Peak Demand Management and Voltage Regulation Using Coordinated Virtual Power Plant Controls
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The crystal structure of L-5-methyltetrahydrofolate calcium trihydrate has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional techniques. Calcium levomefolate trihydrate crystallizes in space group P2 1 2 1 2 1 (#19) with a = 7.1706(6), b = 6.5371(5), c = 53.8357(41) Å, V = 2523.58(26) Å 3 , and Z = 4. The structure is characterized by alternating hydrophobic and hydrophilic layers along the c-axis. The Ca cations are 7-coordinate, and share edges to form chains along the b-axis. Each of the water molecules acts as a donor in two hydrogen bonds. The coordinated water molecule makes two strong intermolecular O–H∙∙∙O hydrogen bonds to carboxyl and carbonyl groups. The two zeolitic water molecules form weaker hydrogen bonds, to carbonyl O atoms, ring N atoms, and aromatic C atoms. Several N–H∙∙∙O/N hydrogen bonds, as well as C–H∙∙∙O hydrogen bonds, also contribute to the lattice energy.
Current methods for metal chelation are generally based on multidentate organic ligands, which are generated through cumbersome multistep synthetic processes that lack flexibility for systematically varying metal-binding motifs. Octadentate ligands incorporating hydroxypyridinone or catecholamide binding moieties onto a spermine scaffold are known to display some of the highest affinities towards f-elements. Enhancing binding affinity for specific lanthanide or actinide ions however, necessitates ligand architectures that allow for modular and high throughput synthesis. Here we introduce a high-throughput combinatorial library of 16 tetrameric N -substituted glycine oligomers (peptoids) containing hydroxypyridinone or catecholamide chelating units linked via an ethylenediamine bridge and, for comparison, we also synthesized the corresponding mixed ligands derived from the spermine scaffold: 3,4,3-LI(1,2-HOPO) 2 (CAM) 2 and 3,4,3-LI(CAM) 2 (1,2-HOPO) 2 . Coordination-based luminescence studies were carried out with Eu 3+ and Tb 3+ to begin probing the properties of the new ligand architecture and revealed higher sensitization efficiency with the spermine scaffold as well as different spectroscopic features among the structural peptoid isomers. Solution thermodynamic properties of selected ligands revealed different coordination properties between the spermine and peptoid analogues with Eu 3+ stability constants log β 110 ranging from 28.88 ± 3.45 to 43.97 ± 0.49. The general synthetic strategy presented here paves the way for precision design of new specific and versatile ligands, with a variety of applications tailored towards the use of f-elements, including separations, optical device optimization, and pharmaceutical development.
The oxygen evolution reaction (OER) requires electrodes that are not only catalytically active, but also stable under harsh electrochemical environments to enable efficient, durable technologies. Our recent report of a stable amorphous Ni 0.5 Sb 0.5 O z OER photoanode established Ni–Sb–O as an important system for computational understanding of both the structural and catalytic behavior of these complex oxides. In the present work we show that Ni x Sb 1-x O z with x > 0.33 crystallizes into a previously unknown phase. Guided by experimental X-ray diffraction, here, we use density functional theory calculations to perform a prototype phase search to identify a broad family of stable and metastable mixed rutile and hexagonal-like phases for x = 0.33, 0.50, and 0.66 compositions. For the identified phases, we predict favorable oxygen vacancy formation energies for Ni-rich compositions under the reducing synthesis conditions which match measured Ni K-edge X-ray absorption spectra. The calculated overpotential for the most active site decreases with increasing Ni content, from 0.91 V (x = 0.33) to 0.49 V (x = 0.66), which captures the experimentally observed trend. We find the active site changes from the Ni–O–Sb bridge to a Ni–O–Ni bridge at increasing Ni concentrations, rather than the commonly studied singly under-coordinated sites. Finally, detailed Pourbaix analysis of the identified phases show excellent electrochemical stability, consistent with experimentally measured low metal ion concentrations in the electrolyte of photoelectrochemical cells. Collectively, our consideration of an ensemble of structures enables identification of the most catalytically prolific structural motifs, aiding the understanding of crystalline and amorphous catalysts and elucidating the co-optimization of activity and durability in nickel antimonates.
