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Optical Labeling with Artificial Intelligence Using Infrared-Responsive Functional Textiles.

Yi-Ting Tsai1, Chi-Wei Wu1,2, Ren-Jei Chung2

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Summary

Researchers developed a sustainable textile using pineapple leaf fiber and quantum dots for covert optical encoding. This intelligent material is detectable with infrared imaging and recognized by AI, enabling secure applications.

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AI recognitionconvolutional neural networkhyperspectral imagingshort-wave infraredsmart textiles

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Textile Engineering

Background:

  • Conventional dyes are visible and easily detectable.
  • Need for concealed optical functionalities in textiles.
  • Limitations of current methods for secure labeling.

Purpose of the Study:

  • To create a sustainable textile platform with concealed optical functionality.
  • To enable covert information encoding in natural fibers.
  • To demonstrate material-level recognition using AI.

Main Methods:

  • Integration of SiO2/Al2O3-coated hydrophilic Ag2S quantum dots into pineapple leaf fiber (PALF).
  • Utilizing short-wave infrared (SWIR) imaging for detection.
  • Training a convolutional neural network (CNN) on hyperspectral imaging data.

Main Results:

  • The biogenic composite showed excellent photostability and water compatibility.
  • Concealed optical functionality was detectable only under SWIR imaging.
  • CNN achieved 82% accuracy in identifying optically functionalized millimeter-scale yarn samples.

Conclusions:

  • This work demonstrates a novel synergy between renewable materials, nanophotonics, and AI.
  • The developed textile platform offers practical utility in secure labeling and anticounterfeiting.
  • Potential for interactive textiles with invisible, AI-detectable features.