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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid Porous Crystalline Materials from Metal Organic Frameworks and Covalent Organic Frameworks

Abstract Two frontier crystalline porous framework materials, namely, metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs), have been widely explored owing to their outstanding physicochemical properties. While each type of framework has its own intrinsic advantages and shortcomings for specific applications, combining the complementary properties of the two materials allows the engineering of new classes of hybrid porous crystalline materials with properties superior to the individual components. Since the first report of MOF/COF hybrid in 2016, it has rapidly evolved as a novel platform for diverse applications. The state‐of‐art advances in the various synthetic approaches of MOF/COF hybrids are hereby summarized, together with their applications in different areas. Perspectives on the main challenges and future opportunities are also offered in order to inspire a multidisciplinary effort toward the further development of chemically diverse, multi‐functional hybrid porous crystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Conductive Metallophthalocyanine Framework Films with High Carrier Mobility as Efficient Chemiresistors

Abstract The poor electrical conductivity of two‐dimensional (2D) crystalline frameworks greatly limits their utilization in optoelectronics and sensor technology. Herein, we describe a conductive metallophthalocyanine‐based NiPc‐CoTAA framework with cobalt(II) tetraaza[14]annulene linkages. The high conjugation across the whole network combined with densely stacked metallophthalocyanine units endows this material with high electrical conductivity, which can be greatly enhanced by doping with iodine. The NiPc‐CoTAA framework was also fabricated as thin films with different thicknesses from 100 to 1000 nm by the steam‐assisted conversion method. These films enabled the detection of low‐concentration gases and exhibited remarkable sensitivity and stability. This study indicates the enormous potential of metallophthalocyanine‐based conductive frameworks in advanced stand‐off chemical sensors and provides a general strategy through tailor‐make molecular design to develop sensitive and stable chemical sensors for the detection of low‐concentration gases.

Yue, Yan↗
DOE OSTI2019

Parallel beamlet dose calculation via beamlet contexts in a distributed multi‐GPU framework

Purpose Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model‐based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units ( GPU s), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under‐utilizing the potential of modern GPU s. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi‐ GPU computational nodes. Methods The proposed context‐based transformation separates beamlet‐local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed‐cone convolution superposition procedure, producing the set of beamlet‐specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager‐worker architecture is constructed around the context‐based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPU s. Phantom experiments were executed to verify the accuracy of the context‐based approach compared to Monte Carlo and a reference CPU ‐ CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4 π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet‐sequential GPU ‐ CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPU s. Results The dosimetric accuracy of the context‐based method displays <1.35% and 2.35% average error from the existing serialized CPU ‐ CCCS algorithm and Monte Carlo simulation for beamlet‐specific PDD s in water and slab phantoms, respectively. The context‐based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet‐sequential GPU ‐ CCCS method in the tested configurations. The context‐based framework demonstrates near linear scaling in the number of distributed compute nodes and GPU s employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization. Conclusions The context‐based approach demonstrates a new expectation of performance for beamlet‐based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large‐scale treatment planning problems ‐ such as automatic 4 π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets ‐ in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large‐scale treatment planning tasks and clinical workflows.

Neph, Ryan↗
DOE OSTI2019

An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. Furthermore, the numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.

17 WIND ENERGY↗
DOE OSTI2018

A Modeling Framework for Evaluating the Drought Resilience of a Surface Water Supply System under Non-stationarity

The future resilience of water supply systems is unprecedentedly challenged by non-stationary processes, such as fast population growth and a changing climate. A thorough understanding of how these non-stationarities impact water supply resilience is vital to support sustainable decision making, particularly for large cities in arid and/or semi-arid regions. In this study, a novel modeling framework, which integrates hydrological processes and water management, was established over a representative water limited metropolitan area to evaluate the impacts of water availability and water demand on reservoir storage and water supply reliability. In this framework, climate change induced drought events were selected from statistically downscaled Coupled Model Intercomparison Project Phase 5 outputs under the Representative Concentration Pathway 8.5 scenario, while future water demand was estimated by the product of projected future population and per capita water use. Compared with the first half of the 21st century (2000–2049), reservoir storage and water supply reliability during the second half century (2050–2099) are projected to reduce by 16.1% and 14.2%, respectively. While both future multi-year droughts and population growth will lower water supply resilience, the uncertainty associated with future climate projection is larger than that associated with urbanization. To reduce the drought risks, a combination of mitigation strategies (e.g., additional conservation, integrating new water sources, and water use redistribution) was found to be the most efficient approach and can significantly improve water supply reliability by as much as 15.9%.

