly↗

A Concept of a Convection–Cloud Chamber to Study Aerosol–Cloud–Drizzle Interactions

Understanding and quantifying the full chain of processes from aerosol activation to drizzle formation, and the associated feedbacks to the aerosol chemical and physical properties, all within a turbulent cloud are some of the toughest challenges in atmospheric chemistry and physics and are keys to the cloud–precipitation puzzle. This paper describes a concept for a new type of research facility consisting of a cloud chamber plus associated instrumentation and computational models, to explore aerosol–cloud interactions and processing, cloud optical properties, entrainment–cloud interactions, and quantitative assessment of drizzle onset. The envisioned design is for a 3 m × 3 m × 9 m chamber, such that the height is sufficient to achieve long lifetimes for aerosol processing and for significant drizzle growth by collision and coalescence. A suite of computational tools for simulating microphysical properties in the chamber provides a digital twin for designing the chamber and a range of example experiments. Theory and test results from novel remote sensing systems for exploring chemical and physical interactions and evolution of aerosols, cloud droplets, and drizzle within turbulent clouds are described. Testing of technology needed for the operation of a large-volume chamber, including aerosol generation methods and novel materials for water vapor boundary conditions, is described. Simulations suggest that spatially uniform turbulence and microphysical properties can be sustained in a steady state, with reasonable aerosol and water vapor fluxes, and that substantial drizzle can be produced through collision and coalescence of cloud droplets. Remaining challenges for more detailed engineering design and a discussion of possible first-light experiments are described.

54 ENVIRONMENTAL SCIENCES↗

Integrative omics analyses broaden treatment targets in human cancer

Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers. To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO. As a result, within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability. Finally, our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.

60 APPLIED LIFE SCIENCES↗

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Preliminary Designs for Criticality Safety Benchmarks—Iron/Steel/Chromium Series

The nuclear criticality safety community uses critical benchmarks extensively when analyzing nuclear systems. Having benchmarks sensitive to important materials of interest helps to ensure safety and constrain results. Critical benchmarks are also invaluable to the nuclear data community, which uses these benchmarks as a check and measure of quality of nuclear data libraries. Additionally, integral benchmarks are often used to validate the performance of computational tools for simulating nuclear systems. Many integral benchmarks have been documented in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook, the International Reactor Physics Experiment Evaluation Project (IRPhEP) handbook, and the Shielding Integral Benchmark Archive Database (SINBAD). The National Criticality Experiments Research Center (NCERC), operated by Los Alamos National Laboratory, is the only general purpose critical experiments facility in the United States of America. NCERC regularly designs and executes critical experiments and other measurements useful to a wide variety of fields including nuclear criticality safety, commercial nuclear energy, nonproliferation, and nuclear data. NCERC has recently developed tools to visualize gaps and deficiencies in existing criticality benchmarks – this work is still ongoing. Through this work and other reviews, some high priority deficiencies have been identified. This paper aims to highlight three areas where additional critical benchmarks would be useful to the nuclear criticality safety community and various other groups. Additionally, NCERC is seeking feedback in regard to these proposed measurements to make sure they are as useful to as many communities as possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Predicting HLA Class I Non-Permissive Amino Acid Residues Substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.

59 BASIC BIOLOGICAL SCIENCES↗

Robust, automated sleep scoring by a compact neural network with distributional shift correction

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

60 APPLIED LIFE SCIENCES↗

RC-SFA Data Management Templates and Guidance for Standardized, Reusable AI-Ready Data Packages

This data package provides templates and supporting documentation developed by the River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) to communicate its approach to managing and publishing AI-ready data. The package is intended to help data users and data producers understand the structures, metadata practices, and quality-control approaches that support consistent, reusable, and machine-actionable data products across RC-SFA studies. Rather than focusing on a single experimental dataset, this package documents the data management framework used to make RC-SFA data easier to find, ingest, navigate, and interpret. The materials in this package reflect RC-SFA practices for standardized data package organization, including the use of a human- and machine-readable README, file-level metadata, data dictionaries, descriptive file naming, method identifiers, and automated and review-based quality assurance procedures. Together, these components illustrate how RC-SFA extends FAIR data principles toward AI-readiness by prioritizing deep metadata, consistency across data packages, and support for informed downstream reuse by both humans and computational tools. This dataset is comprised of (1) readme; (2) presentation slides with an overview of RC-SFA approach and guidance; (3) document of RC-SFA best practices; (4) data dictionary (dd); (5) file level metadata (flmd); and a subfolder containing templates for dd and flmd. All files are .csv and .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.

AI-readiness↗

Data for Zheng et al. (2025), "AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry"

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties, but current models inadequately represent these complexities. This dataset contains the observational and modeling data from Zheng et al. (2025), which described a process-based modeling framework that couples soil solution chemistry with microbial carbon cycling reactions to study the impacts of soil salinization. This conceptual model is implemented numerically into the open-source geochemical program PHREEQC 3.0 (Parkhurst and Appelo, 2013). This dataset consists of: - Figure2_AquaMEND_salinity_buffer: Contains model simulation outputs to assess the impact of three different cation exchange and surface complexation processes on salinity buffering (Fig. 2 from Zheng et al. 2025). - Figure3_Salinity_function: Contains salinity function fitting for literature data (Fig. 3 from Zheng et al. 2025). - Figure4_AquaMEND_microbial_mechanisms: Contains model simulation outputs for testing various microbial process-based hypotheses related to soil salinization, including microbial mortality, carbon use efficiency (CUE), extracellular enzyme activity, and other microbial mechanisms (Fig. 4 from Zheng et al. 2025). - Figure5_AquaMEND_Redox: Contains on model simulation outputs to evaluate shifts among key redox processes, such as aerobic respiration, sulfate reduction, and methanogenesis (Fig.5 from Zheng et al. 2025). - Figure6_AquaMEND_sorption: Contains on model simulation outputs for investigating the effects of salinity on dissolved organic matter (DOM) sorption and desorption processes (Fig. 6 from Zheng et al. 2025). - Figure7_AquaMEND_process_couple: Contains on model simulation outputs for exploring coupled biotic-abiotic processes and their interactions (Fig. 7 from Zheng et al. 2025). - data: Includes datasets used to develop salinity response functions and evaluate salinity buffering capacity. Datasets for MEND model calibration. - database: Contains the `.dat` file required by PHREEQC for model execution. - README.md: A Markdown plain text file describing the computational tools and directories. Files are a mixture of plain text CSV (comma-separated value) and plain text *.dat files written by the model; no special software is required to read them.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL SALINIT↗

Complex concentrated alloys and chemical short-range ordering

Abstract Complex concentrated alloys (CCAs) are materials comprising three or more elements in similar proportions and possessing structural but no chemical long-range order. Fascination with CCAs has grown over the last 20 years and to date, CCAs have opened a new materials design paradigm and horizon for discovery of materials to meet the demands of applications in aggressive environments. Understanding the fundamental mechanisms controlling their response, however, is challenging due to the chemical and structural variations that wildly fluctuate over fine atomic and nanoscales. This issue focuses on the experimental, computational, and theoretical investigations that aim to uncover phenomena and processes determining the structure, kinetics, mechanics, or deformation mechanisms in CCAs at the atomic scale. At the atomic scale at which they operate, chemical short-range ordering can be influential. This issue further addresses the capabilities, as well as the debatable need, to characterize, predict, and relate short-range ordering to material performance. Collectively, the articles in this issue highlight the insights, understanding, and experimental and computational tools that attempt to create property-tunable CCAs “from the atom up” by treating short-range ordering and engineering atomic-scale mechanisms. Graphical abstract

36 MATERIALS SCIENCE↗

Progress on Beam-Plasma Effect Simulations in Muon Ionization Cooling Lattices

New computational tools are essential for accurate modeling and simulation of the next generation of muon-based accelerators. One of the crucial physics processes specific to muon accelerators that has not yet been simulated in detail is beam-induced plasma effect in liquid, solid, and gaseous absorbers. We report here on the progress of developing the required simulation tools and applying them to study the properties of plasma and its effects on the beam in muon ionization cooling channels.

