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This poster provides an overview on a linear approximation line pack model for integrated electricity and natural gas systems OPF.
Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.
Presentation on the "Effects of Fluid Axial Conduction on Liquid Metal Natural Circulation and Linear Stability" 35 slides
The Alfvén-cyclotron instability is driven by an ion temperature anisotropy such that T ⊥ /T || >1 where ⊥ and || denote directions perpendicular and parallel to a uniform background magnetic field B o , respectively. The computations presented here consider a model of a magnetized, homogeneous, collisionless plasma. Two representations of the proton velocity distribution are considered: a single bi-Maxwellian and a magnetospheric-like configuration of two components, a more dense, relatively cool, isotropic component and a less dense, relatively hot, bi-Maxwellian component which drives the instability. Only wave propagation parallel to B o is considered. Here, using numerical solutions of the full kinetic linear dispersion equation, concise analytic expressions for the scaling of the dimensionless maximum instability growth rate and the corresponding dimensionless real frequency are derived as functions of three dimensionless variables: the hot proton temperature anisotropy, the relative hot proton density, and the hot proton β || . Furthermore, using one-dimensional hybrid particle-in-cell simulations of this same instability, a third relation for the scaling of the maximum amplitude of the dimensionless fluctuating magnetic field energy density is derived.
Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called “better”, “faster”, and “less-biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. Further, to improve the interpretability of the latent space, a variational Kullback–Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.
Understanding the emergent electronic structure in twisted atomically thin layers has led to the exciting field of twistronics. However, practical applications of such systems are challenging since the specific angular correlations between the layers must be precisely controlled and the layers have to be single crystalline with uniform atomic ordering. Here, in this work, an alternative, simple, and scalable approach is suggested, where nanocrystalline two-dimensional (2D) film on 3D substrates yields twisted-interface-dependent properties. Ultrawide-bandgap hexagonal boron nitride (h-BN) thin films are directly grown on high in-plane lattice mismatched wide-bandgap silicon carbide (4H-SiC) substrates to explore the twist-dependent structure-property correlations. Concurrently, nanocrystalline h-BN thin film shows strong non-linear second-harmonic generation and ultra-low cross-plane thermal conductivity at room temperature, which are attributed to the twisted domain edges between van der Waals stacked nanocrystals with random in-plane orientations. First-principles calculations based on time-dependent density functional theory manifest strong even-order optical nonlinearity in twisted h-BN layers. This work unveils that directly deposited 2D nanocrystalline thin film on 3D substrates could provide easily accessible twist-interfaces, therefore enabling a simple and scalable approach to utilize the 2D-twistronics integrated in 3D material devices for next-generation nanotechnology.
Aminopolymer-based adsorbents are a prominent class of materials being used for direct air capture of CO 2 at the industrial scale. However, improving their working lifetime, specifically by increasing their resilience to oxidative degradation, remains an ongoing challenge. Toward this end, functionalization of aminopolymers with non-amine functionalities such as hydroxyls has emerged in recent years as a promising strategy toward improving adsorbent lifetime. Although there is a growing body of work demonstrating the effectiveness of this approach and investigating the origin of this improved stability, studies to date have primarily focused on branched aminopolymer systems such as branched poly(ethylenimine). In this work, hydroxyl-functionalized linear poly(ethylenimine) is used to continue to probe the underlying protective mechanism of this strategy. A combination of thermogravimetric analysis, NMR relaxometry, differential scanning calorimetry, and computational simulations is used to better understand the relationship between the extent of chemical functionalization, physical properties, and adsorbent performance.
Abstract Hydrofluoroethers (HFEs) have been adopted widely as electrolyte cosolvents for battery systems because of their unique low solvating behavior. The electrolyte is currently utilized in lithium‐ion, lithium–sulfur, lithium–air, and sodium‐ion batteries. By evaluating the relative solvating power of different HFEs with distinct structural features, and considering the shuttle factor displayed by electrolytes that employ HFE cosolvents, we have established the quantitative structure–activity relationship between the organic structure and the electrochemical performance of the HFEs. Moreover, we have established the linear free‐energy relationship between the structural properties of the electrolyte cosolvents and the polysulfide shuttle effect in lithium–sulfur batteries. These findings provide valuable mechanistic insight into the polysulfide shuttle effect in lithium–sulfur batteries, and are instructive when it comes to selecting the most suitable HFE electrolyte cosolvent for different battery systems.
