Synthesis and O 2 Reactivity of a Titanium(III) Metal-Organic Framework
Abstract not provided
Mason, Jarad A.↗
SEARCH · Engineering Papers
Results for “Frameworks”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Zirconium-Based Metal–Organic Framework for Removal of Perrhenate from Water
Efficient removal of pertechnetate (TcO4 -) anions from liquid waste or melter off-gas solution for alternative treatment is one of the promising options to manage 99Tc in legacy nuclear waste. Safe immobilization of 99Tc is of major importance due to its long half-life (t1/2= 2.13 × 105 yrs) and environmental mobility. Different types of inorganic and solid state ion-exchange materials such as layered double hydroxides have been shown to absorb TcO4 - anions from water. However, both high capacity and selectivity have yet to be achieved in a single material. Herein, we show that a protonated version of an ultra-stable zirconium based metalorganic framework can adsorb perrhenate (ReO4 -) anions, a non-radioactive surrogate for TcO4 -, from water even in the presence of other common anions. Synchrotron based powder X-ray diffraction and molecular simulations were used to identify the position of the adsorbed ReO4 - (surrogate for TcO4 -) molecule within the framework.
Banerjee, Debasis↗
UiO-66 Metal–Organic Framework as an Anode for a Potassium-Ion Battery: Quantum Mechanical Analysis
The natural abundance of potassium in the earth’s crust is 1000 times higher than that of lithium, so energy technologies built on potassium are more sustainable. Potassium-ion batteries have attracted considerable attention because of their relatively low cost and high operating potential, but questions remain about the best anode material for such batteries. Here, in this paper, we report first-principles computations based on density functional theory to investigate the performance of the UiO-66 metal–organic framework as an anode material for potassium-ion batteries; the goal is to provide a fundamental understanding of metal–organic framework (MOF)-based electrodes to guide the design and development of high-performance potassium-ion batteries. Our study includes the stability and electronic properties of potassiated structures and the mechanisms of potassium intercalation and diffusion in the framework. The results indicate that UiO-66 has a maximum specific capacity of 644 mAh/g as the anode of a potassium-ion battery. During potassiation, we observe charge transfer from potassium to carbon or oxygen of UiO-66 near the intercalated K. During K diffusion, the K migrates along the UiO-66 framework with a maximal migration energy barrier of 0.377 eV in the optimal pathway, which is much lower than the barriers for Li and Na diffusion in UiO-66. The diffusion coefficient of K in the anode is several orders of magnitude larger than those of Li and Na. This favors potassium ions over lithium ions or sodium ions when UiO-66 is the anode.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enantioselective Recognition of Ammonium Carbamates in a Chiral Metal–Organic Framework
Abstract not provided
Martell, Jeffrey D.↗
A Framework to Delineate Precipitation-Runoff Regimes: Precipitation vs. Snowpack in the Western U.S.
We propose a framework to classify precipitation events accompanied by snow process (precipitation-and-snow, or PAS events) into five regimes, based on the relative contributions of snowpack change and precipitation amount to the runoff response. This framework is applied to a regional climate simulation over the western U.S. to reveal the regions where snow process plays important roles. Over the western U.S., PAS events account for 50-90% of all the precipitation events. About 60-80% of PAS events feature snow accumulation, but snow melting frequently happens in high-elevation areas. Compared with snow accumulation events, snow melting events have a more limited spatial distribution. Different regimes peak in different months between early winter and late spring. Lastly, the impact of atmospheric rivers is investigated. Although accounting for only 2% of the grid-scale precipitation events, atmospheric rivers trigger significant snowmelt and contribute to 20% and 11% of light and heavy snow melting events, respectively.
