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The urgent demand for stable and highly efficient bifunctional electrocatalysts for the oxygen reduction/evolution reactions (ORR/OER) is pivotal in advancing energy conversion and storage technologies. Herein, we introduce a metal–organic framework (MOF)-derived bifunctional electrocatalyst Fe3Co-NC@900, featuring synergistically coupled dual single-atom sites (Fe/Co–Nx) and core–shell Co3Fe7@C nanoparticles. Leveraging the synergistic interactions of Fe–Nx sites with associated Co–Nx sites and core–shell nanoparticles within the MOF-derived nitrogen-doped mesoporous sheets and nanotubes, Fe3Co-NC@900 demonstrates outstanding ORR and OER performance, outpacing the Fe-NC@900 catalyst fabricated without Co incorporation. Specifically, it demonstrates superior ORR performance with a higher half-wave potential (E1/2) of 0.88 V and a small Tafel slope of 62.1 mV dec−1, surpassing the Pt/C benchmark (E1/2 of 0.82 V) and Fe-NC@900 (E1/2 of 0.788 V). Additionally, it demonstrates noteworthy OER performance, requiring a 1.572 V overpotential at 10 mA cm−2 current density, resulting in a significant bifunctional activity with a potential gap (ΔE) of 0.692 V, surpassing the Pt/C + Ir/C combination (ΔE = 0.748 V). Moreover, serving as a bifunctional air electrode, Fe3Co-NC@900 exhibits excellent performance in lab-made Zn–air batteries, with outstanding open circuit voltage, specific capacity, and cycling stability (>650 cycles), outpacing the Pt/C + Ir/C-based battery. [ABSTRACT FROM AUTHOR]

Nanofibres have been widely used in many chemical engineering applications and their performance greatly depends on the size distribution of the nanofibres. Researchers have developed automated tools to determine nanofibre diameters, primarily using commercial MATLAB software package. However, no researchers have reported automatic processing of multiple images, which is essential to the consistency and accuracy of results. Nor has anyone reported nanofibre sizing using deep learning. Therefore, this paper reports an automated tool to measure the size distribution of electrospun nanofibres by simultaneous multi‐image processing. This tool determines the diameters of nanofibres using deep learning based on UNet model. Results show that the UNet‐based deep learning approach is more accurate than those obtained using existing methods, compared to experimental data. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries (LiBs) are one of the most promising energy storage devices. However, the large‐scale application of LiBs is limited by their electrochemical properties. In this study, we built a three‐dimensional (3D) conductive network structure with carbon and 3DG coating VPO4 (VPO4@C@3DG) via a one‐pot hydrothermal method with subsequent high‐temperature annealing. The effects of the content of three‐dimensional porous graphene (3DG) on the crystal structure, morphology, and electrochemical properties of VPO4/C are investigated using characterization and electrochemical test techniques. The SEM images show that the size of sphere‐like particles of VPO4@C@3DG composite with 20 wt.% of 3DG (VPO4@C@3DG‐20) is the smallest in all samples. In addition, the electrochemical experimental results reveal that VPO4@C@3DG‐20 exhibits the best cycling and rate performance compared to other VPO4@C@3DG composites. Specifically, VPO4@C@3DG‐20 achieves an initial charge capacity of 601.2 mAh g−1 at 0.2 C (110 mA g−1) and keeps at 354 mAh g−1 at the 100th cycle. This is because the introduction of 20 wt.% 3DG graphene inhibits the growth and aggregation of the particles, thus shortening the diffusion path of Li+. In addition, the 3D conducting network structure boosts the conductivity of the materials and buffers the volume variation resulting from the charging/discharging process. [ABSTRACT FROM AUTHOR]