Enhanced Potentiometric Hydrogen Sensing Response Based on the Ba0.5Sr0.5Co1-yFeyO3-δ Electrode with Unusual Polarity.

In this work, unusual potentiometric hydrogen sensing of mixed conducting Ba0.5Sr0.5Co0.8Fe0.2O3-δ was reported. Inspired by the unusual polarity, a dual sensing electrode (SE) potentiometric hydrogen sensor was fabricated by pairing Ba0.5Sr0.5Co0.8Fe0.2O3-δ with electronic conducting ZnO to enhance the hydrogen response. Hydrogen sensing measurements suggested that significantly higher response, larger sensitivity, and lower limit of detection (LOD) were achieved by the dual SE sensor when compared with the single SE sensor based on Ba0.5Sr0.5Co0.8Fe0.2O3-δ or ZnO. A high response of 97.3 mV for 500 ppm hydrogen and a low LOD of 2.5 ppm were obtained by the dual SE sensor at 450 °C. Furthermore, the effect of the Fe doping concentration in Ba0.5Sr0.5Co1-yFeyO3-δ (y = 0.2, 0.5, and 0.8) on hydrogen sensing response was investigated. The potentiometric response values to hydrogen increased monotonically with increasing Fe doping concentration. With the Fe/Co atomic ratio increased from 0.25 to 4, the responses to 500 ppm hydrogen raised by 69.6 and 94% at 350 and 450 °C, respectively. The sensing behaviors of unusual Ba0.5Sr0.5Co1-yFeyO3-δ may be ascribed to the predominant surface electrostatic effect. These results show that mixed conducting Ba0.5Sr0.5Co1-yFeyO3-δ is desirable for developing high-performance dual SE hydrogen sensors.

Multivariate time series similarity searching.

Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches.