10.13% Efficiency All-Polymer Solar Cells Enabled by Improving the Optical Absorption of Polymer Acceptors

The limited light absorption capacity for most polymer acceptors hinders the improvement of the power conversion efficiency (PCE) of all-polymer solar cells (all-PSCs). Herein, by simultaneously increasing the conjugation of the acceptor unit and enhancing the electron-donating ability of the donor unit, a novel narrow-bandgap polymer acceptor PF3-DTCO based on an A–D–A-structured acceptor unit ITIC16 and a carbon–oxygen (C–O)-bridged donor unit DTCO is developed. The extended conjugation of the acceptor units from IDIC16 to ITIC16 results in a red-shifted absorption spectrum and improved absorption coefficient without significant reduction of the lowest unoccupied molecular orbital energy level. Moreover, in addition to further broadening the absorption spectrum by the enhanced intramolecular charge transfer effect, the introduction of C–O bridges into the donor unit improves the absorption coefficient and electron mobility, as well as optimizes the morphology and molecular order of active layers. As a result, the PF3-DTCO achieves a higher PCE of 10.13% with a higher short-circuit current density (Jsc) of 15.75 mA cm−2 in all-PSCs compared with its original polymer acceptor PF2-DTC (PCE = 8.95% and Jsc = 13.82 mA cm−2). Herein, a promising method is provided to construct high-performance polymer acceptors with excellent optical absorption for efficient all-PSCs

Robust Framework for PET Image Reconstruction Incorporating System and Measurement Uncertainties

In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts

Generation of integration-free neural progenitor cells from cells in human urine

Human neural stem cells hold great promise for research and therapy in neural disease. We describe the generation of integration-free and expandable human neural progenitor cells (NPCs). We combined an episomal system to deliver reprogramming factors with a chemically defined culture medium to reprogram epithelial-like cells from human urine into NPCs (hUiNPCs). These transgene-free hUiNPCs can self-renew and can differentiate into multiple functional neuronal subtypes and glial cells in vitro. Although functional in vivo analysis is still needed, we report that the cells survive and differentiate upon transplant into newborn rat brain.postprin

Deep Unfolding Sparse Bayesian Learning Network for Off-Grid DOA Estimation with Nested Array

Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of their improved estimation accuracy and reduced computational cost. However, few have considered the existence of a nested array (NA) with off-grid DOA estimation. In this study, we present a deep sparse Bayesian learning (DSBL) network to solve this problem. We first establish the signal model for off-grid DOA with NA. Then, we transform the array output into a real domain for neural networks. Finally, we construct and train the DSBL network to determine the on-grid spatial spectrum and off-grid value, where the loss function is calculated using reconstruction error and the sparsity of network output, and the layers correspond to the steps of the sparse Bayesian learning algorithm. We demonstrate that the DSBL network can achieve better generalization ability without training labels and large-scale training data. The simulation results validate the effectiveness of the DSBL network when compared with those of existing methods

Convolution Neural Networks for Localization of Near-Field Sources via Symmetric Double-Nested Array

We present the convolution neural networks (CNNs) to achieve the localization of near-field sources via the symmetric double-nested array (SDNA). Considering that the incoherent near-field sources can be separated in the frequency spectrum, we first calculate the phase difference matrices and consider the typical elements as the inputs of the networks. In order to guarantee the precision of the angle-of-arrival (AOA) estimation, we implement the autoencoders to divide the AOA subregions and construct the corresponding classification CNNs to obtain the AOAs of near-field sources. Then, we construct a particular range vector without the estimated AOAs and utilize the regression CNN to obtain the range parameters of near-field sources. The proposed algorithm is robust to the off-grid parameters and suitable for the scenarios with the different number of near-field sources. Moreover, the proposed method outperforms the existing method for near-field source localization

High-Transparency and Colorless Polyimide Film Prepared by Inhibiting the Formation of Chromophores

Colorless polyimides (CPIs) with outstanding mechanical properties are essential materials in the production of flexible display panels, foldable windows, and even spacecraft cockpits. This paper specifically elaborates that the Morkit unit, and azo and nitro chromophores are important factors contributing to yellow PI, together with the well-known charge transfer complex (CTC) theory. Three diamine monomers, two anhydrides monomers, and three blockers were used to inhibit chromophores formation and, thus, obtain CPI films. The cut-off wavelength was blue-shifts to 334 nm and the transmittance is improved to 98.9% in the UV–vis range. Mechanical and thermal properties of the CPI films are not reduced through coupling effects of the blockers. Therefore, the inhibition method of the Morkit units and chromophore groups is a promising process for preparing CPIs to be used as flexible display materials

Recent Progress in Functionalized Coatings for Corrosion Protection of Magnesium Alloys—A Review

Magnesium (Mg) and its alloys, which have good mechanical properties and damping capacities, are considered as potential candidate materials in the industrial field. Nevertheless, fast corrosion is the main obstacle that seriously hinders its wide applications. Surface modification is an available method to avoid the contact between corrosive media and Mg substrates, thus extending the service life of Mg-based materials. Generally, manufacturing a dense and stable coating as physical barriers can effectively inhibit the corrosion of Mg substrates; however, in some complex service environments, physical barrier coating only may not satisfy the long-term service of Mg alloys. In this case, it is very important to endow the coating with suitable functional characteristics, such as superhydrophobic and self-healing properties. In this review, the various surface treatments reported are presented first, followed by the methods employed for developing superhydrophobic surfaces with micro/nanostructuring, and an overview of the various advanced self-healing coatings, devolved on Mg alloys in the past decade, is further summarized. The corresponding preparation strategies and protection mechanisms of functional coatings are further discussed. A potential research direction is also briefly proposed to help guide functional strategies and inspire further innovations. It is hoped that the summary of this paper will be helpful to the surface modification of Mg alloys and promote the further development of this emerging research field