ABSTRACT
Metal-organic frameworks (MOFs) offer promising opportunities for modifying energetic materials due to their micro-porous structure and high performance. In this study, we present a novel green MOF named cyclodextrin-MOF (CD-MOF), which incorporates potassium ions, synthesized using a simple methanol vapor diffusion approach. The CD-MOF incorporates potassium ions and enhances propellant performance through intermolecular force optimization with nitrocellulose (NC). Molecular dynamics simulations reveal stronger interactions between the CD-MOF and NC. The loading of the CD-MOF within NC forms a stable structure with resistance to migration and defense against crystalline precipitation and water absorption. Notably, in static combustion and pyrolysis tests, the CD-MOF exhibits efficient flame and flash inhibition. The thermal degradation and cauterization of the CD-MOF resulted in the formation of a complex microporous material capable of absorbing flammable and harmful gases such as CO, NO, NO2, and N2O. These findings shed light on the superior performance of the CD-MOF compared to conventional inorganic salts, and the comprehensive characterization and molecular simulations provide insights into the unique properties and applications of the CD-MOF, emphasizing its significant contribution to the field of green propellants.
ABSTRACT
Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.
