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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Coordination-Supported Imidazolate Networks: Water- and Heat-Stable Mesoporous Polymers for Catalysis

The poor water stability of most porous coordination polymers (PCPs) or metal-organic frameworks (MOFs) is widely recognised as a barrier hampering their practical applications. Herein, a facile and scalable route to prepare metal-containing polymers with a good stability in boiling water (100°C, 24 h) and air (up to 390°C) is presented. The bifunctional 1-vinylimidazole (VIm) with both a coordinating site and a polymerizable organic group is introduced as the building block. This core strategy includes the synthesis of a rigid monomer with four VIm branches via a coordination process at room temperature, followed by a radical polymerization. Here we call this material Coordination-supported Imidazolate Networks (CINs). Interestingly, CINs are composed of rich mesopores from 2 to 15 nm, as characterized by low-energy (60 kV) STEM-HAADF images. Especially, the stable CINs illustrate a high turnover frequency (TOF) of 779 h -1 in the catalytic oxidation of phenol with H 2 O as the green solvent.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Probing DNA clamps with single-molecule force spectroscopy

Detailed mechanisms of DNA clamps in prokaryotic and eukaryotic systems were investigated by probing their mechanics with single-molecule force spectroscopy. Specifically, the mechanical forces required for the Escherichia coli and Saccharomyces cerevisiae clamp opening were measured at the single-molecule level by optical tweezers. Steered molecular dynamics simulations further examined the forces involved in DNA clamp opening from the perspective of the interface binding energies associated with the clamp opening processes. In combination with additional molecular dynamics simulations, we identified the contact networks between the clamp subunits that contribute significantly to the interface stability of the S.cerevisiae and E. coli clamps. These studies provide a vivid picture of the mechanics and energy landscape of clamp opening and reveal how the prokaryotic and eukaryotic clamps function through different mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Demonstration of Thermoacoustic Power Sensor

The goal of this project is to develop and demonstrate a thermoacoustic power sensor (TAPS) for Sodium-cooled Fast Reactor (SFR), with potential application also envisioned to other nuclear technologies such as Lead-Cooled Fast Reactor (LFR), Molten Salt Reactor (MSR), in addition to Light Water Reactors (LWRs). The project was led by Westinghouse Electric Company, LLC (Westinghouse) and carried out in collaboration with Argonne National Laboratory (ANL) and the University of Pittsburgh. A TAPS is a passive (self-powered), non-invasive (wireless) sensor envisioned for measuring key parameters, such as local temperature and neutron flux, in a nuclear reactor core. The sensor generates pressure waves (i.e., sound waves) with a frequency and amplitude dependent upon nuclear operating conditions (coolant temperature or power changes). The acoustic waves are able to travel through the core and associated structures, and reach to the detector network placed outside of the reactor vessel. These detectors require a very small amount of power which, during loss of power events, can be provided for example by harvesting gamma radiation energy, thus resulting in a monitoring system that can function both during normal operation and during loss of power events.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Ultralow-light-level all-optical transistor in rubidium vapor

An all-optical transistor (AOT) is a device in which one light beam can efficiently manipulate another. It is the foundational component of an all-optical communication network. An AOT that can operate at ultralow light levels is especially attractive for its potential application in the quantum information field. Here, we demonstrate an AOT driven by a weak light beam with an energy density of 2.5 × 10{sup −5} photons/(λ{sup 2}/2π) (corresponding to 6 yJ/(λ{sup 2}/2π) and about 800 total photons) using the double-Λ four-wave mixing process in hot rubidium vapor. This makes it a promising candidate for ultralow-light-level optical communication and quantum information science.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1992

The HEASARC graphical user interface

An OSF/Motif-based graphical user interface has been developed to facilitate the use of the database and data analysis software packages available from the High Energy Astrophysics Science Archive Research Center (HEASARC). It can also be used as an interface to other, similar, routines. A small number of tables are constructed to specify the possible commands and command parameters for a given set of analysis routines. These tables can be modified by a designer to affect the appearance of the interface screens. They can also be dynamically changed in response to parameter adjustments made while the underlying program is running. Additionally, a communication protocol has been designed so that the interface can operate locally or across a network. It is intended that this software be able to run on a variety of workstations and X terminals.