Here, this paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.
Ca-ion electrochemical systems have been pushed to the forefront of recent multivalent energy storage advances due to their use of earth-abundant redox materials and their high theoretical specific densities in relation to monovalent or even other more widely explored multivalent-charge carriers. However, significant pitfalls in metal plating and stripping arise from electrolyte decomposition and can be related to the coordination environment around Ca 2+ with both the negatively charged anion and the organic–aprotic solvent. Here, we apply multiple spectroscopic techniques in conjunction with density functional theory to evaluate the coordination environment of Ca 2+ across a class of ethereal solvents. Through the combination of X-ray absorption fine structure and time-dependent density functional theory, descriptive measures of the local geometry, coordination, and electronic structure of Ca–ethereal complexes provide distinct structural trends depending on the extent of the Ca 2+ –solvent interaction. Finally, we correlate these findings with electrochemical measurements of calcium tetrakis(hexafluoroisopropoxy)borate (CaBHFIP 2 ) salts dissolved within this class of solvents to provide insight into the preferred structural configuration of Ca 2+ electrolytic solutions for optimized electrochemical plating and stripping.
A simplified procedure is presented for analysis of ducts with discretely nonuniform properties. The analysis uses basis functions as the generalized coordinates. The duct eigenfunctions are approximated by finite series of these functions. The emphasis is on solution of the resulting large sparse set of linear equations. Characteristics of sparse matrix algorithms are outlined and some criteria for application are established. Analogies with structural methods are used to illustrate variations which can increase efficiency in generating values for design optimization routines. The effects of basis function selection, number of eigenfunctions and identification and ordering of equations on the sparsity and solution stability are included.
The scales of temporal and spatial variations in stratospheric minor species concentrations and dynamical quantities are briefly reviewed. Despite the fact that the stratosphere is often considered to be a very quiescent region of the atmosphere, it exhibits transient and generally unpredictable episodes of intense activity. The resultant necessity for some degree of coordination between different observers in their choice of locations and times for measurements of minor constituents is emphasized. A specific suggestion is provided for one or more short 'International Stratospheric Periods' whose duration would depend upon the time of year. During these short periods, as many observers as possible would perform an integrated series of observations designed to optimize the usefulness of their data to atmospheric scientists.
We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.
Abstract Microalgae hold great promises as sustainable cellular factories for the production of alternative fuels, feeds, and biopharmaceuticals for human health. While the biorefinery approach for fuels along with the coproduction of high-value compounds with industrial, therapeutic, or nutraceutical applications have the potential to make algal biofuels more economically viable, a number of challenges continue to hamper algal production systems at all levels. One such hurdle includes the metabolic trade-off often observed between the increased yields of desired products, such as triacylglycerols (TAG), and the growth of an organism. Initial genetic engineering strategies to improve lipid productivity in microalgae, which focused on overproducing the enzymes involved in fatty acid and TAG biosynthesis or inactivating competing carbon (C) metabolism, have seen some successes albeit at the cost of often greatly reduced biomass. Emergent approaches that aim at modifying the dynamics of entire metabolic pathways by engineering of pertinent transcription factors or signaling networks appear to have successfully achieved a balance between growth and neutral lipid accumulation. However, the biological knowledge of key signaling networks and molecular components linking these two processes is still incomplete in photosynthetic eukaryotes, making it difficult to optimize metabolic engineering strategies for microalgae. Here, we focus on nitrogen (N) starvation of the model green microalga, Chlamydomonas reinhardtii , to present the current understanding of the nutrient-dependent switch between proliferation and quiescence, and the drastic reprogramming of metabolism that results in the storage of C compounds following N starvation. We discuss the potential components mediating the transcriptional repression of cell cycle genes and the establishment of quiescence in Chlamydomonas, and highlight the importance of signaling pathways such as those governed by the target of rapamycin (TOR) and sucrose nonfermenting-related (SnRK) kinases in the coordination of metabolic status with cellular growth. A better understanding of how the cell division cycle is regulated in response to nutrient scarcity and of the signaling pathways linking cellular growth to energy and lipid homeostasis, is essential to improve the prospects of biofuels and biomass production in microalgae.
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Subnanometer transition-metal clusters have been shown to possess catalytic activity that is size-dependent. It has been suggested that the fluxionality of these small clusters may be closely related to their catalytic activity. Here we use basin-hopping global optimization with density functional theory (DFT) to study the energy landscape of Ptn (n = 10–13) clusters. We analyze a large set of local minima obtained from the DFT-based global optimization. We find that Pt10 is unique with respect to the other studied sizes in its structural landscape, which shows a single, distinct structural motif corresponding to a tetrahedral global minimum. In contrast, Pt11–13 all display characteristics of high fluxionality with the presence of multiple significantly differing structural features in the low-energy region, as characterized by coordination number, interatomic distances, and shape. These observations demonstrate the structural diversity and fluxionality of the subnanometer Pt clusters that will have important implications for catalysis.
Here, we use a nonequilibrium variational principle to optimize the steady-state, shear-induced interconversion of self-assembled nanoclusters of DNA-coated colloids. Employing this principle within a stochastic optimization algorithm allows us to identify design strategies for functional materials. We find that far-from-equilibrium shear flow can significantly enhance the flux between specific colloidal states by decoupling trade-offs between stability and reactivity required by systems in equilibrium. For isolated nanoclusters, we find nonequilibrium strategies for amplifying transition rates by coupling a given reaction coordinate to the background shear flow. We also find that shear flow can be made to selectively break detailed balance and maximize probability currents by coupling orientational degrees of freedom to conformational transitions. For a microphase consisting of many nanoclusters, we study the flux of colloids hopping between clusters. We find that a shear flow can amplify the flux without a proportional compromise on the microphase structure. This approach provides a general means of uncovering design principles for nanoscale, autonomous, functional materials driven far from equilibrium.
Abstract In many fields of science and engineering, we frequently encounter experiments or simulations datasets that describe the behavior of complex systems and uncovering human interpretable patterns between their inputs and outputs via exploratory data analysis is essential for building intuition and facilitating discovery. Often, we resort to 2D embeddings for examining these high-dimensional relationships (e.g. dimensionality reduction). However, most existing embedding methods treat the dimensions as coordinates for samples in a high-dimensional space, which fail to capture the potential functional relationships, and the few methods that do take function into consideration either only focus on linear patterns or produce non-linear embeddings that are hard to interpret. To address these challenges, we proposed function preserving projections (FPP), which construct 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns between the domain and the range of a high-dimensional function. The intuition here is that humans are good at understanding potentially non-linear patterns in 2D but unable to interpret non-linear mapping from high-dimensional space to 2D. Therefore, we should restrict the projection to linear but not the pattern we are seeking. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in extremely large datasets that could not be processed with existing dimension reduction methods.
FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation): Multifidelity aerodynamic polar data generation for hydrofoil/tidal-turbine airfoil sections. Foilpolars ties together three pieces: *AeroSandbox supplies the baseline airfoil coordinates (UIUC database). *G2Aero parameterizes those shapes on a Grassmannian manifold (Karcher mean + PGA basis) and samples new perturbed shapes around that basis. *XFoil (panel method) and NeuralFoil (neural-network surrogate, shipped with AeroSandbox) each solve the resulting shapes for lift, drag, moment, and pressure at the swept angles of attack, Reynolds numbers, and n_crit values. Design optimization of foil shapes in a computationally efficient way requires polars data across many candidate shapes, not just a handful of baseline foils. However, high-fidelity CFD at that scale is too costly, and naive shape perturbation strays from realistic geometries. FOILPOLARS addresses this by loading baseline airfoils (via AeroSandbox) and mapping them onto a Grassmannian manifold (via G2Aero), computing a Karcher mean and principal geodesic analysis (PGA) basis. New shapes are sampled by perturbing PGA coefficients, keeping them close to the manifold of realistic foils. Each sampled shape is evaluated across a configurable sweep of angle of attack, Reynolds number, and critical amplification factor using two solvers: XFoil (panel method) and NeuralFoil (neural-network surrogate), producing a paired dataset of lift, drag, moment, pressure, convergence, and confidence, indexed alongside each shape's PGA coefficients and shared Grassmannian basis in a single xarray dataset. From this, FOILPOLARS produces convergence summaries and comparison plots per shape, Reynolds number, and n_crit. A command-line interface exposes each pipeline stage independently, supporting data-driven design, optimization, and machine-learning workflows for foils.