Linear scaling relationships (SRs), which relate binding energies of adsorbates across a space of catalyst surfaces, have been extensively explored for metal and oxide surfaces, but little is known about their properties at interfaces between metal nanoparticles and oxide supports, which are ubiquitous in heterogeneous catalysis. Using periodic DFT calculations, scaling principles are extended to bifunctional Au/oxide interfaces. Adopting a Au nanorod on doped MgO (100) as a model, SRs for species participating in water gas shift, methanol synthesis, and oxidation reactions are reported. SR slopes are not constrained by the bond order conservation rule postulated for metals, oxides, and zeolites, potentially permitting greater flexibility in catalyst design strategies. Finally, the deviation from bond counting, along with the physical origin of scaling behavior at interfaces, are explored using a conceptual framework involving electrostatic interactions at the Au/oxide interface.
Abstract Hydrofluoroethers (HFEs) have been adopted widely as electrolyte cosolvents for battery systems because of their unique low solvating behavior. The electrolyte is currently utilized in lithium‐ion, lithium–sulfur, lithium–air, and sodium‐ion batteries. By evaluating the relative solvating power of different HFEs with distinct structural features, and considering the shuttle factor displayed by electrolytes that employ HFE cosolvents, we have established the quantitative structure–activity relationship between the organic structure and the electrochemical performance of the HFEs. Moreover, we have established the linear free‐energy relationship between the structural properties of the electrolyte cosolvents and the polysulfide shuttle effect in lithium–sulfur batteries. These findings provide valuable mechanistic insight into the polysulfide shuttle effect in lithium–sulfur batteries, and are instructive when it comes to selecting the most suitable HFE electrolyte cosolvent for different battery systems.
In this study, we formed core–shell-like polyelectrolyte complexes (PECs) from an anionic bottlebrush polymer with poly (acrylic acid) side chains with a cationic linear poly (allylamine hydrochloride). By varying the pH, the number of side chains of the polyanionic BB polymers (Nbb), the charge density of the polyelectrolytes, and the salt concentration, the phase separation behavior and salt resistance of the complexes could be tuned by the conformation of the BBs. By combining the linear/bottlebrush polyelectrolyte complexation with all-liquid 3D printing, flow-through tubular constructs were produced that showed selective transport across the PEC membrane comprising the walls of the tubules. These tubular constructs afford a new platform for flow-through delivery systems.
Bimetallic catalysts offer enhanced catalytic performance through synergistic interactions between the two metals, allowing them to break the linear scaling relations and reach high electrocatalytic activity. This study presents bimetallic aerogel-based catalyst synthesized as a covalent, three-dimensional framework containing neighboring iron and manganese sites. The aerogel structure provides a high surface area and porosity, facilitating an ultra-high active site density and efficient mass transport. The MnFe porphyrrole's unique structure is obtained by alternately linking Mn-porphyrin and Fe-corrole complexes. It exhibited outstanding performance with an onset potential of 0.99 V RHE . Comparative studies with a free-base Fe porphyrrole catalyst (E onset 0.97 V RHE ) revealed that while Mn incorporation led to only a slight improvement in half-cell performance, it resulted in significantly enhanced performance in anion exchange membrane fuel cell. The MnFe catalyst achieved an OCV of 0.97 V and a peak power density of 0.27 W cm −2 , outperforming the free-base Fe counterpart. Using density functional theory calculations, we show that the higher ORR activity of MnFe-porphyrrole is due to charge transfer between Mn and Fe atoms, which is absent in the reference free-base Fe-porphyrrole. These findings underscore the advantages of bimetallic catalysts in improving ORR activity and fuel cell efficiency by leveraging synergistic effects.
