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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.
Necrobiome framework for bridging decomposition ecology of autotrophically and heterotrophically derived organic matter
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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.
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.
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.
A learning-based time-efficient framework for building energy performance evaluation
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Hydrolysis of zeolite framework aluminum and its impact on acid catalyzed alkane reactions
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A unified conceptual framework for prediction and control of microbiomes
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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.
A relative value framework: Why do different electricity system technologies have different economic value?
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Tuning the Formations of Metal-Organic Frameworks by Modification of Ratio of Reactant, Acidity of Reaction System, and Use of a Secondary Ligand
Four porous coordination networks (PCNs), {[Zn3O(H2O)(3)(adc)(3)]center dot 2(C2H6NH2)center dot 2(DMF)center dot 3(H2O)}(n) (PCN-131), Zn-2(DMA)(2)(adc)(2)]center dot 2(DMA)}(n) (PCN-132), {[Zn3O(DMF)(adc)(3)(4,4'-bpy)]center dot 2(C2H6NH2)center dot S}(n) (PCN-131'), and {[Zn(adc)(4,4'-bpy)(0.5)]center dot S}(n) (PCN-132'), have been synthesized by the assembly of anthrancene-9,10-dicarboxylic acid (H(2)adc) with Zn(II) under different reaction conditions, including modifications of reactant ratio, acidity variations, and the use of a secondary ligand. Single-crystal X-ray diffraction studies reveal that PCN-131, obtained from the dimethylformamide (DMF) solution under acid condition, has a three-dimentional (3D) framework structure with one-dimensional (1D) honeycomb channels. PCN-132 isolated from dimethylacetamide (DMA) solution without adding acid in synthesis is a two-dimensional (2D) layer compound. By employing 4,4'-bipyridyl (4,4'-bpy) as a secondary ligand, PCN-131' and PCN-132' were synchronously synthesized as a mixture outcome with more PCN-131' than PCN-132'. In PCN-131', 4,4'-bpy acting as a secondary ligand is arranged inside the honeycomb channel of the 3D PCN-131, resulting in an effective improvement of thermal stability of the network, while in PCN-132', 4,4'-bpy ligands link 2D layers of PCN-132 to form a pillared-layer 3D framework Gas adsorption has been performed for selected materials. The results show that the framework of PCN-131 is thermally unstable after removing the solvent molecules coordinated to their metal sites. While PCN-131' is stable for gas uptake, with an evaluated Langmuir surface area of 199.04 m(2) g(-1), it shows a selective adsorption of CO2 over CH4.
Collaborative Triple Framework Interpenetration and Immobilization of Open Metal Sites within a Microporous Mixed Metal-Organic Framework for Highly Selective Gas Adsorption
A three-dimensional triply interpenetrated mixed metal-organic framework, Zn{sub 2}(BBA){sub 2}(CuPyen) {center_dot} G{sub x} (M'MOF-20; BBA = biphenyl-4,4'-dicarboxylate; G = guest solvent molecules), of primitive cubic net was obtained through the solvothermal reaction of Zn(NO{sub 3}){sub 2}, biphenyl-4,4'-dicarboxylic acid, and the salen precursor Cu(PyenH{sub 2})(NO{sub 3}){sub 2} by a metallo-ligand approach. The triple framework interpenetration has stabilized the framework in which the activated M'MOF-20a displays type-I N{sub 2} gas sorption behavior with a Langmuir surface area of 62 m{sup 2} g{sup -1}. The narrow pores of about 3.9 {angstrom} and the open metal sites on the pore surfaces within M'MOF-20a collaboratively induce its highly selective C{sub 2}H{sub 2}/CH{sub 4} and CO{sub 2}/CH{sub 4} gas separation at ambient temperature.
Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework
Abstract not provided
A Titanium–Organic Framework as an Exemplar of Combining the Chemistry of Metal– and Covalent–Organic Frameworks
A crystalline material with a two-dimensional structure, termed metal–organic framework-901 (MOF-901), was prepared using a strategy that combines the chemistry of MOFs and covalent–organic frameworks (COFs). This strategy involves in situ generation of an amine-functionalized titanium oxo cluster, Ti6O6(OCH3)6(AB)6 (AB = 4-aminobenzoate), which was linked with benzene-1,4-dialdehyde using imine condensation reactions, typical of COFs. The crystal structure of MOF-901 is composed of hexagonal porous layers that are likely stacked in staggered conformation (hxl topology). This MOF represents the first example of combining metal cluster chemistry with dynamic organic covalent bond formation to give a new crystalline, extended framework of titanium metal, which is rarely used in MOFs. The incorporation of Ti(IV) units made MOF-901 useful in the photocatalyzed polymerization of methyl methacrylate (MMA). The resulting polyMMA product was obtained with a high-number-average molar mass (26 850 g mol–1) and low polydispersity index (1.6), which in many respects are better than those achieved by the commercially available photocatalyst (P-25 TiO2). Additionally, the catalyst can be isolated, reused, and recycled with no loss in performance.