A 2.20 eV Bandgap Polymer Donor for Efficient Colorful Semitransparent Organic Solar Cells

Semitransparent organic solar cells (ST-OSCs) have attracted increasing attention due to their promising prospect in building-integrated photovoltaics. Generally, efficient ST-OSCs with good average visible transmittance (AVT) can be realized by developing active layer materials with light absorption far from the visible light range. Herein, the development of ultrawide bandgap polymer donors with near-ultraviolet absorption, paired with near-infrared acceptors, is proposed to achieve high-performance ST-OSCs. The key points for the design of ultrawide bandgap polymers include constructing donor–donor type conjugated skeleton, suppressing the quinoidal resonance effect, and minimizing the twist of conjugated skeleton via noncovalent conformational locks. As a proof of concept, a polymer named PBOF with an optical bandgap of 2.20 eV is synthesized, which exhibited largely reduced overlap with the human eye photopic response spectrum and afforded a power conversion efficiency (PCE) of 16.40% in opaque device. As a result, ST-OSCs with a PCE over 10% and an AVT over 30% are achieved without optical modulation. Moreover, colorful ST-OSCs with visual aesthetics can be achieved by tuning the donor/acceptor weight ratio in active layer benefiting from the ultrawide bandgap nature of PBOF. This study demonstrates the great potential of ultrawide bandgap polymers for efficient colorful ST-OSCs.     

Parallel XPath query based on cost optimization

The Journal of Supercomputing - The performance of XPath query is the key factor to the capacity of XML processing. It is an important way to improve the performance of XPath by making full use of...     

Applying deep learning in football ankle injury for value of high-power magnetic resonance bioimaging evaluation

The Journal of Supercomputing - This study was to evaluate the performance of magnetic resonance imaging (MRI) reconstruction algorithm based on convolutional neural network (CNN) in the diagnosis...     

滚筒反力式制动检验台制动力测量不确定度评定

本文介绍了滚筒反力式制动检验台制动力的不确定度评定。     

The interlaminar shear failure characteristics of asphalt pavement coupled heating cables

Nowadays, heating cables are used as heat sources for heating pavements in practical engineering. However, there is a contradiction between the snow melting function and the interlaminar stability of heating pavement. In order to solve the contradiction, the interlaminar failure behavior of asphalt mixture coupled heating cables specimen (AMCS) was researched, through experiments and the finite element method. Under the different conditions of heating cables and rolling times, a series of direct shear tests was performed at the interface of AMCS, to compare the interlaminar stability of three different AMCS. Meanwhile, based on the bilinear cohesive zone model and coulomb friction model a 2D finite element model was established, to simulate this shear failure processes and make up for the limitations of the experiment. According to above test and simulation results, the failure mechanism and the weakest interface in AMCS were found, and the influence of the heating cable’s diameter and embedded spacing on the interlaminar shear strength were found. Then, a modified coulomb theorem model was proposed to predict the shear strength of the AMCS. This research enriches the design theory of the heating pavement and it has great significance for its structural design of heating asphalt pavement.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.