Abstract Linear inverse models (LIMs) are used to explore predictability and information content of the Madden–Julian Oscillation (MJO). Hindcast skill for outgoing longwave radiation (OLR) related to the MJO on intraseasonal timescales in the tropics has been examined for a variety of LIMs using OLR and optionally 200 and 850 hPa zonal wind information channels. The dependence of OLR hindcast skill on wind channels was evaluated by randomizing in time, averaging in space, or omitting data entirely. Results show positive prediction skill (relative to climatology) up to 3 weeks and wind information, mostly at the largest scales, adds 1–2 days of skill.
Here, the combination of two different β-diketone ligands facilitates the size-controlled assembly of pure heterometallic [LnLn'Ln] linear compounds thanks to two different coordination sites present in the molecular scaffold. [HoCeHo], [ErCeEr], and [YbCeYb] analogues are presented here and are characterized both in the solid state and in solution, demonstrating the selectivity of this unique method to produce heterometallic 4f molecular entities.
The reductive cyclization of two linear hexasilanes is contrasted, where a methylated precursor yielded a cyclohexasilane while the tert-butyl-functionalized analog unexpectedly yielded a cyclopentasilane. A comprehensive analysis using X-ray diffraction, IR, HR, HRMS, and multinuclear (1H, 13C and 29Si) 1D and 2D NMR spectroscopy identified the 1,2-dihydrodisilane tBuPhHSi–SiHPhtBu as an additional product, which was interpreted as supportive of a mechanism involving silylene elimination. Here, the results of this study may prove informative about substituent effects in the practice of complex organosilicon molecular synthesis.
We describe a bulk adjusted linear combination of atomic orbitals (BA-LCAO) approach for nanoparticles. In this method, we apply a many-body scaling function (in similar manner as in the environment-modified total energy based tight-binding method) to the DFT-derived diatomic AO interaction potentials (like in the conventional orbital-based density-functional tight binding approach) strictly according to atomic valences acquired naturally in a bulk structure. This modification, (a) facilitates all atom orbital-based electronic structure calculations of charge carrier dynamics in nanoscale structures with a molecular acceptor, and (b) allows to closely match high-level density functional calculation data (previously adjusted to the available experimental findings) for bulk structures. To advance practical application of the BA-LCAO approach we parameterize the Hamiltonian of wurtzite CdSe by fitting its band structure to a high-level DFT reference, corrected for experimentally measured band edges. Here, unlike in conventional DFTB approach, we: (1) use hydrogen-like AOs for the basis as exact atomic eigenfunctions, while orbital energies of which are taken from experimentally measured ionization potentials, and (2) parameterize the many-body scaling functions rather than the atomic wavefunctions. Development of this approach and parameters is guided by our aim to devise a method capable of simultaneously treating the problems of (i) interfacial electron/hole transfer between finite, variable size nanoparticles and electron scavenging molecules, and (ii) high-energy electronic transitions (Auger transitions) that mediate multi-exciton decay in quantum dots. Electronic structure results are described for CdSe quantum dots of various sizes.
In this work, we present a detailed analysis of the linear polarizability (α) and second hyperpolarizability (γ) in a series of streptocyanines, as predicted with various range-separated functionals and CCSD(T)-based methods. Contrary to previous work on these systems, we find that the lowest-energy electronic states for the larger streptocyanine oligomers are not closed-shell singlets, and improved accuracy can be obtained with certain DFT methods by allowing the system to relax to a lower-energy broken-symmetry solution. Our extensive analyses are complemented by new large-scale CCSD(T) and explicitly correlated CCSD(T)-F12 calculations that comprise the most complete and accurate benchmarks of α and γ for the streptocyanine systems to date. Taken together, our CCSD(T) and broken-symmetry DFT calculations (1) show that the MP2 benchmarks used in previous studies still exhibit significant errors (~ 25% for α and ~100% for γ) and, therefore, the MP2 calculations should not be used as reliable benchmarks for polarizabilities or hyperpolarizabilities, and (2) emphasize the importance of testing for a lower-energy open-shell configuration when calculating nonlinear optical properties for these systems. © 2018 Wiley Periodicals, Inc.
Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network temporal evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this work, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present useful modeling techniques and apply them to mathematically express and constrain a broad class of network properties in the context of an optimization formulation. We derive complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies.