RESUMEN
Bismuth vanadate (BVO) is regarded as an exceptional photoanode material for photoelectrochemical (PEC) water splitting, but it is restricted by the severe photocorrosion and slow water oxidation kinetics. Herein, a synergistic strategy combined with a Co3(HPO4)2(OH)2 (CoPH) cocatalyst and an Al2O3 (ALO) passivation layer was proposed for enhanced PEC performance. The CoPH/ALO/BVO photoanode exhibits an impressive photocurrent density of 4.9 mA cm-2 at 1.23 VRHE and an applied bias photon-to-current efficiency (ABPE) of 1.47% at 0.76 VRHE. This outstanding PEC performance can be ascribed to the suppressed surface charge recombination, facilitated interfacial charge transfer, and accelerated water oxidation kinetics with the introduction of the CoPH cocatalyst and ALO passivation layer. This work provides a novel and synergistic approach to design an efficient and stable photoanode for PEC applications by combining an oxygen evolution cocatalyst and a passivation layer.
RESUMEN
Processing images for specific targets on a large scale has to handle various kinds of contents with regular processing steps. To segment objects in one image, we utilized dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) to build a background (BG) gray-level variation mesh, which can help to identify BG and object regions. It was developed from a macroscopic perspective on image BG gray levels and implemented using standard procedures, thus robustly dealing with large-scale database images. The image segmentation capability of existing methods can be exploited by the BG mesh to improve object segmentation accuracy. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Content-based image retrieval (CBIR) was carried out to evaluate the object segmentation capability in dealing with large-scale database images. Retrieval precision-recall (PR) and rank performances, with and without SEGON, were compared. For multi-instance retrieval with shape feature, AdaBoost was used to select salient common feature elements. For color features, the histogram intersection between two scalable HSV descriptors was calculated, and the mean feature vector was used for multi-instance retrieval. The distance measure for color feature can be adapted when both positive and negative queries are provided. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance with multifeature query. Experiments showed that the proposed object segmentation method outperforms others by 21% in the PRI. Performing SEGON-enabled CBIR on large-scale databases also improves on the PR performance reported elsewhere by up to 42% at a recall rate of 0.5. The proposed object segmentation method can be extended to extract other image features, and new feature types can be incorporated into the algorithm to further improve the image retrieval performance.
