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One of the primary hurdles in microdevice fabrication lies in ascertaining the most impactful tactics for adapting metal surfaces. Through a one-pot tackle and distinct mechanochemical reactions evoked by 15 min aqueous wet sand-milling (SM-15), we successfully grafted Mo-based metal–organic frameworks (Mo-MOFs) onto graphene oxides (GOs). Following this, a convenient and readily scalable methodology of electrophoretic deposition was implemented to create controllable thickness of SM-15 GOs@Mo-MOFs lubricating films, achieving considerable enhancements of 143% and 91% in hardness and Young's modulus, respectively, when compared to those of SM-15 Mo-MOFs. The successful synthesis of SM-15 GOs@Mo-MOFs was corroborated using strategies such as x-ray diffraction, Fourier transform infrared spectroscopy, and field emission scanning electron microscopy. Analyses using the micro-tribotester indicated that the new film exhibited a lowest friction coefficient of roughly 0.5 when imposed with a load of 5 N and sliding speed of 8 mm/s. In addition, the optical profiler nano-indentation in situ scanning probe microscope revealed that SM-15 GOs@Mo-MOFs films had smaller and shallower scratches and grooves compared to SM-15 Mo-MOFs ones. The calculated results of key descriptors (EHOMO, ELUMO, ΔE, etc.) in density functional theory quantitatively disclosed the interaction mechanisms between GOs@Mo-MOFs molecules and microdevices. We first scrutinized the innate properties of molecule adsorption energy and frictional mechanical behaviors using synergetic cross-scale simulations, such as Monte Carlo and finite element methods. The expectation was that this process would motivate a valuable technique for shielding in the thriving micromanufacturing. [ABSTRACT FROM AUTHOR]

Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8's capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model's feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an mAP@0.5 of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the mAP@0.5 increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis. [ABSTRACT FROM AUTHOR]