The development of surface-enhanced Raman scattering (SERS) devices for detection of trace pesticides has attracted more and more attention. In this work, a large-area self-assembly approach assisted with reactive ion etching (RIE) is proposed for preparing SERS devices consisting of Ag-covered "hedgehog-like" nanosphere arrays (Ag/HLNAs). Such a SERS device has an enhancement factor of 2:79×10⁷, a limit of detection (LOD) up to 10^-12M for Rhodamine 6G (R6G) analytes, and a relative standard deviation (RSD) smaller than 10%, demonstrating high uniformity. Besides, for pesticide detections, the device achieves an LOD of 10^-8M for thiram molecules. It indicates that the proposed SERS device has a promising opportunity in detecting toxic organic pesticides.

基于静态Allan方差分析方法无法有效分析和辨识动态工况下激光陀螺仪的随机误差,也无法给动态工况下激光陀螺仪的随机误差补偿提供准确依据。为此,提出时间框动态Allan方差分析方法,利用分段建模对随机误差项进行动态Allan方差分析和辨识。建立灰色GM(1,1)预测模型,对辨识出的随机误差参数项进行预测,针对传统GM(1,1)预测模型因数据不全存在波动大的问题,基于小波滤波平滑处理原始数据,并利用残差修正模型改进GM(1,1)预测模型。实验结果表明,针对激光陀螺仪在同一工况下的随机误差系数,改进GM(1,1)模型预测算法的预测精度高于传统GM(1,1)模型预测算法的预测精度。