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Experimental Evidence of Negative Linear Compressibility in the MIL-53 Metal-Organic Framework Family
Here, we report a series of powder X-ray diffraction experiments performed on the soft porous crystals MIL-53(Al) and NH 2 -MIL-53(Al) in a diamond anvil cell under different pressurization media. Systematic refinements of the obtained powder patterns demonstrate that these materials expand along a specific direction while undergoing total volume reduction under an increasing hydrostatic pressure. Our results confirm for the first time the negative linear compressibility behaviour of this family of materials, recently predicted from quantum chemical calculations.
Pore-controlled formation of 0D metal complexes in anionic 3D metal–organic frameworks
The host–guest chemistry between a series of anionic MOFs and their trapped counterions was investigated by single crystal XRD.
A single crystalline porphyrinic titanium metal–organic framework
We have successfully synthesized a single crystalline porphyrinic titanium MOF, namely PCN-22. PCN-22 represents an important step towards mimicking dye sensitized TiO 2 in MOFs.
Functionalized Porous Aromatic Frameworks as High‐Performance Adsorbents for the Rapid Removal of Boric Acid from Water
Abstract This study demonstrates that functionalized, highly porous polymers are promising for the adsorptive capture of boric acid, a neutral contaminant that is difficult to remove from seawater using conventional reverse osmosis membranes. Appending N ‐methyl‐d‐glucamine (NMDG) to the pore walls of high‐surface‐area porous aromatic frameworks (PAFs) yields the adsorbents PAF‐1‐NMDG and P2‐NMDG in a simple two‐step synthesis. The boron‐selective PAFs demonstrate adsorption capacities that are up to 70% higher than those of a commercial boron‐selective resin, Amberlite IRA743, and markedly faster adsorption rates, owing to their higher NMDG loadings and greater porosities relative to the resin. Remarkably, PAF‐1‐NMDG is able to reduce the boron concentration in synthetic seawater from 2.91 to <0.5 ppm in less than 3 min at an adsorbent loading of only 0.3 mg mL −1 . The boron adsorption rate constants of both frameworks, determined via a pseudo‐second‐order rate model, represent the highest values reported in the literature—in most cases orders of magnitude higher than those of other boron‐selective adsorbents. The frameworks can also be readily regenerated via mild acid/base treatment and maintain constant boron adsorption capacities for at least 10 regeneration cycles. These results highlight the numerous advantages of PAFs over traditional porous polymers in water treatment applications.
A holistic framework integrating plant-microbe-mineral regulation of soil bioavailable nitrogen
Abstract Soil organic nitrogen (N) is a critical resource for plants and microbes, but the processes that govern its cycle are not well-described. To promote a holistic understanding of soil N dynamics, we need an integrated model that links soil organic matter (SOM) cycling to bioavailable N in both unmanaged and managed landscapes, including agroecosystems. We present a framework that unifies recent conceptual advances in our understanding of three critical steps in bioavailable N cycling: organic N (ON) depolymerization and solubilization; bioavailable N sorption and desorption on mineral surfaces; and microbial ON turnover including assimilation, mineralization, and the recycling of microbial products. Consideration of the balance between these processes provides insight into the sources, sinks, and flux rates of bioavailable N. By accounting for interactions among the biological, physical, and chemical controls over ON and its availability to plants and microbes, our conceptual model unifies complex mechanisms of ON transformation in a concrete conceptual framework that is amenable to experimental testing and translates into ideas for new management practices. This framework will allow researchers and practitioners to use common measurements of particulate organic matter (POM) and mineral-associated organic matter (MAOM) to design strategic organic N-cycle interventions that optimize ecosystem productivity and minimize environmental N loss.
A Flexible and Generic Functional Mock-up Unit Based Threat Injection Framework for Grid-interactive Efficient Buildings: A Case Study in Modelica
Grid-interactive efficient buildings (GEBs) have been considered as an important asset to support the power grid reliability by utilizing the demand flexibility offered by GEBs. GEBs are enabled by advances in sensors and controls, and the communication between building equipment, whole buildings, and the grid. The integration of different building technologies and network-based communication system makes GEBs vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the impact of threats on the system performance. This paper proposes a generic and flexible threat injection framework for commonly-used building energy simulators such as EnergyPlus and Modelica to support threat modeling and evaluation. This framework leverages functional mock-up unit (FMU) to develop a general modeling interface for threat injection and simulation. A numerical case study using Modelica as a building energy simulator is conducted to demonstrate the capability of the framework for supporting single/multiple-order threat modeling and simulation of a GEB. Four threats and their combinations are injected on a Modelica-based threat-free building energy and control system, including operating supply fan at its full speed, remotely cycling the chiller on and off, blocking the chiller from receiving the chilled water supply temperature setpoints, and hijacking the global zone air temperature setpoint. Simulation results show that the cyber-attack that leads to short-term signal blocking has small effects on the system operation due to the "self-healing" feature of the heating, ventilation, and air-conditioning (HVAC) interactive control system. The threat that takes control of resetting the global zone air temperature setpoints has the most adverse impact on the system energy use, peak power demand, thermal comfort and the provision of demand flexibility. The combination of four threats have aggregative effects on the system but the effects are less than the additive effects of the individual threat.
