Complex Sparse Signal Recovery with Adaptive Laplace Priors

Because of its self-regularizing nature and uncertainty estimation, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most methods are based on the real-value signal model, with the complex-value signal model rarely considered. Typically, the complex signal model is adopted so that phase information can be utilized. Therefore, it is non-trivial to develop Bayesian models for the complex-value signal model. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning (SBL) framework, a hierarchical model with adaptive Laplace priors is proposed for applications of complex sparse signal recovery in this paper. The proposed hierarchical Bayesian framework is easy to extend for the case of multiple measurement vectors. Moreover, the space alternating principle is integrated into the algorithm to avoid using the matrix inverse operation. In the experimental section of this work, the proposed algorithm is concerned with both complex Gaussian random dictionaries and directions of arrival (DOA) estimations. The experimental results show that the proposed algorithm offers better sparsity recovery performance than the state-of-the-art methods for different types of complex signals

A hybrid physical-data approach for solving dynamic optimal power flow considering uncertainties and different topology configurations

The security and economy of power system operation or planning are threatened by various uncertain factors, including the unpredictability of renewable energy, the variation of topology configurations, and significant load fluctuation. It is a challenge to solve dynamic optimal power flow efficiently for the operation and planning of power system considering uncertainties on both Renewable generation and Load, as well as different Topology configurations (RLT-DOPF). For fast solving the RLT-DOPF problem, we propose a hybrid physical-data approach using a physics-driven method — two-stage robust optimization approach based on an improved uncertainty set, and a data-driven method combined the Graph convolutional network with Long short term memory named GL-DOPF. The two-stage robust optimization approach can supply robust solutions for the RLT-DOPF problem under various topology configurations. Then the GL-DOPF model is trained using a training dataset that includes robust solutions. The solutions for RTL-DOPF are available with the trained GL-DOPF model and an AC power flow calculation fast. Simulations on IEEE 30/300-bus systems show that the speedup of the proposed hybrid physical-data approach is x25/29 as compared to a robust optimization method for RLT-DOPF under almost the same accuracy and the solutions for RTL-DOPF are feasible and robust. The training dataset for GL-DOPF generated from the proposed two-stage robust optimization model is less conservative than the original RO models and can cover the prediction errors of uncertainties. The effectiveness and robustness of the proposed hybrid physical-data approach for the RLT-DOPF problem clearly outperform

Epitaxial Growth of ZnO Nanodisks with Large Exposed Polar Facets on Nanowire Arrays for Promoting Photoelectrochemical Water Splitting

Single-crystalline and branched 1D arrays, ZnO nanowires/nanodisks (NWs/NDs) arrays, are fabricated to significantly enhance the performance of photoelectrochemical (PEC) water splitting. The epitaxial growth of the ZnO NDs with large exposed polar facets on ZnO NWs exhibits a laminated structure, which dramatically increases the light scattering capacity of the NWs arrays, especially in the wavelength region around 400 nm. The ND branching of the 1D arrays in the epitaxial fashion not only increase surface area and light utilization, but also support fast charge transport, leading to the considerable increase of photocurrent. Moreover, the tiny size NDs can facilitate charge separation and reduce charge recombination, while the large exposed polar facets of NDs reduce the external potential bias needed for water splitting. These advantages land the ZnO NWs/NDs arrays a four times higher power conversion efficiency than the ZnO NWs arrays. By sensitizing the ZnO NWs/NDs with CdS and CdSe quantum dots, the PEC performance can be further improved. This work advocates a trunk/leaf in forest concept for the single-crystalline NWs/NDs in array with enlarged exposure of polar facets, which opens the way for optimizing light harvesting and charge separation and transport, and thus the PEC water splitting

High performance inverted structure perovskite solar cells based on a PCBM:polystyrene blend electron transport layer

Hybrid organic/inorganic perovskite solar cells are among the most competitive emerging photovoltaic technologies. Here, we report on NiO-based inverted structure perovskite solar cells with a high power conversion efficiency of 10.68%, which is achieved by adding a small percentage (1.5 wt%) of high molecular weight polystyrene (PS) into the PCBM electron transport layer (ETL). The addition of PS facilitates the formation of a highly smooth and uniform PCBM ETL that is more effective in preventing undesirable electron-hole recombination between the perovskite layer and the top electrode. As a result, the V-OC of the PCBM: PS-based cells is increased from 0.97 V to 1.07 V, which leads to significantly enhanced power conversion efficiencies of the solar cells. Our study provides a simple and low-cost approach to improving the ETL film quality and the performance of inverted perovskite solar cells

High-Performance Hole-Extraction Layer of Sol-Gel-Processed NiO Nanocrystals for Inverted Planar Perovskite Solar Cells

Hybrid organic/inorganic perovskite solar cells have been rapidly evolving with spectacular successes in both nanostructured and thin-film versions. Herein, we report the use of a simple sol-gel-processed NiO nanocrystal (NC) layer as the hole-transport layer in an inverted perovskite solar cell. The thin NiO NC film with a faceted and corrugated surface enabled the formation of a continuous and compact layer of well-crystallized CH3NH3PbI3 in a two-step solution process. The hole-extraction and -transport capabilities of this film interfaced with the CH3NH3PbI3 film were higher than those of organic PEDOT:PSS layers. The cell with a NiO NC film with a thickness of 30-40nm exhibited the best performance, as a thinner layer led to a higher leakage current, whereas a thicker layer resulted in a higher series resistance. With the NiO film, we observed a cell efficiency of 9.11%, which is by far the highest reported for planar perovskite solar cells based on an inorganic hole-extracting layer

Efficiency Enhancement of Perovskite Solar Cells through Fast Electron Extraction: The Role of Graphene Quantum Dots

We report on a significant power conversion efficiency improvement of perovskite solar cells from 8.81% to 10.15% due to insertion of an ultrathin graphene quantum dots (GQDs) layer between perovskite and TiO₂. A strong quenching of perovskite photoluminescence was observed at ∼760 nm upon the addition of the GQDs, which is pronouncedly correlated with the increase of the IPCE and the APCE of the respective cells. From the transient absorption measurements, the improved cell efficiency can be attributed to the much faster electron extraction with the presence of GQDs (90–106 ps) than without their presence (260–307 ps). This work highlights that GQDs can act as a superfast electron tunnel for optoelectronic devices