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With the rapid development of industry, the increasing discharge of sewage causes the detection of water quality to be of increasing importance. Potassium dichromate titration is one of the most important testing methods in water quality detection; the ability to accurately identify the titration end-point of potassium dichromate is currently a research challenge. To identify titration end-point quickly and accurately, this study proposes a ResNet14Attention network, which utilizes residual modules that focus on original image information and an attention mechanism that focuses highly on classification targets. The proposed ResNet14Attention network is compared with 12 convolutional neural networks such as ResNet series networks, VGG, and GoogLeNet. The results of comparison experiments reveal that only the proposed ResNet14Attention network has the highest training and testing accuracy of 100% among all convolutional neural networks in the comparison experiment; the proposed ResNet14Attention network has the highest training speed compared to all the networks that over 90% accuracy.

The oxygen evolution reaction (OER) is the efficiency-determining half-reaction process of high-demand, electricity-driven water splitting due to its sluggish four-electron transfer reaction. Tremendous effects on developing OER catalysts with high activity and strong acid-tolerance at high oxidation potentials have been made for proton-conducting polymer electrolyte membrane water electrolysis (PEMWE), which is one of the most promising future hydrogen-fuel-generating technologies. This review presents recent progress in understanding OER mechanisms in PEMWE, including the adsorbate evolution mechanism (AEM) and the lattice-oxygen-mediated mechanism (LOM). We further summarize the latest strategies to improve catalytic performance, such as surface/interface modification, catalytic site coordination construction, and electronic structure regulation of catalytic centers. Finally, challenges and prospective solutions for improving OER performance are proposed.