43 PARTICLE ACCELERATORS↗

Electron cloud simulations in the Fermilab booster

As part of Fermilab's Proton Improvement Plan-II (PIP-II),the Fermilab Booster synchrotron will operate at a higherintensity, increasing from 4.5 x 10^12 to 6.7 x 10^12 protons per pulse (ppp). A potential challenge for achievinghigh intensity performance arises from rapid transverse instabilities induced by electron cloud (EC). This researchpresents EC simulations using PyECLOUD, which is anadvanced computational tool that incorporates measurements of the secondary electron yield (SEY) from theBooster's combined function magnet material. By systematically varying beam parameters in PyECLOUD, such asbunch structure, SEY, bunch length, and intensity, it becomes possible to forecast the impact of EC effects on thebeam stability of the PIP-II era Booster.

43 PARTICLE ACCELERATORS↗

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Progress Report for Diffusion Welding of the NGNP Process Application Heat Exchangers (Revision 1)

The U.S. Department of Energy selected the high temperature gas-cooled reactor as the basis for the Next Generation Nuclear Plant (NGNP). The NGNP will demonstrate the use of nuclear power for electricity, hydrogen production, and process heat applications. The NGNP Project is currently investigating the use of metallic, diffusion welded, compact heat exchangers to transfer heat from the primary (reactor side) heat transport system to the secondary heat transport system. An intermediate heat exchanger will transfer this heat to downstream applications such as hydrogen production, process heat, and electricity generation. The channeled plates that make up the heat transfer surfaces of the intermediate heat exchanger will have to be assembled into an array by diffusion welding. This report describes the preliminary results of a scoping study that evaluated the diffusion welding process parameters and the resultant mechanical properties of diffusion welded joints using Alloy 800H. The long-term goal of the program is to progress towards demonstration of small heat exchanger unit cells fabricated with diffusion welds. Demonstration through mechanical testing of the unit cells will support American Society of Mechanical Engineers rules and standards development, reduce technical risk, and provide proof of concept for heat exchanger fabrication methods needed to deploy heat exchangers in several potential NGNP configurations.1 Researchers also evaluated the usefulness of modern thermodynamic and diffusion computational tools (Thermo-Calc and Dictra) in optimizing the parameters for diffusion welding of Alloy 800H. The modeling efforts suggested a temperature of 1150°C for 1 hour with an applied pressure of 5 MPa using 15 μm nickel foil as joint filler to reduce chromium oxidation on the welded surfaces. Good agreement between modeled and experimentally determined concentration gradients was achieved

08 HYDROGEN↗

Review of the Oak Ridge National Laboratory (ORNL) Neutronic Calculations Regarding the Conversion of the High Flux Isotope Reactor (HFIR) to the Use of Low Enriched Uranium (LEU) Fuel