Chen, Xiaodong↗
Adsorption of two gas molecules at a single metal site in a metal–organic framework
One strategy to markedly increase the gas storage capacity of metal–organic frameworks is to introduce coordinatively-unsaturated metal centers capable of binding multiple gas molecules. Herein, we provide an initial demonstration that a single metal site within a framework can support the terminal coordination of two gas molecules—specifically hydrogen, methane, or carbon dioxide.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An inexact regularized Newton framework with a worst-case iteration complexity of $ {\mathscr O}(\varepsilon^{-3/2}) $ for nonconvex optimization
Abstract An algorithm for solving smooth nonconvex optimization problems is proposed that, in the worst-case, takes ${\mathscr O}(\varepsilon ^{-3/2})$ iterations to drive the norm of the gradient of the objective function below a prescribed positive real number $\varepsilon $ and can take ${\mathscr O}(\varepsilon ^{-3})$ iterations to drive the leftmost eigenvalue of the Hessian of the objective above $-\varepsilon $. The proposed algorithm is a general framework that covers a wide range of techniques including quadratically and cubically regularized Newton methods, such as the Adaptive Regularization using Cubics (arc) method and the recently proposed Trust-Region Algorithm with Contractions and Expansions (trace). The generality of our method is achieved through the introduction of generic conditions that each trial step is required to satisfy, which in particular allows for inexact regularized Newton steps to be used. These conditions center around a new subproblem that can be approximately solved to obtain trial steps that satisfy the conditions. A new instance of the framework, distinct from arc and trace, is described that may be viewed as a hybrid between quadratically and cubically regularized Newton methods. Numerical results demonstrate that our hybrid algorithm outperforms a cubically regularized Newton method.
Mathematics↗
DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology
Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.
Plant Sciences↗
A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log
Reliability, availability and serviceability (RAS) logs of high performance computing (HPC) resources, when closely investigated in spatial and temporal dimensions, can provide invaluable information regarding system status, performance, and resource utilization. These data are often generated from multiple logging systems and sensors that cover many components of the system. The analysis of these data for finding persistent temporal and spatial insights faces two main difficulties: the volume of RAS logs makes manual inspection difficult and the unstructured nature and unique properties of log data produced by each subsystem adds another dimension of difficulty in identifying implicit correlation among recorded events. To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.
Park, Byung↗
A Unified Hardware/Software Co-Design Framework for Neuromorphic Computing Devices and Applications
Explore the source record for details and available documents.
Plank, James↗
Minimizing the Electromechanical Stresses in Poloidal Field Coils by Optimizing their Numbers and Locations using FREDA Framework
Poloidal field (PF) and central solenoid (CS) coils play a crucial role in sustaining the equilibrium and preserving the shape of highly confined tokamak plasmas. Ensuring that PF coil current and mechanical stress stay within superconducting and structural limitations is an important check in the design assessment. Minimizing the PF coil currents and mechanical stresses influences reliability, cost, and performance. A free-boundary MHD equilibrium code—FreeGS is employed within the fusion reactor design and assessment (FREDA) whole facility modeling (WFM) framework to construct the plasma equilibrium based on the configuration and currents in the PF coils. Here, we present the capability of the FreeGS code to minimize the currents, forces, and electromagnetic stresses on the PF coils by optimizing their number, sizes, structures, and locations while maintaining an MHD stable plasma configuration with a large confinement factor. The workflow is initialized with a configuration of plasma parameters and coils’ locations from the 0-D tokamak build systems code in the FREDA framework. Then, FreeGS is called to calculate the initial equilibrium at the minimum total current in PF coils. Thereafter, FreeGS’s internal optimizer minimizes the currents and hoop and central forces on the PF coils while maintaining the reference equilibrium. Finally, the input configuration is updated with the optimized parameters for equilibria over the ramp-up phase of a burning-plasma operation. FREDA’s whole facility optimization capability, which includes all magnetic field coil systems, blanket, vacuum vessel (VV), first wall, divertor, etc., is under development and out of the scope for this study.
Hassan, Ehab [ORNL] (ORCID:0000000181060301)↗
A framework to measure myocardial extracellular volume fraction using dual-phase low dose CT images
Purpose: Myocardial extracellular volume fraction (ECVF) is a surrogate imaging biomarker of diffuse myocardial fibrosis, a hallmark of pathologic ventricular remodeling. Low dose cardiac CT is emerging as a promising modality to detect diffuse interstitial myocardial fibrosis due to its fast acquisition and low radiation; however, the insufficient contrast in the low dose CT images poses great challenge to measure ECVF from the image. Methods: To deal with this difficulty, the authors present a complete ECVF measurement framework including a point-guided myocardial modeling, a deformable model-based myocardium segmentation, nonrigid registration of pre- and post-CT, and ECVF calculation. Results: The proposed method was evaluated on 20 patients by two observers. Compared to the manually delineated reference segmentations, the accuracy of our segmentation in terms of true positive volume fraction (TPVF), false positive volume fraction (FPVF), and average surface distance (ASD), were 92.18% ± 3.52%, 0.31% ± 0.10%, 0.69 ± 0.14 mm, respectively. The interobserver variability measured by concordance correlation coefficient regarding TPVF, FPVF, and ASD were 0.95, 0.90, 0.94, respectively, demonstrating excellent agreement. Bland-Altman method showed 95% limits of agreement between ECVF at CT and ECVF at MR. Conclusions: The proposed framework demonstrates its efficiency, accuracy, and noninvasiveness in ECVF measurement and dramatically advances the ECVF at cardiac CT toward its clinical use.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework
In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.
