This study introduces a computational framework for precisely quantifying electrical conductance in metallic nanomesh structures, leveraging advanced image processing and computer vision algorithms. Nanomesh images obtained via scanning electron microscopy are subjected to preprocessing operations, including threshold-based binary conversion and convolution techniques, to mitigate defects and delineate conductive pathways. A rigorous equivalent electrical path model is subsequently established through keypoint identification via mean-shift segmentation. Kirchhoff's current law is applied to the model to deduce the conductance of the nanomesh. The computationally estimated conductance results are stringently validated against experimental measurements, confirming the model's accuracy. Further, the framework is applied to ascertain the resistivity of nanoscale Ag films deposited on glass substrates, revealing a resistivity higher than that of bulk materials—a finding corroborated by both computational predictions and experimental data. The methodology can be a robust, automated analytical tool for assessing conductance in comparable nanostructured materials.

中文翻译：

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使用图像处理和计算机视觉技术量化金属纳米网格中电导的计算框架


本研究介绍了一个计算框架，用于利用先进的图像处理和计算机视觉算法精确量化金属纳米网格结构中的电导率。通过扫描电子显微镜获得的纳米网格图像经过预处理操作，包括基于阈值的二进制转换和卷积技术，以减少缺陷并描绘导电通路。随后，通过均值偏移分割的关键点识别建立严格的等效电路径模型。将 Kirchhoff 的当前定律应用于模型，以推断纳米网格的电导。计算估计的电导结果与实验测量进行了严格的验证，证实了模型的准确性。此外，该框架还用于确定沉积在玻璃基板上的纳米级 Ag 薄膜的电阻率，揭示了高于块状材料的电阻率——这一发现得到了计算预测和实验数据的证实。该方法可以是一种强大的自动化分析工具，用于评估可比纳米结构材料的电导率。