Near-Perfect CO 2 /CH 4 Selectivity Achieved through Reversible Guest Templating in the Flexible Metal–Organic Framework Co(bdp)
Abstract not provided
Taylor, Mercedes K.↗
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Magnetic ordering through itinerant ferromagnetism in a metal-organic framework
In this report, the materials that combine magnetic order with other desirable physical attributes could find transformative applications in spintronics, quantum sensing, low-density magnets and gas separations. Among potential multifunctional magnetic materials, metal-organic frameworks, in particular, bear structures that offer intrinsic porosity, vast chemical and structural programmability, and the tunability of electronic properties. Nevertheless, magnetic order within metal-organic frameworks has generally been limited to low temperatures, owing largely to challenges in creating a strong magnetic exchange. Here we employ the phenomenon of itinerant ferromagnetism to realize magnetic ordering at T C = 225 K in a mixed-valence chromium(II/III) triazolate compound, which represents the highest ferromagnetic ordering temperature yet observed in a metal-organic framework. The itinerant ferromagnetism proceeds through a double-exchange mechanism, which results in a barrierless charge transport below the Curie temperature and a large negative magnetoresistance of 23% at 5 K. These observations suggest applications for double-exchange-based coordination solids in the emergent fields of magnetoelectrics and spintronics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Nanoconfinement of Molecular Magnesium Borohydride Captured in a Bipyridine-Functionalized Metal-Organic Framework
The lower limit of metal hydride nanoconfinement is demonstrated through the coordination of a molecular hydride species to binding sites inside the pores of a metal-organic framework (MOF). Magnesium borohydride, which has a high hydrogen capacity, is incorporated into the pores of UiO-67bpy (Zr6O4(OH)4(bpydc)6 with bpydc2- = 2,2'-bipyridine-5,5'- dicarboxylate) by solvent impregnation. The MOF retained its long-range order, and transmission electron microscopy and elemental mapping confirmed the retention of the crystal morphology and revealed a homogeneous distribution of the hydride within the MOF host. Notably, the B-, N-, and Mg-edge XAS data confirm the coordination of Mg(II) to the N atoms of the chelating bipyridine groups. In situ 11B MAS NMR studies helped elucidate the reaction mechanism and revealed that complete hydrogen release from Mg(BH4)2 occurs as low as 200 °C. Sieverts and thermogravimetric measurements indicate an increase in the rate of hydrogen release, with the onset of hydrogen desorption as low as 120 °C, which is approximately 150 °C lower than that of the bulk material. Furthermore, density functional theory calculations support the improved dehydrogenation properties and confirm the drastically lower activation energy for B-H bond dissociation.
Nanoconfinement, Metal Hydrides, Metal-Organic Fra↗
An Electron/Ion Dual‐Conductive Alloy Framework for High‐Rate and High‐Capacity Solid‐State Lithium‐Metal Batteries
Abstract The solid‐state Li battery is a promising energy‐storage system that is both safe and features a high energy density. A main obstacle to its application is the poor interface contact between the solid electrodes and the ceramic electrolyte. Surface treatment methods have been proposed to improve the interface of the ceramic electrolytes, but they are generally limited to low‐capacity or short‐term cycling. Herein, an electron/ion dual‐conductive solid framework is proposed by partially dealloying the Li–Mg alloy anode on a garnet‐type solid‐state electrolyte. The Li–Mg alloy framework serves as a solid electron/ion dual‐conductive Li host during cell cycling, in which the Li metal can cycle as a Li‐rich or Li‐deficient alloy anode, free from interface deterioration or volume collapse. Thus, the capacity, current density, and cycle life of the solid Li anode are improved. The cycle capability of this solid anode is demonstrated by cycling for 500 h at 1 mA cm −2 , followed by another 500 h at 2 mA cm −2 without short‐circuiting, realizing a record high cumulative capacity of 750 mA h cm −2 for garnet‐type all‐solid‐state Li batteries. This alloy framework with electron/ion dual‐conductive pathways creates the possibility to realize high‐energy solid‐state Li batteries with extended lifespans.
