本研究对多巴胺(DA)进行光电化学检测,通过制备二氧化硅悬浮液(SiO2)、还原氧化石墨烯(rGO)、溴氧化铋(BiOBr)三种材料进而制备SiO2@BiOBr/rGO复合材料并构筑光电化学传感器,SiO2@BiOBr/rGO在有光时检测多巴胺时有显著的光电流响应。石...本研究对多巴胺(DA)进行光电化学检测,通过制备二氧化硅悬浮液(SiO2)、还原氧化石墨烯(rGO)、溴氧化铋(BiOBr)三种材料进而制备SiO2@BiOBr/rGO复合材料并构筑光电化学传感器,SiO2@BiOBr/rGO在有光时检测多巴胺时有显著的光电流响应。石墨烯具有优异的导电性和显著的机械强度。二氧化硅(SiO2)是一种成本低、高生物相容性、热稳定性、透光性能好、带隙窄的材料。BiOBr作为一种典型的半导体光电材料,拥有独特的层状四方结构,花状BiOBr由层状结构组成,有利于进一步抑制空穴–电荷重组,可提高其光电性能。该光电传感器检测多巴胺时的浓度范围为2~300 μmol/L,检出限为0.67 μmol/L,表明传感器对多巴胺有较好的检测效果。该SiO2@BiOBr/rGO光电化学传感器具有稳定性好、灵敏度高等优点,对多巴胺的检测具有重要的意义,希望其在监测细胞内多巴胺浓度水平方面具有广阔的应用前景。This study aimed to prepare SiO2@BiOBr/rGO composite materials by combining SiO2 suspension, reduced graphene oxide (rGO), and bismuth oxybromide (BiOBr) for the construction of relevant photoelectrochemical sensors to detect dopamine (DA). Under visible light irradiation, SiO2@BiOBr/ rGO exhibited a significant photocurrent response during dopamine detection. Graphene has excellent electrical conductivity and remarkable mechanical strength. SiO2 has low production cost, high biocompatibility, thermal stability, good transparency, and a narrow-forbidden bandgap. As a typical semiconductor photoelectric material, BiOBr has a unique layered tetragonal structure. Flower-like BiOBr is composed of layered structures, which is conducive to further suppressing hole-charge recombination and improving its photoelectric performance. The concentration range of dopamine detected by this photoelectric sensor was 2~300 μmol/L, with a detection limit of 0.67 μmol/L, indicating that the sensor had a good detection effect on dopamine. This SiO2@BiOBr/rGO photoelectrochemical sensor has the advantages of good stability, high sensitivity and so on, and is of great significance for the detection of dopamine. It is expected to have broad application prospects in monitoring intracellular dopamine concentration levels.展开更多
针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项...针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项,以便后端算法修正IMU零偏和轮式里程计测量噪声;其次,通过拓展卡尔曼滤波算法融合轮式里程计IMU数据,补偿机器人里程计精确度;最后,将整体最大后验概率问题(Maximum A Posteriori Estimation,MAP)转换为最小二乘问题,利用后端QR算法提高求解矩阵准确度。长走廊环境中,所提方法地图还原精度较LIO-SAM提高了32.61%;在相同实验场景中,机器人定位精度较LIO-SAM提高了35.47%。实验结果表明,所提方法具有较高的地图还原度与定位精度。展开更多
文摘本研究对多巴胺(DA)进行光电化学检测,通过制备二氧化硅悬浮液(SiO2)、还原氧化石墨烯(rGO)、溴氧化铋(BiOBr)三种材料进而制备SiO2@BiOBr/rGO复合材料并构筑光电化学传感器,SiO2@BiOBr/rGO在有光时检测多巴胺时有显著的光电流响应。石墨烯具有优异的导电性和显著的机械强度。二氧化硅(SiO2)是一种成本低、高生物相容性、热稳定性、透光性能好、带隙窄的材料。BiOBr作为一种典型的半导体光电材料,拥有独特的层状四方结构,花状BiOBr由层状结构组成,有利于进一步抑制空穴–电荷重组,可提高其光电性能。该光电传感器检测多巴胺时的浓度范围为2~300 μmol/L,检出限为0.67 μmol/L,表明传感器对多巴胺有较好的检测效果。该SiO2@BiOBr/rGO光电化学传感器具有稳定性好、灵敏度高等优点,对多巴胺的检测具有重要的意义,希望其在监测细胞内多巴胺浓度水平方面具有广阔的应用前景。This study aimed to prepare SiO2@BiOBr/rGO composite materials by combining SiO2 suspension, reduced graphene oxide (rGO), and bismuth oxybromide (BiOBr) for the construction of relevant photoelectrochemical sensors to detect dopamine (DA). Under visible light irradiation, SiO2@BiOBr/ rGO exhibited a significant photocurrent response during dopamine detection. Graphene has excellent electrical conductivity and remarkable mechanical strength. SiO2 has low production cost, high biocompatibility, thermal stability, good transparency, and a narrow-forbidden bandgap. As a typical semiconductor photoelectric material, BiOBr has a unique layered tetragonal structure. Flower-like BiOBr is composed of layered structures, which is conducive to further suppressing hole-charge recombination and improving its photoelectric performance. The concentration range of dopamine detected by this photoelectric sensor was 2~300 μmol/L, with a detection limit of 0.67 μmol/L, indicating that the sensor had a good detection effect on dopamine. This SiO2@BiOBr/rGO photoelectrochemical sensor has the advantages of good stability, high sensitivity and so on, and is of great significance for the detection of dopamine. It is expected to have broad application prospects in monitoring intracellular dopamine concentration levels.
文摘针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项,以便后端算法修正IMU零偏和轮式里程计测量噪声;其次,通过拓展卡尔曼滤波算法融合轮式里程计IMU数据,补偿机器人里程计精确度;最后,将整体最大后验概率问题(Maximum A Posteriori Estimation,MAP)转换为最小二乘问题,利用后端QR算法提高求解矩阵准确度。长走廊环境中,所提方法地图还原精度较LIO-SAM提高了32.61%;在相同实验场景中,机器人定位精度较LIO-SAM提高了35.47%。实验结果表明,所提方法具有较高的地图还原度与定位精度。