White, N.↗
NASA NTRS2011

NASA Tech Briefs, December 2011

Topics covered include: 1) SNE Industrial Fieldbus Interface; 2) Composite Thermal Switch; 3) XMOS XC-2 Development Board for Mechanical Control and Data Collection; 4) Receiver Gain Modulation Circuit; 5) NEXUS Scalable and Distributed Next-Generation Avionics Bus for Space Missions; 6) Digital Interface Board to Control Phase and Amplitude of Four Channels; 7) CoNNeCT Baseband Processor Module; 8) Cryogenic 160-GHz MMIC Heterodyne Receiver Module; 9) Ka-Band, Multi-Gigabit-Per-Second Transceiver; 10) All-Solid-State 2.45-to-2.78-THz Source; 11) Onboard Interferometric SAR Processor for the Ka-Band Radar Interferometer (KaRIn); 12) Space Environments Testbed; 13) High-Performance 3D Articulated Robot Display; 14) Athena; 15) In Situ Surface Characterization; 16) Ndarts; 17) Cryo-Etched Black Silicon for Use as Optical Black; 18) Advanced CO2 Removal and Reduction System; 19) Correcting Thermal Deformations in an Active Composite Reflector; 20) Umbilical Deployment Device; 21) Space Mirror Alignment System; 22) Thermionic Power Cell To Harness Heat Energies for Geothermal Applications; 23) Graph Theory Roots of Spatial Operators for Kinematics and Dynamics; 24) Spacesuit Soft Upper Torso Sizing Systems; 25) Radiation Protection Using Single-Wall Carbon Nanotube Derivatives; 26) PMA-PhyloChip DNA Microarray to Elucidate Viable Microbial Community Structure; 27) Lidar Luminance Quantizer; 28) Distributed Capacitive Sensor for Sample Mass Measurement; 29) Base Flow Model Validation; 30) Minimum Landing Error Powered-Descent Guidance for Planetary Missions; 31) Framework for Integrating Science Data Processing Algorithms Into Process Control Systems; 32) Time Synchronization and Distribution Mechanisms for Space Networks; 33) Local Estimators for Spacecraft Formation Flying; 34) Software-Defined Radio for Space-to-Space Communications; 35) Reflective Occultation Mask for Evaluation of Occulter Designs for Planet Finding; and 36) Molecular Adsorber Coating

Source record↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of an Enhanced Radiation Physics Toolset for Modeling Spectral and Imaging Signatures in the Warm Dense Matter Experiments