Yang, Chunpeng↗
Thorium Oxo‐Clusters as Building Blocks for Open Frameworks
Abstract Fundamental understanding of tetravalent actinide chemistry is essential to optimize many steps of the nuclear fuel cycle, as well as to predict actinide speciation in the environment. Herein, we report synthesis and structure of three open inorganic frameworks composed of Th 4+ ‐oligomers and sulfate anions. The compounds [Th 10 O 4 (OH) 8 (SO 4 ) 12 (H 2 O) 18 ] ⋅ 28 H 2 O ( 1 ), [Th 9 O 4 (OH) 5 (SO 4 ) 12 (H 2 O) 18 ] ⋅ 1 TMA ⋅ 18 H 2 O ( 2 ), and [Th 8.5 O 4 (OH) 4 (SO 4 ) 12 (H 2 O) 18 ] ⋅ 2 TMA ⋅ n H 2 O ( 3 ) were obtained by slow evaporation of aqueous Th 4+ ‐SO 4 2− solutions, with variable SO 4 2− :Th 4+ ratios (SO 4 2− :Th 4+ =1:1, 1.5:1 and 2:1 for 1 , 2 , and 3 ; TMA=tetramethylammonium). The structure of 1 features a neutral open framework architecture with two interpenetrating networks, built from Th 6 O 4 (OH) 4 12+ hexamers and Th 2 (OH) 2 6+ dimers, linked by sulfate anions. The complex anionic framework of 2 is built from hexamers, dimers, and monomers, with charge‐balancing TMA cations. Compound 3 is very similar to 2 , but it is constructed only from hexamers and monomers. Small angle X‐ray scattering (SAXS) of the reaction solutions reveals pre‐assembled hexamer building units in the presence of sulfate, and underlines the key role of the sulfate‐oxoanion ligand on thorium polymerization processes.
Falaise, Clément↗
Necrobiome framework for bridging decomposition ecology of autotrophically and heterotrophically derived organic matter
Not provided.
Environmental Sciences & Ecology↗
A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems
A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.
Bayesian network↗
BASIN-3D: A brokering framework to integrate diverse environmental data
Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.
Varadharajan, C↗
A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness
The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these diculties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such diculties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The eectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10)) where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.
Lei, Huan↗
A learning-based time-efficient framework for building energy performance evaluation
Not Available
Bhattacharya, Saptarshi↗
Hydrolysis of zeolite framework aluminum and its impact on acid catalyzed alkane reactions
Not Available
Xue, Nianhua↗
A unified conceptual framework for prediction and control of microbiomes
Not Available
Stegen, James C.↗
A Framework for Quantifying Energy and Productivity Benefits of Smart Manufacturing Technologies
The transformative possibilities of smart manufacturing, which is defined by digitization, enhanced connectivity, advanced analytics, and integrated cyber-physical systems in manufacturing processes and systems, have been extensively discussed in the literature. Potential benefits underscored include cost reduction, production flexibility, shorter product times-to-market, energy efficiency, environmental impact reduction, and increased productivity. Scant attention has been given to formal methods for quantifying and analyzing energy productivity to aid a broad range of manufacturing industries in evaluating the merits of adoption. Here we present a strategic analysis framework to estimate cost-effective improvements in energy efficiency and productivity that may be realized through smart manufacturing. The framework uses the cost of conserving energy (CCE) as a complementary measure to determine the feasibility of a set of smart manufacturing interventions in the context of a specific factory, firm, or entire industry. Guided by a function-driven approach based on key performance indicators, the CCE measure accounts for system-wide changes in direct and indirect costs and total energy use relative to the same manufacturing system without smart manufacturing interventions. These changes include cost of cyber-physical systems, which include components such as sensors, controllers, smart equipment, and information and communication technology (ICT) equipment. Additionally, cost and savings are accounted from changes in primary and intermediate inputs, production time, off-spec production, and waste. Energy calculations include upstream energy use of the cyber-physical systems. For complex processes with significant circularity and/or feedbacks, we propose the use of input-output models to characterize elements of the CCE. The framework is discussed in the context of recent efforts in craft breweries to use smart manufacturing for reducing energy intensity and increasing product yield.
Supekar, Sarang D.↗