Development of Amperometric Microbiosensors for Neurochemical Detection

Abnormal neurochemical signaling is often the underlying cause of brain disorders. Electrochemical microsensors are widely used to monitor neurochemicals with high spatial-temporal resolution. This research aimed to understand and develop highperformance microsensors to detect two types of neurotransmitters: glutamate and dopamine. This work included optimizing multiple parameters used to determine performance of an enzyme-based glutamate microsensor or carbon nanomaterials-based dopamine microsensor. The parameters included sensor surfaces, glutamate oxidase, interferent exclusive layers, storage methods, self-referencing, carbon nanotube coating, polymer exclusive layer applications et al. The developed sensor was also tested in animals. Tuning key parameters allowed the developed microsensors to exhibit a sensitivity as high as 530±34 nA/cm2µM (Mean ± SEM), an excellent ascorbic acid selectivity of 841±54 (Mean ± SEM) for in vitro beaker studies. The microsensor achieved excellent long-term stability in a wet storage method. A microsensor was also used successfully for real time measuring of glutamate ex vivo in brain slices with a fast response time and in vivo in a free-behaving rat after introduced status epilepsy. As for dopamine sensor development, a carbon nanotube modified diamond microelectrode was developed for improved detection of dopamine. Modified microelectrodes were then characterized by cyclic voltammetry, scanning electron microscopy, x-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy (EIS). With regard to implantable microsensors, the as-received platinum surface with a thin nafion coating has a comparatively low sensitivity to dopamine of 0.62±0.02 µA/cm2µM, but a competitive selectivity of 670±50 and limit of detection of 25 nM. Furthermore, after carbon nanotube coating, we found a drastic increase in sensitivity (45.7±2.3 µA/cm2µM), and limit of detection was reduced to 5 nM. With an additional ionic-exclusive layer of thick Nafion, we obtained a high selectivity of 683±17 at the cost of sacrificing sensitivity down to 13.5±0.6 µA/cm2µM. This sensor was found to last for at least one month when dry stored in the box. In summary, this dissertation formed a systematic study of electrochemical microsensors for neurochemical detection. Improved glutamate and dopamine microsensors have been developed; this work led to a comprehensive understanding of microsensor microarrays in brain chemical application

Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95{\deg} and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.Comment: To be appeared in ICB201