A Highly Efficient Control Framework for Centralized Residential Charging Coordination of Large Electric Vehicle Populations
The potential for widespread adoption of plug-in electric vehicles (PEVs) brings with it the potential for negative impacts on the electric grid from electric vehicle charging. Uncoordinated PEV charging may increase electricity demand during peak hours, which could create concerns for the grid as the number of PEVs increases. Therefore, it is important to coordinate charging to alleviate potential negative impacts. Centralized charging coordination is preferred by grid operator over distributed control strategies because it can systematically allocate energy across a large population of PEVs and achieve global coordination benefits. However, centralized methods presented in the literature to date require prohibitively expensive computational resources and lack realistic PEV charging models. As a result, they cannot achieve the efficiency and accuracy required to implement charging coordination in the real world. This paper introduces a highly efficient receding horizon control framework that enables dynamic charging coordination for large PEV populations. A two-stage hierarchical optimization routine is proposed that aggregates individual PEV charging flexibility to reduce the computational complexity of the optimization process. The control framework is based on high-fidelity, validated charging system models and charging behavior models derived from a real-world data set of residential charging activities collected from thousands of charging stations over multiple years. Case studies illustrate that the proposed charging control framework is capable of effectively coordinating the charging of millions of PEVs using a standard desktop computer.
The influence of pressure on the photoluminescence properties of a terbium-adipate framework
The influence of pressure (over the 0–4.7 GPa range) on the photoluminescence emissions and crystal structure of the known 3D terbium-adipate metal-organic framework material Tb-GWMOF6 has been evaluated by high-pressure single-crystal X-ray diffraction and spectroscopic techniques. The results from this study show that this complex lanthanide framework structure undergoes three phase transitions within the 0–4 GPa pressure range that involve alterations in the number of symmetry independent Tb{sup 3+} ion sites within the crystal lattice. These pressure induced modifications to the structure of Tb-GWMOF6 lead to pronounced changes in the profiles of the {sup 5}D{sub 4}→{sup 7}F{sub 5} emission spectra of this complex. - Graphical abstract: The influence of pressure on the structure and photoluminescence emissions of a 3D terbium-adipate framework. - Highlights: • High-pressure luminescence spectra for a Tb framework were collected. • High-pressure single-crystal XRD experiments were conducted with the Tb Framework. • The framework undergoes two pressure-induced phase transitions. • The three phases of the material show different photoluminescence behaviour.
Buckling of Two-Dimensional Covalent Organic Frameworks under Thermal Stress
Two-dimensional covalent organic frameworks (2D COFs) are periodic, permanently porous, and lightweight solids that are polymerized from topologically designed monomers. The predictable design and structural modularity of these materials make them promising candidates for applications including catalysis, environmental remediation, chemical separations, and organic electronics, many of which will require stability to mechanical and thermal stress. Based on their reinforced structures and high degradation temperatures, as determined by thermal gravimetric analysis (TGA), many reports have claimed that COFs have excellent thermal stability. However, their stability to heat and pressure has not been probed using methods that report on structural changes rather than the loss of volatile compounds. Here, we explore two structurally analogous 2D COFs with different polymerization chemistries using in operando X-ray diffraction (XRD), which demonstrates the loss of crystallinity at lower temperatures than the degradation temperatures measured by TGA. Density functional theory calculations suggest that an asymmetric buckling of the COF lattice is responsible for the observed loss of crystallinity. In addition to their thermal stability, XRD of the 2D COFs under gas pressures up to 100 bar showed no loss in crystallinity or structural changes, indicating that these materials are robust to mechanical stress by applied pressure. We expect that these results will encourage further exploration of COF stability as a function of framework design and isolated form, which will guide the design of frameworks that withstand demanding application-relevant conditions.
Uncovering the Local Magnesium Environment in the Metal–Organic Framework Mg 2 (dobpdc) Using 25 Mg NMR Spectroscopy
The incorporation of N,N'-dimethylethylenediamine into an expanded MOF-74 framework has yielded a material (mmen-Mg2(dobpdc)) exhibiting “step-shaped” CO2 adsorption isotherms. The coordination of mmen at the Mg open metal center is essential for the unique cooperative adsorption mechanism elucidated for this material. Despite its importance for carbon capture, there is as yet no experimental structure determination available for the underlying metal– organic framework Mg2(dobpdc). Our 25Mg solid-state NMR data unravel the local Mg environments in several Mg2(dobpdc) samples, unambiguously confirming the formation of fivecoordinate Mg centers in the activated material and six-coordinate Mg centers in the solvent- or diamine-loaded samples, such as mmen-Mg2(dobpdc). A fraction of the Mg centers are locally disordered due to the framework deformation accompanied by the guest distributions and dynamics.
