Complex multifunctional coatings combining order and disorder are central for information, biomedical, transportation, and energy technologies. Their scalable fabrication is possible using nanostructured composites made by layer-by-layer assembly (LBL). Here, we show that structural descriptions encompassing their nonrandom disorder and related property-focused design are possible using graph theory (GT). Two-dimensional images of LBL films of silver and gold nanowires (NWs) were used to calculate GT representations. We found that random stick computational models often used to describe NW, nanofiber, and nanotube materials give inaccurate predictions of their structure. Concurrently, image-informed GT models accurately predict the structure and properties of the LBL films, including the unexpected nonlinearity of charge transport vs. LBL cycles. The conductivity anisotropy in LBL composites, not readily detectable with microscopy, was accurately predicted using GT models. Spray-assisted LBL offers the direct translation of GT predictions to additive, scalable coatings for drones and potentially other technologies.

中文翻译：

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逐层组装的纳米线网络可实现多功能涂层的图论设计


有序与无序相结合的复杂多功能涂层是信息、生物医学、运输和能源技术的核心。使用逐层组装 （LBL） 制成的纳米结构复合材料，可以进行可扩展的制造。在这里，我们表明，使用图论 （GT） 可以包含其非随机无序和相关的以属性为中心的设计的结构描述。银和金纳米线 （NWs） 的 LBL 薄膜的二维图像用于计算 GT 表示。我们发现，通常用于描述 NW、纳米纤维和纳米管材料的随机棒计算模型对其结构的预测不准确。同时，图像知情 GT 模型准确预测 LBL 薄膜的结构和特性，包括电荷传输与 LBL 循环的意外非线性。LBL 复合材料中的电导率各向异性，不容易用显微镜检测到，使用 GT 模型准确预测。喷涂辅助 LBL 可将 GT 预测直接转化为用于无人机和潜在其他技术的增材、可扩展涂层。