Ion leaching from pure-phase oxygen-evolving electrocatalysts generally exists, leading to the collapse and loss of catalyst crystalline matrix. Here, different from previous design methodologies of pure-phase perovskites, we introduce soluble BaCl 2 and SrCl 2 into perovskites through a self-assembly process aimed at simultaneously tuning dual cation/anion leaching effects and optimizing ion match in perovskites to protect the crystalline matrix. As a proofof-concept, self-assembled hybrid Ba 0.35 Sr 0.65 Co 0.8 Fe 0.2 O 3-δ (BSCF) nanocomposite (with BaCl 2 and SrCl 2) exhibits the low overpotential of 260 mV at 10 mA cm-2 in 0.1 M KOH. Multiple operando spectroscopic techniques reveal that the pre-leaching of soluble compounds lowers the difference of interfacial ion concentrations and thus endows the host phase in hybrid BSCF with abundant time and space to form stable edge/face-sharing surface structures. These self-optimized crystalline structures show stable lattice oxygen active sites and short reaction pathways between Co-Co/Fe metal active sites to trigger favorable adsorption of OH − species.

water electrolysis shows greater advantages in clean and sustainable hydrogen generation due to its beneficial features of wide water resources, renewable electric power sources (e.g., from wind, solar, and hydroelectric power), and environmentalfriendly reaction processes. [3,4] However, the efficiency of this promising technology is limited by the sluggish kinetics of oxygen-evolution reaction (OER) at the anode and hydrogen-evolving reaction (HER) at the cathode. [4,5] In terms of producing one gas molecular, the four-. [5,6] Therefore, developing efficient OER catalyst candidates becomes one of the biggest challenges in alkaline water electrolysis. [6,7] Among all the alternatives of high-cost Ir/ Ru-based compounds, cost-effective and scalable Co-based oxides, with large compositional tunability, abundant molecular structures and rich electronic structural features, have attracted considerable interest in catalyzing OER. [8,9] In the past decade, the development of oxides for alkaline OER based on ex situ design principles has attained remarkable achievements. On one hand, various important oxide families have been explored, including spinels (AB 2 O 4 ), [7,10] perovskites (ABO 3 ), [8,11] and perovskite-type oxides. [9] On the other hand, many OER activity descriptors, such as e g -orbital filling, [11] orbital band center, [12] and charge-transfer energy, [13] were proposed to understand the structure-activity relationships and guide the discovery of new promising candidates. However, very recent studies based on operando techniques found that surface structures and electronic structures of some oxides would be reconstructed under electro-derived OER processes. [14][15][16][17] The surface reconstruction behaviors on oxides can induce the leaching of active ions from oxide matrix, [14,18] which may lead to a low utilization of oxide active ions and a poor stability. Therefore, developing dynamic active and stable sites in oxides is crucial but quite challenging.3D oxides with corner-sharing structures (Figure 1 left) were widely studied for alkaline OER. [9,19] Shao-Horn's group reported corner-sharing Ba 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3−δ (BSCF) perovskite, which exhibited a much higher intrinsic activity than the IrO 2 catalyst. [11] However, their subsequent studies found that Corner-sharing oxides usually suffer from structural reconstruction during the bottleneck oxygen-evolution reaction (OER) in water electrolysis. Therefore, introducing dynamically stable active sites in an alternative structure is urgent but challenging. Here, 1D 5H-polytype Ba 5 Bi 0.25 Co 3.75 FeO 14−δ oxide with facesharing motifs is identified as a highly active and stable candidate for alkaline OER. Benefiting from the stable face-sharing motifs with three couples of combined bonds, Ba 5 Bi 0.25 Co 3.75 FeO 14−δ can maintain its local structures even under high OER potentials as evidenced by fast operando spectroscopy, contributing to a negligible performance degradation over 110 h. Besides, the higher Co valence and smaller orb...

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For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal wounds, lameness and fractures, and will no doubt reduce animal welfare. The purpose of this paper is to develop an efficient learning algorithm that is able to detect the mounting behaviour of pigs based on the data characteristics of visible light images. Four Göttingen minipigs were selected as experimental subjects and were monitored for a week by a camera that overlooked the pen. The acquired videos were analysed and the frames containing mounting behaviour were intercepted as positive samples of the dataset, and the images with inter-pig adhesion and separated pigs were taken as negative samples. Pig segmentation network based on Mask Region-Convolutional Neural Networks (Mask R-CNN) was applied to extract individual pigs in the frames. The region of interest (RoI) parameters and mask coordinates of each pig, from which eigenvectors were extracted, could be obtained. Subsequently, the eigenvectors were classified with a kernel extreme learning machine (KELM) to determine whether mounting behaviour has occurred. The pig segmentation presented considerable accuracy and mean pixel accuracy (MPA) with 94.92% and 0.8383 respectively. The presented method showed high accuracy, sensitivity, specificity and Matthews correlation coefficient with 91.47%, 95.2%, 88.34% and 0.8324 respectively. This method can be an efficient way of solving the problem of segmentation difficulty caused by partial occlusion and adhesion of pig bodies, even if the pig body colour was similar to the background, in recognition of mounting behaviour.