Controlling electrodeposited Ni layers by different-sized graphene oxides enables conductive e-textiles for the highly sensitive electrochemical detection of glucose

With the increasing popularity of wearable electronic devices,there is an urgent demand to develop electronic textiles(e-textiles)for device fabrication.Nevertheless,the difficulty in reconciliation between conductivity and manufacturing costs hinders their large-scale practical applications.Herein,we reported a facile and economic method for preparing conductive e-textiles.Specifically,nonconductive polypropylene(PP)was wrapped by reduced graphene oxide(rGO),followed by the electrodeposition of Ni nanoparticles(NPs).Notably,modulating the sheet size of graphene oxide(GO)resulted in controllable deposition of Ni NPs with adjustable size,allowing for controlled manipulations over the structures,morphologies,and conductivity of the obtained e-textiles,which influenced their performance in electrochemical glucose detection subsequently.The optimal material,denoted as Ni/rGO+(0.2)/PP,exhibited an impressive conductivity of 7.94×10^(4)S·m^(−1).With regard to the excellent conductivity of the as-prepared e-textiles and the high electrocatalytic activity of Ni for glucose oxidation,the asprepared e-textiles were subjected to glucose detection.It was worth emphasizing that the Ni/rGO_(0.2)/PP-based electrode demonstrated promising performance for nonenzymatic/label-free glucose detection,with a detection limit of 0.36μM and a linear response range of 0.5μM to 1 mM.This study paves the way for further development and application prospects of conductive etextiles.

Path-tracking control based on a dynamic trigonometric function

With the rapid development of the modern vehicle industry,the automated control of new vehicles is in increasing demand.However,traditional course control has been unable to meet the actual needs of such demand.To solve this problem,more precise pathtracking control technologies have attracted increased attention.This paper presents a new algorithm based on the latitude and longitude information,as well as a dynamic trigonometric function,to improve the accuracy of position deviation.First,the algorithm takes the course deviation and adjustment time as the optimization objectives and the given path and speed as the constraints.The controller continuously adjusts the output through a cyclic“adjustment and detection”process.Second,through an integration of the steering,positioning,and speed control systems,an experimental platform of a path-tracking control system based on the National Instruments(NI)myRIO controller and LabVIEW was developed.In addition,path-tracking experiments were carried out along a linear path,while changing lanes,and on a curved path.When comparing and analyzing the experimental results,it can be seen that the average deviation in lateral displacement along the linear and curved paths was 0.32 and
0.8 cm,and the standard deviation of the lateral displacement was 2.65 and 2.39 cm,respectively.When changing lanes,the total adjustment time for the vehicle close to the target line to reach stability was about 1.5 s.Finally,the experimental results indicate that the new algorithm achieves good stability and high control accuracy,and can overcome directional and positional errors caused by road interference while driving,meeting the precision requirements of automated vehicle control.