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使用红外响应功能性织品使用人工智能进行光学标签

Yi-Ting Tsai¹, Chi-Wei Wu^1,2, Ren-Jei Chung²

¹Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan.

ACS applied materials & interfaces

|October 11, 2025

概括

研究人员开发了一种可持续的织品,使用叶纤维和量子点进行隐蔽的光学编码. 这种智能材料可通过红外成像检测,并被AI识别,从而实现安全应用.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
织工程织工程织工程

背景情况:

传统的染料是可见的,易于检测.
在织品中需要隐藏的光学功能.
现有安全标签方法的局限性.

研究的目的:

创建一个可持续的织平台,隐藏的光学功能.
为了使隐蔽信息能够在天然纤维中进行编码.
使用人工智能来证明材料层面的识别.

主要方法:

在叶纤维 (PALF) 中整合SiO2/Al2O3涂层的性Ag2S量子点.
使用短波红外 (SWIR) 成像进行检测.
在超光谱成像数据上训练一个卷积神经网络 (CNN).

主要成果:

生物复合材料显示出出色的光稳定性和水相容性.
隐藏的光学功能只能在SWIR成像下检测到.
在识别光学功能化的毫米尺度线样本方面,CNN的准确率达到了82%.

结论:

这项工作表明了可再生材料,纳米光子学和人工智能之间的新协同作用.
开发的织平台在安全标签和防伪方面提供了实际实用性.
互动性织品的潜力与看不见的,AI可检测的特征.

关键词:

人工智能识别识别AI识别卷积神经网络是一种卷积神经网络.超光谱成像技术的使用.短波红外线是一种短波红外线.智能织品智能织品是什么

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