Zhao, Gang (ORCID:0000000327370530)↗
DOE OSTI2015

Ultra-flexible framework breathing in response to dehydration in liskeardite, [(Al,Fe){sub 16}(AsO{sub 4}){sub 9}(OH){sub 21}(H{sub 2}O){sub 11}]·26H{sub 2}O, a natural open-framework compound

Dehydration of the natural open-framework compound, liskeardite, [(Al,Fe){sub 16}(AsO{sub 4}){sub 9}(OH){sub 21}(H{sub 2}O){sub 11}]·26H{sub 2}O, is accompanied by a change in the sign of the thermal expansion from positive to negative above room temperature, and at ~100 °C the structure undergoes a dramatic 2D contraction by co-operative rotation of heteropolyhedral columns that constitute the framework walls. Monoclinic liskeardite, I112 with a≈b≈24.7 Å, c ≈7.8 Å and β≈90° is transformed to a tetragonal phase, I-4 with a≈20.6 Å, c ≈7.7 Å. The associated 30% decrease in volume is unprecedented in inorganic microporous compounds. The flexibility of the contraction is related to the double-hinged nature of the column rotations about [001]. Octahedra in adjacent columns are interconnected by corner-sharing with the two pairs of anions forming opposing edges of AsO{sub 4} tetrahedra, so a double-hinged rotation mechanism operates. Thermal analysis and mass spectroscopic results for liskeardite show that the phase transition at ~100 °C is related to removal of the channel water. The tetragonal phase shows exceptionally large NTE behaviour. Over the temperature range 148–178 the NTE along a and b is close to linear with a magnitude of the order of −900×10{sup −6} °C{sup −1}. The contraction along the channel direction is smaller but still appreciable at −200×10{sup −6} °C{sup −1}. - Graphical abstract: Structure of the collapsed liskeardite framework, formed on dehydration above 100 °C. - Highlights: • The thermal expansion of the mineral liskeardite changes + to − above ambient. • Dehydration at 100 °C results in a record reversible 30% volume reduction. • In situ synchrotron XRD has led to a structural model for the dehydrated phase. • Framework breathing flexibility is attributed to a double-hinge rotation mechanism. • The dehydrated phase shows unprecedented -ve expansion for inorganic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

NMR relaxation and exchange in metal-organic frameworks for surface area screening

We describe a robust screening technique that correlates the surface area of metal organic frameworks to the proton T-2 relaxation behavior of imbibed solvent at low field (13 MHz). In frameworks with small pore sizes (<1 nm) or strong solvent-framework interactions, diffusional exchange between the pore-confined and inter-particle solvent populations remains slow compared to the T-2 of the pore-confined solvent, allowing for a direct porosity analysis of the T-2 spectrum obtained from Laplace inversions. Increases in framework pore-size (>1 nm) lead to corresponding increases in the rate of solvent exchange, as confirmed by T-2 relaxation exchange (REXSY) experiments; increases in the pore size also increases the T-2 of the pore-confined solvent. The combination of these two effects results in comparable rates of relaxation and exchange, which precludes the direct analysis of Laplace inversions. Thus, two- and three-site kinetics models were applied to extract porosity from relaxation decays, thereby improving the utility of the porosity screening tool. (C) 2014 Elsevier Inc. All rights reserved.

Chen, JJ↗
DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Enhancement of Proton Conductivity in Nonporous Metal–Organic Frameworks: The Role of Framework Proton Density and Humidity

Owing to their inherent pore structure, porous metal–organic frameworks (MOFs) can undergo postsynthetic modification, such as loading extra-framework proton carriers. However, strategies for improving the proton conductivity for nonporous MOFs are largely lacking, although increasing numbers of nonporous MOFs exhibit promising proton conductivities. Often, high humidity is required for nonporous MOFs to achieve high conductivities, but to date no clear mechanisms have been experimentally identified. Here we describe the new materials MFM-550(M), [M(HL 1 )], (H 4 L 1 = biphenyl-4,4'-diphosphonic acid; M = La, Ce, Nd, Sm, Gd, Ho), MFM-550(Ba), [Ba(H 2 L 1 )], and MFM-555(M), [M(HL 2 )], (H 4 L 2 = benzene-1,4-diphosphonic acid; M = La, Ce, Nd, Sm, Gd, Ho), and report enhanced proton conductivities in these nonporous materials by (i) replacing the metal ion to one with a lower oxidation state, (ii) reducing the length of the organic ligand, and (iii) introducing additional acidic protons on the MOF surface. Increased framework proton density in these materials can lead to an enhancement in proton conductivity of up to 4 orders of magnitude. Additionally, we report a comprehensive investigation using in situ 2H NMR and neutron spectroscopy, coupled with molecular dynamic modeling, to elucidate the role of humidity in assembling interconnected networks for proton hopping. This study constructs a relationship between framework proton density and the corresponding proton conductivity in nonporous MOFs, and directly explains the role of both surface protons and external water in assembling the proton conduction pathways.

36 MATERIALS SCIENCE↗
DOE OSTI2016

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↗
DOE OSTI2021

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↗
DOE OSTI2019

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↗
DOE OSTI2016

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↗
DOE OSTI2018

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↗