Radiative and atomic processes in plasmas play a critical role in a wide variety of high energy density laboratory plasma (HEDLP) experiments. The emission, absorption, and transport of radiation can strongly affect the overall energetics and evolution of such plasmas. In addition, radiation-based diagnostics – including imaging, spectroscopy, and absolute flux measurements – are widely used to determine key features of HEDLPs. To advance our understanding of HEDLP science, it is vital to have high-fidelity computational physics tools that have well-tested radiation physics modeling, and that are readily accessible to researchers in the HEDLP community. Simulations play an extremely important role for planning and designing the experiments, as well as for post-experiment data analysis. Prism Computational Sciences develops software that is used by National Laboratories and universities (including five members of LaserNetUS network). Prominent examples of such research efforts include z-pinch and short-pulse laser experiments designed to study the basic physics of photoionized plasmas and photoionization fronts, as well as their application to astrophysical plasmas. The main effort was dedicated to the development of non-equilibrium equation-of-state (EOS) models within the HELIOS-CR code, a hydrodynamics code with inline collisional-radiative atomic kinetics. Gas cell experiments on Z and Omega demonstrated the importance of non-equilibrium effects on atomic kinetics in photoionized plasmas. Recent proof-of-principle experiments on Omega EP confirmed the advantages of using a short-pulse laser to create an intense radiation drive, leading to additional experiments being proposed. Photoionization front experiments at LLE also emphasizes the importance of radiation and atomic physics. In both studies, HELIOS-CR simulations played a crucial role in computing non-equilibrium opacities and ionization distributions. A newly developed non-LTE EOS model will help addressing possible non-equilibrium effects, on for example specific heat, and their influence in plasma evolution. Prism also implemented support for open-source atomic data generated by the Flexible Atomic Code. This allows researchers to generate custom atomic tables and use them within the complex framework of simulation tools developed by Prism. The ability to use open-source atomic data would be extremely valuable for hydrodynamics and spectroscopic simulations that include high-Z materials, e.g., picosecond x-ray pulse generation experiments. Support for new atomic structures was fully implemented, and the data can be used by all simulation tools developed at Prism: radiation-hydrodynamics, imaging and spectroscopy, EOS and opacity. The development resulted in a significant fidelity enhancement to the simulations tools developed by Prism that are currently used in other cutting-edge experiments including: opacity measurement experiments performed to both understand the basic radiative and atomic properties of plasmas as well as provide data for more accurately modeling the internal structure of the Sun and other stars, high-intensity short-pulse laser experiments performed to develop short-wavelength light sources for use as backlighters and to investigate fast ignition concepts for inertial fusion energy; capsule implosion experiments designed to develop inertial fusion as an energy source, etc.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Nitrogen-doped carbon nanofibers derived from polypyrrole coated bacterial cellulose as high-performance electrode materials for supercapacitors and Li-ion batteries

Here, nitrogen-doped carbon nanofiber (NDCN) was synthesized via carbonization of polypyrrole (PPy) coated bacterial cellulose (BC) composites, where BC serves as templates as well as precursor, and PPy serves as the nitrogen source. The synthesized NDCN was employed as electrode for both supercapacitors and Li-ion batteries. The large surface area exposed to electrolyte resulting from the 3D carbon networks leads to sufficient electrode/electrolyte interface and creates shorter transport paths of electrolyte ions and Li + ion. Besides, the three types of N dopants in NDCN improve the electronic conductivity, as well as superior electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2019

Modular design: Implementing proven engineering principles in biotechnology

Modular design resides at the foundation of contemporary engineering, enabling rapid, efficient, and reproducible construction and maintenance of complex systems across applications. Remarkably, modularity has recently been discovered as a governing principle in natural biological systems from genes to proteins to complex networks within a cell and organism communities. The convergent knowledge of natural and engineered modular systems provides a key to drive modern biotechnology to address emergent challenges associated with health, food, energy, and the environment. Here, we first present the theory and application of modular design in traditional engineering fields. We further discuss the significance and impact of modular architectures on systems biology and biotechnology. Next, we focus on the very recent theoretical and experimental advances in modular cell engineering that seeks to enable rapid and systematic development of microbial catalysts capable of efficiently synthesizing a large space of useful chemicals. We conclude with an outlook towards theoretical and practical opportunities for a more systematic and effective application of modular engineering in biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Langston University - High Energy Physics (LU-HEP)

