Modulating Electronic Structure of Iridium Single-Atom Anchored on 3D Fe-Doped ß-Ni(OH)2 Catalyst with Nanopyramid Array Structure for Enhanced Oxygen Evolution Reaction.

Developing high-performance electrocatalysts for oxygen evolution reaction (OER) is crucial in the pursuit of clean and sustainable hydrogen energy, yet still challenging. Herein, a spontaneous redox strategy is reported to achieve iridium single-atoms anchored on hierarchical nanosheet-based porous Fe doped ß-Ni(OH)2 pyramid array electrodes (SAs Ir/Fe-ß-Ni(OH)2), which exhibits high OER performance with a low overpotential of 175 mV at 10 mA cm-2 and a remarkable OER current density in alkaline electrolyte, surpassing Fe-ß-Ni(OH)2/NF and IrO2 by 31 and 38 times at 1.43 V versus RHE, respectively. OER catalytic mechanism demonstrates that the conversion of *OH→*O and the active lattice O content can be significantly improved due to the modulation effect of the Ir single atoms on the local electronic structure and the redox behavior of FeNi (oxy) hydroxide true active species. This work provides a promising insight into understanding the OER enhancement mechanism for Ir single-atoms modified FeNi-hydroxide systems.

Coupling Ferricyanide/Ferrocyanide Redox Mediated Recycling Spent LiFePO4 with Hydrogen Production.

Replacing the oxygen evolution reaction with thermodynamically more favorable alternative oxidation reactions offers a promising alternative to reduce the energy consumption of hydrogen production. However, questions remain regarding the economic viability of alternative oxidation reactions for industrial-scale hydrogen production. Here, we propose an innovative cost-effective, environment-friendly and energy-efficient strategy for simultaneous recycling of spent LiFePO4 (LFP) batteries and hydrogen production by coupling the spent LFP-assisted ferricyanide/ferrocyanide ([Fe(CN)6 ]4- /[Fe(CN)6 ]3- ) redox reaction. The onset potential for the electrooxidation of [Fe(CN)6 ]4- to [Fe(CN)6 ]3- is low at 0.87 V. Operando Raman and UV/Visible spectroscopy confirm that the presence of LFP in the electrolyte allows for the rapid reduction of [Fe(CN)6 ]3- to [Fe(CN)6 ]4- , thereby completing the [Fe(CN)6 ]4- /[Fe(CN)6 ]3- redox cycle as well as facilitating the conversion of spent LiFePO4 into LiOH ⋅ H2 O and FePO4 . The electrolyzer consumes 3.6 kWh of electricity per cubic meter of H2 produced at 300 mA cm-2 , which is 43 % less than conventional water electrolysis. Additionally, this recycling pathway for spent LFP batteries not only minimizes chemical consumption and prevents secondary pollution but also presents significant economic benefits.

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information.