Electrochemical Oxidation of Ammonia on Ir Anode in Potential Fixed Electrochemical Sensor

Ir catalyst possesses a good electrocatalytic activity and selectivity for the oxidation of NH3 and/or NH4OH at Ir anode in the potential fixed electrochemical sensor with the neutral solution. Owing to the same electrochemical behavior of NH3 and NH4OH in a NaClO4 solution, NH4OH can be used instead of NH3 for the experimental convenience. It was found that the potential of the oxidation peak of NH4OH at the Ir/GC electrode in NaClO4 solutions is at about 0.85 V, and the current density of the oxidation peak of NH4OH is linearly proportional to the concentration of NHaOH. The electrocatalytic oxidation of NH4OH is diffusion-controlled. Especially, Ir has no electrocatalytic activity for the CO oxidation, illustrating that CO does not interfere in the measurement of NH4OH and the potential fixed electrochemical NH3 sensor with the neutral solution, and the anodic Ir catalyst possesses a good selectivity. Therefore, Ir may have practical application in the potential fixed electrochemical NH3 sensor with the neutral solution.

A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF(Electric Field)sensors.Currently,an EF sensor or EPS(Electric Potential Sensor)system is attracting attention as a next-generationmotion sensing technology due to low computation and price,high sensitivity and recognition speed compared to other sensor systems.However,it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion,due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions.This hinders the further utilization of the EF sensing;thus,it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices.In this study,we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject.This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy.After setting motion frames,we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types.Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1%accuracy,99.25%detection rate,98.4%motion frame matching rate and 97.7%detection&extraction success rate.