Charge Delocalization and Bulk Electronic Conductivity in the Mixed-Valence Metal–Organic Framework Fe(1,2,3-triazolate) 2 (BF 4 ) x
Metal–organic frameworks are of interest for use in a variety of electrochemical and electronic applications, although a detailed understanding of their charge transport behavior, which is of critical importance for enhancing electronic conductivities, remains limited. Herein, we report isolation of the mixed-valence framework materials, Fe(tri) 2 (BF 4 ) x (tri – = 1,2,3-triazolate; x = 0.09, 0.22, and 0.33), obtained from the stoichiometric chemical oxidation of the poorly-conductive iron(II) framework Fe(tri) 2 , and find that the conductivity increases dramatically with iron oxidation level. Notably, the most oxidized variant, Fe(tri) 2 (BF 4 ) 0.33 , displays a room-temperature conductivity of 0.3(1) S/cm, which represents an increase of eight orders of magnitude from that of the parent material and is one of the highest conductivity values reported among three-dimensional metal–organic frameworks. Detailed characterization of Fe(tri) 2 and the Fe(tri) 2 (BF 4 ) x materials via powder X-ray diffraction, Mössbauer spectroscopy, and IR and UV-vis-NIR diffuse reflectance spectroscopies reveals that the high conductivity arises from intervalence charge transfer between mixed-valence low-spin Fe II/III centers. Further, Mössbauer spectroscopy indicates the presence of a valence-delocalized Fe II/III species in Fe(tri) 2 (BF 4 ) x at 290 K, one of the first such observations for a metal–organic framework. The electronic structure of valence-pure Fe(tri) 2 and the charge transport mechanism and electronic structure of mixed-valence Fe(tri) 2 (BF 4 ) x frameworks are discussed in detail.
Colloidal Three-Dimensional Covalent Organic Frameworks and Their Application as Porous Liquids
Developing solid porous materials into free-flowing liquids with permanent porosity is a promising strategy for overcoming certain limitations of conventional sorbent materials employed in gas storage and separation applications. The ability to control the pore size and chemical functionalities of organic frameworks gives these particular nanoporous materials distinct advantages over other small cage-like molecules or hollow particles that have been developed into porous liquids. Here, we describe the synthesis of a 3D imine-linked colloidal covalent organic framework (COF)-based porous liquid, designed for efficient size-exclusion of solvent, as well as for long-term stability. By tethering ionic liquids to the colloid surface, the colloids can be dried, purified, and resuspended in a variety of solvents without irreversible aggregation typically observed of COF colloids. Colloid size could be controlled between 50 and 400 nm, with surface areas as high as 800 m2/g. The 3D intertwining morphology of the colloids had pore sizes ranging from 5 to 14 Å, allowing them to efficiently sizeexclude bulky ionic liquids. The COF colloids were stable towards flocculation in an ionic liquid for > 1 year. Permanent porosity was confirmed with a combination of 19F NMR measurements and gas sorption techniques. CO2 and CH4 uptake in these porous liquids increased more than 10 and 20-fold, respectively, over non-porous, neat ionic liquid control samples. The work not only advances the state of COF-based colloid science but also represents a practical advance towards developing more robust, tunable framework-based porous liquid materials for a host of gas storage and separation applications.
Trial Implementation of a Multihazard Risk Assessment Framework for High-Impact Low-Frequency Power Grid Events
The Pacific Northwest National Laboratory developed a risk framework for modeling high-impact, low-frequency power grid events to support risk-informed decisions. In this research article, we briefly recap the framework and demonstrate its implementation for seismic and geomagnetic hazards using a benchmark reliability test system. We describe integration of a collection of models implemented to perform hazard analysis, fragility evaluation, consequence estimation and post-event restoration. We demonstrate the value of the framework as a multi-hazard power grid risk assessment and management tool. The research will benefit transmission planners and emergency planners by improving their ability to maintain a resilient grid infrastructure against impacts from major events.
A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization
This paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and PMU measurements are calculated separately through unscented transformation and a vector autoregression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case of synchronization issues between SCADA and PMU measurements, either forecasted PMU measurements or prior SCADA measurements from the last estimation run are leveraged to restore system observability. After that, robust generalized maximum-likelihood (GM)-estimator is developed to integrate full measurement correlations and handle bad data in SCADA and PMU measurements. Thanks to the robustness and flexibility of the proposed robust estimator, bad data as well as imperfect measurement synchronization are mitigated, yielding high statistical efficiency. Comprehensive comparison results with other alternatives under various conditions demonstrate the benefits of the proposed framework.
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint
We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.
A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification
Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.