99 GENERAL AND MISCELLANEOUS↗
Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond
Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.
big data↗
Methods for associating or dissociating guest materials with a metal organic framework, systems for associating or dissociating guest materials within a series of metal organic frameworks, thermal energy transfer assemblies, and methods for transferring thermal energy
Methods for releasing associated guest materials from a metal organic framework are provided. Methods for associating guest materials with a metal organic framework are also provided. Methods are provided for selectively associating or dissociating guest materials with a metal organic framework. Systems for associating or dissociating guest materials within a series of metal organic frameworks are provided. Thermal energy transfer assemblies are provided. Methods for transferring thermal energy are also provided.
42 ENGINEERING↗
Methods for associating or dissociating guest materials with a metal organic framework, systems for associating or dissociating guest materials within a series of metal organic frameworks, thermal energy transfer assemblies, and methods for transferring thermal energy
Methods for releasing associated guest materials from a metal organic framework are provided. Methods for associating guest materials with a metal organic framework are also provided. Methods are provided for selectively associating or dissociating guest materials with a metal organic framework. Systems for associating or dissociating guest materials within a series of metal organic frameworks are provided. Thermal energy transfer assemblies are provided. Methods for transferring thermal energy are also provided.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Highly C2/C1-Selective Covalent Organic Frameworks Substituted with Azo Groups
A series of covalent organic frameworks substituted with azo groups (AzoCOFs) have been synthesized via imine condensation. The obtained frameworks show crystallinity and high stability. More importantly, the AzoCOFs exhibit exceptionally high ideal adsorption solution theory (IAST) selectivity in adsorption of C 2 H 2 (35–2891) over CH 4 at 273 K and 1 bar, owing to the favorable interactions between azo groups and acetylene molecules. The dependence of the gas adsorption property on pore size and polarity of the frameworks was also studied. Here, the triethylene glycol substituted Tg-AzoCOF shows the highest C 2 H 2 /CH 4 selectivity (IAST selectivity of 2891), which represents the highest reported for all porous materials. The AzoCOFs also exhibit high IAST adsorption selectivity of C 2 H 4 /CH 4 (11–20), C 2 H 6 /CH 4 (15–22), and CO 2 /CH 4 (12–37), which is comparable with most porous materials, thus showing their great potential in gas separation applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Covalent-Organic Frameworks Composed of Rhenium Bipyridine and Metal Porphyrins: Designing Heterobimetallic Frameworks with Two Distinct Metal Sites
The incorporation of homogeneous catalysts for CO 2 reduction into extended frameworks has been a successful strategy for increasing catalyst lifetime and activity, but the effects of the linkers on catalysis are underexplored. In this work, a novel rhenium bipyridine complex was synthesized for the purpose of designing a covalent-organic framework (COF) with both metalloporphyrin and metal bipyridine moieties. Investigation of the rhenium complex as a homogeneous catalyst shows a faradaic efficiency of 81(8)% for the electrocatalytic conversion of CO 2 to CO upon the addition of methanol as the proton source. Treatment of the rhenium complex with tetra(4-aminophenyl)porphyrin under Schiff base conditions produces the desired COF, as indicated by powder X-ray diffraction (PXRD) studies. Metalation of the porphyrins was accomplished through postsynthetic modification with CoCl 2 and FeCl 3 metal precursors. The retention of the PXRD peaks and appearance of new Co and Fe peaks in the corresponding X-ray photoelectron spectroscopy spectra suggest the successful incorporation of a secondary metal site into the framework. Cyclic voltammetry measurements display increases in current densities when the atmosphere is changed from N 2 to CO 2 . Finally, controlled potential electrolyses show that the cobalt-postmetalated COF has the highest activity toward CO 2 reduction, reaching a faradaic efficiency of 18(2)%.
36 MATERIALS SCIENCE↗