In awareness of the risk caused by the proliferation of nuclear materials, the international community initiated in 1978 the Reduced Enrichment for Research and Test Reactors (RERTR) Program. The goal of the RERTR program is to seek solutions to convert the reactors using High Enriched Uranium (HEU) fuel ( 235 U/U ≥ 20 wt. %) to the use of Low Enriched Uranium (LEU) Fuel ( 235 U/U < 20 wt. %). Among the 200 reactors worldwide currently in the scope of the program, about 70 have been converted or shut down prior to conversion. In the U.S., six high reactors remain to be converted. One of the U.S. reactors is the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). This multipurpose reactor is mainly used for neutron scattering experiments and isotope production. It has two fuel elements made of involute-shaped fuel plates. The ORNL Research Reactor Division (RRD) has prepared during FY2011 two series of calculations, one neutronic and one thermal-hydraulic (TH), in support of the conversion activities for HFIR. The neutronic calculations cover mainly the evaluation of the matrix of performance (cycle length and flux), the distribution of power in the fuel elements, kinetics parameters and reactivity coefficients. As defined by the RRD procedures, the calculations have to be reviewed. Per internal decision, the RRD proposed an external reviewer, Argonne National Laboratory (ANL). The results of the Argonne neutronic review are presented in this report. The results of the TH review are presented in a separate report. The main conclusion of the ORNL reactor physics analyses is that the proposed reference HFIR LEU core (using the so-called monolithic UMo fuel) could maintain the current level of performance (obtained with a HEU core at 85MW) if it is operated at 100MW. However, to operate within the same safety limits as the current HEU core, the thermal hydraulic analyses show that the peaking of the power distribution occurring at the bottom of the fuel plates has to be reduced. The RRD has found a potential solution by reducing the fuel thickness at the bottom of the fuel plate. This would require addition of an axial grading to the already radially-graded fuel. Thus, the review has been focused on independently recalculating all of the ORNL calculations and making an as fair as possible comparison between the current 85MW HEU core and the reference 100MW LEU core design. ORNL and ANL have used the same computational tools to perform the work. The codes MCNP and ORIGEN have been used to perform the steady state and depletion calculations, respectively. The communication between the two codes has been carried out by the code VESTA. Even though the code MCNP is able to handle complex geometries, it cannot model the involute shape. Nonetheless, ANL has developed an innovative methodology to model the involute shape with MCNP with an approximation that can be made as accurate as desired. Thus, ANL has been able to perform the calculations using an explicit (or discrete) description of the fuel elements. The current modeling used by ORNL consists in homogenizing the different fuel element components (cladding, fuel, coolant…). Although this method is easier to implement, it may also affect the reactor physics which may have an impact on the calculated results. Even though some differences in the physics parameters (flux for instance) have been detected between the HEU explicit (ANL) and homogenized (ORNL) models, they are small enough to not significantly impact the performance parameters. However, this is not the case for the LEU core where the differences in the physics parameters lead to significant differences in the performance parameters, especially the cycle length (found to be considerably longer in the explicit model). Nonetheless, the others parameters calculated by ANL (power distribution, kinetics parameters…) are in good agreement with those calculated by ORNL. Some inconsistencies have been detected in the original ORNL inputs that have been corrected in the reference ones used by ANL for the review and, therefore, the cycle length difference between the LEU homogenized and explicit models has been amplified. Consequently, the ANL comparison of the HFIR HEU and LEU cores has given different results than the ORNL results. The ORNL matrix of performance for the 100MW LEU reference design shows that the reactor cycle length would be as long and the flux as high as the current 85MW HEU core. In contrast, the ANL matrix of performance shows that the 100MW LEU design would have a significantly longer cycle length (~33days) than the current 85MW HEU core (~25days). In addition, it has been shown that, if the reactor is operated for 33days, the neutron fluence would be high enough to increase the overall reactor performance by a significant factor. If the reactor is forced to shut down after 25days, calculations show that the neutron flux would be high enough to maintain the performance of the neutron scattering experiments but losses are predicted to occur in several irradiation facilities. Increasing the cycle length may not be possible for other reasons. For instance, a longer cycle length can increase the cumulative oxide layer thickness growth on the plate. The increase in the neutron fluence may affect the lifetime of the reactor vessel and, by reducing the time for maintenance, pose operational challenges. An assessment of these as well as other potential questions associated with a 33 day cycle length is needed. Since such an assessment has not been carried out, ANL cannot conclude that the ORNL reference LEU core should or could operate at 100MW. The conclusions of the ANL review are different than those of the ORNL investigation mainly because the explicit and homogenized methodologies used to model the core are different. In this report, an attempt is made to justify and explain as clearly as possible why the ANL methodology is thought to be better suited for HFIR analyses. It is acknowledged that because the ANL and ORNL LEU models have not been benchmarked against experiments, it is possible that doubts could be raised concerning the ability of the models to correctly capture all of the physics behavior of the reactor.

