溶剂变色
材料科学
制作
炸薯条
纳米技术
过程(计算)
鉴定(生物学)
生物系统
计算机科学
光子学
混合(物理)
微型加热器
溶剂
降级(电信)
溶解度
有机溶剂
化学过程
工艺工程
聚合物
荧光
传感器阵列
信号(编程语言)
液晶
溶解度参数
肉眼
水溶液
光电子学
液晶显示器
实验室晶片
作者
Jintao Zhang,Hao Ouyang,Yin Cheng,Nuoye Xiong,Xunchi Ma,Xueling Li,Guangming Zhu,Liang Zhang,Jing Sun,Ranran Wang
摘要
Colorimetric sensors for instant and on-the-spot identification of organic liquids have been actively developed across diverse fields such as chemical hazard inspection, manufacturing process monitoring, and beverage safety. Multi-target sensing scenarios often necessitate large-scale sensor arrays, which inevitably increase the fabrication complexity, complicate visual interpretation, and exacerbate data redundancy. Here, we present a biomimetic photonic-gustatory chip (BiPC) comprising a polyacrylate-based solvatochromic disk (PSD) array. The elastic photonic crystal PSDs generate distinct color patterns when swelled by organic solvents. To fully unlock the sensing potential, a Hansen solubility parameter (HSP) framework was developed to assess the overall swelling cross-reactivity toward target liquids, facilitating the design of a compact PSD array with HSP-guided chemical diversity and maximized inter-solvent response distinctiveness. Using intuitive pattern recognition, an HSP-optimized four-PSD array effectively differentiates solvent types and mixing ratios, homologues and isomers, as well as alcoholic beverages. Furthermore, we developed a smartphone application that integrated image capture with deep-learning-assisted cloud computing for on-site solvent identification, achieving high accuracy (97.1% across 80 liquid classes) within 30 s. The HSP framework is generalizable to the design of compact sensor arrays for various field-deployable liquid sensing scenarios.
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