This final report is presented by Langston University (LU) for the project entitled "Langston University High Energy Physics" (LUHEP) under the direction of principal investigator (PI) and project director Professor Joel Snow. The project encompassed high energy physics research performed at hadron colliders. The PI is a collaborator on the DZero experiment at Fermi National Accelerator Laboratory in Batavia, IL, USA and the ATLAS experiment at CERN in Geneva, Switzerland and was during the entire project period from April 1, 1999 until May 14, 2012. Both experiments seek to understand the fundamental constituents of the physical universe and the forces that govern their interactions. In 1999 as member of the Online Systems group for Run 2 the PI developed a cross-platform Python-based, Graphical User Interface (GUI) application for monitoring and control of EPICS based devices for control room use. This served as a model for other developers to enhance and build on for further monitoring and control tasks written in Python. Subsequently the PI created and developed a cross-platform C++ GUI utilizing a networked client-server paradigm and based on ROOT, the object oriented analysis framework from CERN. The GUI served as a user interface to the Examine tasks running in the D\O\ control room which monitored the status and integrity of data taking for Run 2. The PI developed the histogram server/control interface to the GUI client for the EXAMINE processes. The histogram server was built from the ROOT framework and was integrated into the D\O\ framework used for online monitoring programs and offline analysis. The PI developed the first implementation of displaying histograms dynamically generated by ROOT in a Web Browser. The PI's work resulted in several talks and papers at international conferences and workshops. The PI established computing software infrastructure at LU and U. Oklahoma (OU) to do analysis of DZero production data and produce simulation data for the experiment. Eventually this included the FNAL SAM data grid system, the SAMGrid (SG) infrastructure, and the Open Science Grid software stacks for computing and storage elements. At the end of 2003 Snow took on the role of global Monte Carlo production coordinator for the DØ experiment. A role which continues til this day. In January of 2004 Snow started working with the SAMGrid development team to help debug, deploy, and integrate SAMGrid with DØ Monte Carlo production. Snow installed and configured SG execution and client sites at LUHEP and OUHEP, and a SG scheduler site at LUHEP. The PI developed a python based GUI (DAJ) that acts as a front end for job submission to SAMGrid. The GUI interfaces to the DZero Mone Carlo (MC) request system that uses SAM to manage MC requests by the physics analysis groups. DAJ significantly simplified SG job submission and was deployed in DZero in an effort to increase the user base of SG. The following year was the advent of SAMGrid job submission to the Open Science Grid (OSG) and LHC Computing Grid (LCG) through a forwarding mechanism. The PI oversaw the integration of these grids into the existing production infrastructure. The PI developed an automatic MC (Automc) request processing system capable of operating without user intervention (other than getting grid credentials), and able to submit to any number of sites on various grids. The system manages production at all but 2 sites. The system was deployed at Fermilab and remains operating there today. The PI's work in distributed computing resulted in several talks at international conferences. UTA, OU, and LU were chosen as the collaborating institutions that form the Southwest Tier 2 Center (SWT2) for ATLAS. During the project period the PI contributed to the online and offline software infrastructure through his work with the Run 2 online group, and played a major role in Monte Carlo production for DZero. During the part of the project period in which the PI served as MC production coordinator MC production increased very significantly. In the first year of the PI's tenure as production coordinator production was 159M events and 6.7~TB of data. During the last year of the project period production was 2,342~M events and 262~TB of data. That is a factor of 15 increase in events and 39 in data volume. The increase occurred with improvements in computer hardware and networks, through the use of grid technology on diverse resources, and through increased automation and efficiency of the production process. LU HEP developed and deployed the automatic MC request processing system in use at FNAL. The complementary strategies of automation and grid production served DZero well. Fermilab has recognized LU HEP's contribution to DZero by allowing the PI to devote full time to research activities by appointing him a guest scientist for the last six years of the project period.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to ‘co-design’ chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

Characterization of a New Phase of Ammonia Borane

An uncharacterized phase of ammonia borane (AB) was previously identified while investigating its thermal decomposition. This phase was identified as a key intermediate for the release of hydrogen from AB prompting the current characterization study. The new phase was investigated extensively using in situ solid state MAS NMR spectroscopy, including 11B, 15N and 1H NMR. Single-pulse excitation, cross polarization and T1 relaxation experiments collectively demonstrate a significant increase in mobility for the new phase when compared to the room temperature crystalline phase of AB. However, the data are not completely consistent with a liquid phase. The data are also not consistent with a solid phase, but are most consistent with a phase best described as “liquid-like”. This implies a disruption of the long-range order which is primarily stabilized by the extensive dihydrogen bonding network, allowing increased motional freedom. X-ray diffraction and Raman thermal decomposition data of neat AB provide support for a disordered phase, as does data of AB immobilized into a mesoporous scaffold. These observations suggest that a breakdown of the extensive hydrogen bonding network is necessary before hydrogen can be released and will be an important factor in the practical use of AB as a hydrogen storage material. Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy.

