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Novel Techniques for Observing Structural Dynamics of Photoresponsive Liquid Crystals
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Deep learning-enhanced image analysis for liquid crystal optical sensing.

Yuxingyue Zhang, Mengjun Liu, Jiamei Chen

    Optics Letters
    |July 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Deep learning (DL) accelerates liquid crystal (LC) sensor analysis, improving speed and accuracy for detecting surfactants and insulin. This VGG16 model enhances sensing applications with precise, visualized results.

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    Area of Science:

    • Materials Science
    • Analytical Chemistry
    • Biotechnology

    Background:

    • Liquid crystal (LC) sensors generate complex optical images from molecular orientations, posing challenges for rapid analysis.
    • Traditional methods for LC optical image analysis are time-consuming and may lack precision.
    • Deep learning (DL) offers potential for advanced image analysis in scientific sensing applications.

    Purpose of the Study:

    • To utilize the VGG16 deep learning model for accelerating the analysis of liquid crystal optical images.
    • To enhance the speed and sensitivity of liquid crystal-based sensing applications.
    • To enable visualized and precise quantification of analytes using LC optical images.

    Main Methods:

    • Implementation of the VGG16 deep learning model for analyzing diverse optical textures from liquid crystal sensors.
    • Training the DL model to classify and quantify analytes based on LC optical images.
    • Comparison of DL-based analysis with traditional gray scale intensity quantification.

    Main Results:

    • Achieved a classification accuracy of 0.9113 within 30 seconds for detecting surfactants cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS).
    • Reduced average relative errors to 3.54% and 7.94% for quantitative sensing of insulin-specific aptamer and insulin, respectively.
    • Decreased sensing time from 300 seconds to 90 seconds for insulin recognition and concentration detection.

    Conclusions:

    • The VGG16 deep learning model significantly accelerates liquid crystal sensor analysis, improving both speed and accuracy.
    • DL provides a powerful analytical tool for precise, visualized sensing applications based on optical image analysis.
    • This approach demonstrates enhanced performance in detecting surfactants and quantifying biomolecules like insulin.