Bergeron, A.↗

Application of Plasma Waveguides to High Energy Accelerators

The eventual success of laser-plasma based acceleration schemes for high-energy particle physics will require the focusing and stable guiding of short intense laser pulses in reproducible plasma channels. For this goal to be realized, many scientific issues need to be addressed. These issues include an understanding of the basic physics of, and an exploration of various schemes for, plasma channel formation. In addition, the coupling of intense laser pulses to these channels and the stable propagation of pulses in the channels require study. Finally, new theoretical and computational tools need to be developed to aid in the design and analysis of experiments and future accelerators. Here we propose a 3-year renewal of our combined theoretical and experimental program on the applications of plasma waveguides to high-energy accelerators. During the past grant period we have made a number of significant advances in the science of laser-plasma based acceleration. We pioneered the development of clustered gases as a new highly efficient medium for plasma channel formation. Our contributions here include theoretical and experimental studies of the physics of cluster ionization, heating, explosion, and channel formation. We have demonstrated for the first time the generation of and guiding in a corrugated plasma waveguide. The fine structure demonstrated in these guides is only possible with cluster jet heating by lasers. The corrugated guide is a slow wave structure operable at arbitrarily high laser intensities, allowing direct laser acceleration, a process we have explored in detail with simulations. The development of these guides opens the possibility of direct laser acceleration, a true miniature analogue of the SLAC RF-based accelerator. Our theoretical studies during this period have also contributed to the further development of the simulation codes, Wake and QuickPIC, which can be used for both laser driven and beam driven plasma based acceleration schemes. We will continue our development of advanced simulation tools by modifying the QuickPIC algorithm to allow for the simulation of plasma particle pick-up by the wake fields. We have also performed extensive simulations of plasma slow wave structures for efficient THz generation by guided laser beams or accelerated electron beams. We will pursue experimental studies of direct laser acceleration, and THz generation by two methods, ponderomotive-induced THz polarization, and THz radiation by laser accelerated electron beams. We also plan to study both conventional and corrugated plasma channels using our new 30 TW in our new lab facilities. We will investigate production of very long hydrogen plasma waveguides (5 cm). We will study guiding at increasing power levels through the onset of laser-induced cavitation (bubble regime) to assess the role played by the preformed channel. Experiments in direct acceleration will be performed, using laser plasma wakefields as the electron injector. Finally, we will use 2-colour ionization of gases as a high frequency THz source (<60 THz) in order for femtosecond measurements of low plasma densities in waveguides and beams.

43 PARTICLE ACCELERATORS↗

Optimization of Nano-Carbon Materials for Hydrogen Sorption

Research undertaken has added to the understanding of several critical areas, by providing both negative answers (and therefore eliminating expensive further studies of unfeasible paths) and positive feasible options for storage. Theoretical evaluation of the early hypothesis of storage on pure carbon single wall nanotubes (SWNT) has been scrutinized with the use of comprehensive computational methods (and experimental tests by the Center partners), and demonstrated that the fundamentally weak binding energy of hydrogen is not sufficiently enhanced by the SWNT curvature or even defects, which renders carbon nanotubes not practical media. More promising direction taken was towards 3-dimensional architectures of high porosity where concurrent attraction of H2 molecule to surrounding walls of nano-scale cavities can double or even triple the binding energy and therefore make hydrogen storage feasible even at ambient or somewhat lower temperatures. An efficient computational tool has been developed for the rapid capacity assessment combining (i) carbon-foam structure generation, (ii) accurate empirical force fields, with quantum corrections for the lightweight H2, and (iii) grand canonical Monte Carlo simulation. This made it possible to suggest optimal designs for carbon nanofoams, obtainable via welding techniques from SWNT or by growth on template-zeolites. As a precursor for 3D-foams, we have investigated experimentally the synthesis of VANTA (Vertically Aligned NanoTube Arrays). This can be used for producing nano-foams. On the other hand, fluorination of VANTA did not show promising increase of hydrogen sorption in several tests and may require further investigation and improvements. Another significant result of this project was in developing a fundamental understanding of the elements of hydrogen spillover mechanisms. The benefit of developed models is the ability to foresee possible directions for further improvement of the spillover mechanism.

08 HYDROGEN↗