简介:Co(II)-salenwasencapsulatedinMIL-100(Cr)metalorganicframeworkby"shipinabottle"tosynthesizeanewelectrocatalyst,Cosalen@MIL-100(Cr).ThematerialwascharacterizedbyXRD,FT-IR,UV-VisandN2-adsorption.TheCosalen@MIL-100(Cr)modifiedglassycarbonelectrodeexhibitsawell-definedreductionpeakatthepotentialof–0.21Vtowardtheoxygenreductionreaction(ORR)bycyclicvoltammetry(CV)inpH=6.84phosphatebuffer.Almost400mVpositiveshiftofpotentialatCosalen@MIL-100(Cr)modifiedelectrodeforORRcomparedwiththatatbareglassycarbon,indicatesthatCosalen@MIL-100(Cr)possessesexcellentelectrocatalyticactivity.ThetransferrednumberofelectronsforORRwasdeterminedbychronocoulometry.TheresultsuggeststhattheintroductionofCo(II)-salencomplexintoMOFincreasestheelectrocatalyticactivityviaafour-electronreductionpathway.Furthermore,thiselectrocatalystexhibitsgoodstabilityandreproducibility.
简介:针对系统误差的不确定性可能会引起滤波精度降低或发散的问题,提出一种新的基于模型预测滤波的前向神经网络算法。首先,采用模型预测滤波估计网络在正向传递过程中的模型误差,并对其进行修正,以弥补模型误差对隐含层权值更新的影响;然后,利用模型预测滤波为神经网络提供精确的训练样本,学习待估计系统的非线性关系。将提出的算法应用于SINS/CNS/BDS组合导航系统,并与扩展卡尔曼滤波进行比较,仿真结果表明,提出的算法得到的姿态误差、速度误差和位置误差分别在[-0.25′,+0.25′]、[-0.05m/s,+0.05m/s]和[-5m,+5m]以内,滤波性能明显优于扩展卡尔曼滤波算法,表明该算法能提高组合导航定位的解算精度。