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<p>A high-performance ratiometric fluorescence pH sensor based on HPTS-IP/TiO<sub>2</sub>@THHM pH sensing film which contains a pH sensing layer and a scattering layer was developed. The pH sensing layer and scattering layer were fabricated by embedding HPTS-IP hydrophobic ion pairs and TiO<sub>2</sub> in TEOS-HTES organic/inorganic hybrid matrix (THHM), respectively. The HPTS-IP hydrophobic ion pairs were excited using pulsed signals from two LEDs with emission peaks of 404&#xa0;nm and 454&#xa0;nm, respectively. The fluorescence intensity ratio (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{I}_{454}/{I}_{404}\)</EquationSource> </InlineEquation>) was significantly correlated with pH and exhibited excellent performance within the pH range of 6.0 ~ 9.0. Based on fluorescence intensity, peak position, peak width and spectral shape characteristics, a support vector regression (SVR) model was established for predicting the pH value. The root mean square error (RMSE) of the validation dataset in leave-one-out cross-validation (LOOCV) method was 0.180 and the correlation coefficient (R) of the predicted pH versus experimental pH reached as high as 0.999. The sensor exhibited fast response with a response time of 27&#xa0;s, excellent reproducibility, reversibility and stability.</p>

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High-Performance Ratiometric Fluorescence pH Sensor Based on HPTS-IP/TiO2@THHM Sensing Film

  • 期刊论文

  • 发表日期：

    2025年5月17日

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  • Yaning Xue,
  • Jia Xiao,
  • Baohua Yue,
  • Liuming Yan

摘要

A high-performance ratiometric fluorescence pH sensor based on HPTS-IP/TiO2@THHM pH sensing film which contains a pH sensing layer and a scattering layer was developed. The pH sensing layer and scattering layer were fabricated by embedding HPTS-IP hydrophobic ion pairs and TiO2 in TEOS-HTES organic/inorganic hybrid matrix (THHM), respectively. The HPTS-IP hydrophobic ion pairs were excited using pulsed signals from two LEDs with emission peaks of 404 nm and 454 nm, respectively. The fluorescence intensity ratio ( \(\:{I}_{454}/{I}_{404}\) ) was significantly correlated with pH and exhibited excellent performance within the pH range of 6.0 ~ 9.0. Based on fluorescence intensity, peak position, peak width and spectral shape characteristics, a support vector regression (SVR) model was established for predicting the pH value. The root mean square error (RMSE) of the validation dataset in leave-one-out cross-validation (LOOCV) method was 0.180 and the correlation coefficient (R) of the predicted pH versus experimental pH reached as high as 0.999. The sensor exhibited fast response with a response time of 27 s, excellent reproducibility, reversibility and stability.