Periplasmic cytochrome A (PpcA) is a representative of a broad class of multiheme cytochromes functioning as protein “nanowires” for storage and extracellular transfer of multiple electrons in the δ-proteobacterium Geobacter sulfurreducens. PpcA contains three bis-His coordinated hemes held in a spatial arrangement that is highly conserved among the multiheme cytochromes c 3 and c 7 families, carries low potential hemes, and is notable for having one of the lowest number of amino acids utilized to maintain a characteristic protein fold and site-specific heme function. Low temperature X-band electron paramagnetic resonance (EPR) spectroscopy has been used to characterize the electronic configuration of the Fe(III) and the ligation mode for each heme. The three sets of EPR signals are assigned to individual hemes in the three-dimensional crystal structure. The relative energy levels of the Fe(III) 3d orbitals for individual hemes were estimated from the principal g-values. The observed g-tensor anisotropy was used as a probe of electronic structure of each heme, and differences were determined by specifics of axial ligation. To ensure unambiguous assignment of highly anisotropic low-spin (HALS) signal to individual hemes, EPR analyses of iron atom electronic configurations have been supplemented with investigation of porphyrin macrocycles by one-dimensional 1 H NMR chemical shift patterns for the methyl substituents. In conclusion, within optimized geometry of hemes in PpcA, the magnetic interactions between hemes were found to be minimal, similar to the c 3 family of tetraheme cytochromes.
We elucidate the structural evolution of CoN 4 sites during thermal activation by developing a zeolitic imidazolate framework (ZIF)-8-derived carbon host as an ideal model for Co 2+ ion adsorption. Subsequent in situ X-ray absorption spectroscopy analysis can dynamically track the conversion from inactive Co-OH and Co-O species into active CoN 4 sites. The critical transition occurs at 700 °C and becomes optimal at 900 °C, generating the highest intrinsic activity and four-electron selectivity for the oxygen reduction reaction (ORR). DFT calculations elucidate that the ORR is kinetically favored by the thermal-induced compressive strain of Co-N bonds in CoN 4 active sites formed at 900 °C. Further, we developed a two-step (i.e., Co ion doping and adsorption) Co-N-C catalyst with increased CoN 4 site density and optimized porosity for mass transport, and demonstrated its outstanding fuel cell performance and durability.
Mesoporous Fe-doped MgO nanoparticles were synthesized using a facile sol-gel method and utilized for photo-Fenton-like degradation of salicylic acid (SA). The MgO surface dissolution facilitated an increase in the pH under the reaction conditions that allowed the Fe-MgO catalyst to be active without detectable iron leaching. Under simulated solar radiation, SA was completely degraded with an initial rate constant of 0.048 min -1 at the optimal reaction conditions of 500 ppm loading of 5% Fe-MgO, 20 mM H 2 O 2 concentration, and 50 ppm SA concentration. The catalyst was stable over 5 reaction cycles. The Fe-MgO catalyst was shown to have a surface area up to 171 m 2 /g and contain hematite (Fe 2 O 3 ) nanoparticles with octahedrally coordinated iron catalytic centers, as inferred from diffuse reflectance UV–vis measurements. Post-reaction catalyst characterization showed that some Fe 2+ was present in the catalyst due to the redox cycle during the chain initiation and propagation steps of the reaction.