27 Search Results

Abstract The field of two-dimensional (2D) ferromagnetism has been proliferating over the past few years, with ongoing interests in basic science and potential applications in spintronic technology. However, a high-resolution spectroscopic study of the 2D ferromagnet is still lacking due to the small size and air sensitivity of the exfoliated nanoflakes. Here, we report a thickness-dependent ferromagnetism in epitaxially grown Cr ₂ Te ₃ thin films and investigate the evolution of the underlying electronic structure by synergistic angle-resolved photoemission spectroscopy, scanning tunneling microscopy, x-ray absorption spectroscopy, and first-principle calculations. A conspicuous ferromagnetic transition from Stoner to Heisenberg-type is directly observedmore » in the atomically thin limit, indicating that dimensionality is a powerful tuning knob to manipulate the novel properties of 2D magnetism. Monolayer Cr ₂ Te ₃ retains robust ferromagnetism, but with a suppressed Curie temperature, due to the drastic drop in the density of states near the Fermi level. Our results establish atomically thin Cr ₂ Te ₃ as an excellent platform to explore the dual nature of localized and itinerant ferromagnetism in 2D magnets.« less

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second,more » two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.« less

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designedmore » for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.« less

Unsymmetric pentacenequinone-fused (cross-conjugated) and pentacene-fused (linear-conjugated) porphyrins were designed and synthesized. The cross-conjugated (AM₁–AM₃) and linear-conjugated (AM₅–AM₇) porphyrins displayed strikingly different sets of optical and electronic properties, both of which are unusual and nontypical of porphyrins. MCD, DFT, and TDDFT calculations suggest that multiple charge transfer states exist in both π-conjugated systems, which contributes to the complex absorption and MCD spectra of these molecular systems. The general Gouterman’s four-orbital model used to explain porphyrin spectroscopy led to contradicting theoretical and experimental data, and is thus not applicable for these molecular systems. The “2 + 4” and “3 + 3” activemore » spaces have been deduced and have proven effective to interpret the absorption and MCD spectra of the pentacenequinone-fused (cross-conjugated) and pentacene-fused (linear-conjugated) porphyrins, respectively. Spectroelectrochemistry of AM₅–AM₇ revealed broad and intense IR absorptions in the range of 1500–2500 nm, illustrating the exceptional ability of these pentacene-fused systems to accommodate positive charges. A pronounced metal effect was observed for pentacene-fused porphyrins. While pentacene-fused Ni(II) porphyrin (AM₆) demonstrated an abnormal ability to stabilize pentacene with a half-life of >28.3 days, the half-life of the free base and Zn(II) counterparts were normal, similar to those of pentacene analogues. This work provides important and useful information on guiding new material designs.« less

Building upon the recent progress on the intriguing underlying physics for the newly discovered infinite-layer nickelates, in this article we review an examination of valence charge and spin excitations via multi-orbital Hubbard models as way to determine the fundamental building blocks for Hamiltonians that can describe the low energy properties of infinite-layer nickelates. We summarize key results from density-functional approaches, and apply them to the study of x-ray absorption to determine the valence ground states of infinite-layer nickelates in their parent form, and show that a fundamental d⁹ configuration as in the cuprates is incompatible with a self-doped ground statemore » having holes in both d x 2 - y 2 and a rare-earth-derived axial orbital. When doped, we determine that the rare-earth-derived orbitals empty and additional holes form low spin (S = 0) d⁸ Ni states, which can be well-described as a doped single-band Hubbard model. Using exact diagonalization for a 2-orbital model involving Ni and rare-earth orbitals, we find clear magnons at 1/2 filling that persist when doped, albeit with larger damping, and with a dependence on the precise orbital energy separation between the Ni- and rare-earth-derived orbitals. Taken together, a full two-band model for infinite-layer nickelates can well describe the valence charge and spin excitations observed experimentally.« less

An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models withmore » the combined dataset resulted in a classification accuracy of 97.7%. This is a significant improvement over 89.7% to 92.5% accuracy obtained by relying on the field-recorded dataset alone.« less

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machinemore » (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.« less

β-Functionalized push–pull zinc opp-dibenzoporphyrins were designed and synthesized as sensitizers for dye-sensitized solar cells. The utilization of arylamine to replace aliphatic amine as the donor group has been proved to be an effective strategy to solve the solar cell instability reported previously. Three different arylamino groups including carbazole (YH8), diphenylamine (YH9), and phenothiazine (YH10) were investigated as arylamine donor groups, and different electronic effects were observed for these donor groups. Diphenylamine carrying YH9 and phenothiazine carrying YH10 displayed strong push–pull characteristics including segregated HOMO/LUMO and red-shifted and broadened absorption bands. In sharp contrast, the incorporation of carbazole (YH8) cancels outmore » the push–pull effect. YH9 and YH10 solar cells achieved high PCEs of 6.9% and 7.5% respectively, which are comparable to that of the best ruthenium dye, N719 (η = 7.7%), under similar conditions. Here this work demonstrates the great potential of π-extended porphyrins as sensitizers for dye-sensitized solar cells.« less