Layered Structural PBAT Composite Foams for Efficient Electromagnetic Interference Shielding

The utilization of eco-friendly,lightweight,high-efficiency and high-absorbing electromagnetic interference(EMI)shielding composites is imperative in light of the worldwide promotion of sustainable manufacturing.In this work,magnetic poly(butyleneadipate-coterephthalate)(PBAT)microspheres were firstly synthesized via phase separation method,then PBAT composite foams with layered structure was constructed through the supercritical carbon dioxide foaming and scraping techniques.The merits of integrating ferroferric oxideloaded multi-walled carbon nanotubes(Fe3O4@MWCNTs)nanoparticles,a microcellular framework,and a highly conductive silver layer have been judiciously orchestrated within this distinctive layered configuration.Microwaves are consumed throughout the process of“absorption-reflection-reabsorption”as much as possible,which greatly declines the secondary radiation pollution.The biodegradable PBAT composite foams achieved an EMI shielding effectiveness of up to 68 dB and an absorptivity of 77%,and authenticated favorable stabilization after the tape adhesion experiment.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

Preparation of graphene/MgCl_2-supported Ti-based Ziegler-Natta catalysts by the coagglomeration method and their application in ethylene polymerization

We report a facile coagglomeration method for preparing graphene (G)/MgCl2‐supported Ti‐based Ziegler‐Natta catalysts. The effects of graphene feed ratio on catalyst morphology and ethylene polymerization behavior were examined. The synthesized catalyst exhibited very high activity for ethylene polymerization. The resultant polyethylene (PE)/G nanocomposites showed a layered morphology, and the graphene fillers were well dispersed in the PE matrix. In addition, the thermal stability and mechanical properties of PE were significantly enhanced with the introduction of a very small amount of G fillers (0.05 wt%). This work provides a facile approach to the production o fhigh‐performance PE.

Localization and mapping algorithm based on Lidar-IMU-Camera fusion

Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be blocked,leading to inaccurate vehicle positioning.To ensure the security of automatic electric campus vehicles,this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM)algorithm with a monocular vision system added.An algorithm framework based on Lidar-IMU-Camera(Lidar means light detection and ranging)fusion was proposed.A lightweight monocular vision odometer model was used,and the LEGO-LOAM system was employed to initialize monocular vision.The visual odometer information was taken as the initial value of the laser odometer.At the back-end opti9mization phase error state,the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning.The visual word bag model was applied to perform loopback detection.Taking the test results into account,the laser radar loopback detection was further optimized,reducing the accumulated positioning error.The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment.The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav.Compared with the LEGO-LOAM algorithm,the results show that the proposed algorithm can effectively reduce map drift,improve map resolution,and output more accurate driving trajectory information.