08 HYDROGEN↗
DOE OSTI2013

Sponge-like Si-SiO{sub 2} nanocomposite—Morphology studies of spinodally decomposed silicon-rich oxide

Sponge-like Si nanostructures embedded in SiO{sub 2} were fabricated by spinodal decomposition of sputter-deposited silicon-rich oxide with a stoichiometry close to that of silicon monoxide. After thermal treatment a mean feature size of about 3 nm was found in the phase-separated structure. The structure of the Si-SiO{sub 2} nanocomposite was investigated by energy-filtered transmission electron microscopy (EFTEM), EFTEM tomography, and atom probe tomography, which revealed a percolated Si morphology. It was shown that the percolation of the Si network in 3D can also be proven on the basis of 2D EFTEM images by comparison with 3D kinetic Monte Carlo simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Networking: the view from HEP

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. National and global-scale collaborations that characterize HEP would not be feasible without ubiquitous capable networks. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. This paper will briefly discuss the history of networking in HEP, the current activities and challenges we are facing, and try to provide some understanding of where networking may be going in the next 5 to 10 years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Geophysical survey associated with NEON AOP survey, East River, CO 2018

The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2012

Standardized Testing Program for Solid-State Hydrogen Storage Technologies

In the US and abroad, major research and development initiatives toward establishing a hydrogen-based transportation infrastructure have been undertaken, encompassing key technological challenges in hydrogen production and delivery, fuel cells, and hydrogen storage. However, the principal obstacle to the implementation of a safe, low-pressure hydrogen fueling system for fuel-cell powered vehicles remains storage under conditions of near-ambient temperature and moderate pressure. The choices for viable hydrogen storage systems at the present time are limited to compressed gas storage tanks, cryogenic liquid hydrogen storage tanks, chemical hydrogen storage, and hydrogen absorbed or adsorbed in a solid-state material (a.k.a. solid-state storage). Solid-state hydrogen storage may offer overriding benefits in terms of storage capacity, kinetics and, most importantly, safety. The fervor among the research community to develop novel storage materials had, in many instances, the unfortunate consequence of making erroneous, if not wild, claims on the reported storage capacities achievable in such materials, to the extent that the potential viability of emerging materials was difficult to assess. This problem led to a widespread need to establish a capability to accurately and independently assess the storage behavior of a wide array of different classes of solid-state storage materials, employing qualified methods, thus allowing development efforts to focus on those materials that showed the most promise. However, standard guidelines, dedicated facilities, or certification programs specifically aimed at testing and assessing the performance, safety, and life cycle of these emergent materials had not been established. To address the stated need, the Testing Laboratory for Solid-State Hydrogen Storage Technologies was commissioned as a national-level focal point for evaluating new materials emerging from the designated Materials Centers of Excellence (MCoE) according to established and qualified standards. Working with industry, academia, and the U.S. government, SwRI set out to develop an accepted set of evaluation standards and analytical methodologies. Critical measurements of hydrogen sorption properties in the Laboratory have been based on three analytical capabilities: 1) a high-pressure Sievert-type volumetric analyzer, modified to improve low-temperature isothermal analyses of physisorption materials and permit in situ mass spectroscopic analysis of the sample’s gas space; 2) a static, high-pressure thermogravimetric analyzer employing an advanced magnetic suspension electro-balance, glove-box containment, and capillary interface for in situ mass spectroscopic analysis of the sample’s gas space; and 3) a Laser-induced Thermal Desorption Mass Spectrometer (LTDMS) system for high thermal-resolution desorption and mechanistic analyses. The Laboratory has played an important role in down-selecting materials and systems that have emerged from the MCoEs.

08 HYDROGEN↗