A Diffusion-Driven Chromogenic Reagent-Free Viscosity-Based Sensor with Dual Signals for the Visual Detection of Hyaluronidase

Point-of-care testing (POCT) requires simple and cost-effective strategies to enhance reliability and practicality. Here, a state-of-the-art, dual-mode viscosity-based sensor (DM vis-sensor) is developed by leveraging the diffusion-driven viscosity differentiation on the mesoporous membrane. The rapid and visual detection of hyaluronidase (HAase) is demonstrated as a model. The sensor exploits the enzymatic cleavage of hyaluronic acid (HA) by HAase, which reduces solution viscosity, thereby altering the solution diffusion pattern on the mesoporous membrane. High-viscosity HA solutions form small and dark diffusion spots with short diameters and low grayscale ratio (<i>G</i><sub>R</sub>) values, while HAase-treated HA solutions yield large and light diffusion spots with long diameters and high <i>G</i><sub>R</sub> values. This dual-mode sensor, which combines distance and color readouts, enables intuitive and instrument-free detection of HAase without the need of chromogenic agents. It demonstrates high sensitivity to HAase, with detection limits of 0.12 U/mL (distance-based readout) and 0.16 U/mL (grayscale-based readout). Additionally, it is successfully validated through the quantification of HAase in human urine samples. Notably, its simplicity, low cost, and dual-readout reliability make it a promising POCT tool for early diagnosis and monitoring, especially in resource-limited settings. Therefore, this work highlights the potential of the development of dual-mode viscosity-based sensors for POCT applications.

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Publication Details

Journal
ACS Sensors
Published
2026-02-24
DOI
https://doi.org/10.1021/acssensors.6c00293
Primary Topic
Liquid Crystal Research Advancements
Type
article
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article

A Diffusion-Driven Chromogenic Reagent-Free Viscosity-Based Sensor with Dual Signals for the Visual Detection of Hyaluronidase

Fangchao Yin, Yanhui Bi, Wenli Wu, Zheng Wang et al.
ACS Sensors
Liquid Crystal Research Advancements
article

A Diffusion-Driven Chromogenic Reagent-Free Viscosity-Based Sensor with Dual Signals for the Visual Detection of Hyaluronidase

Fangchao Yin, Yanhui Bi, Wenli Wu, Zheng Wang, Jun Lin, Mei Zhao, Ranran Cheng, Qiongzheng Hu
article en

Abstract

Point-of-care testing (POCT) requires simple and cost-effective strategies to enhance reliability and practicality. Here, a state-of-the-art, dual-mode viscosity-based sensor (DM vis-sensor) is developed by leveraging the diffusion-driven viscosity differentiation on the mesoporous membrane. The rapid and visual detection of hyaluronidase (HAase) is demonstrated as a model. The sensor exploits the enzymatic cleavage of hyaluronic acid (HA) by HAase, which reduces solution viscosity, thereby altering the solution diffusion pattern on the mesoporous membrane. High-viscosity HA solutions form small and dark diffusion spots with short diameters and low grayscale ratio (<i>G</i><sub>R</sub>) values, while HAase-treated HA solutions yield large and light diffusion spots with long diameters and high <i>G</i><sub>R</sub> values. This dual-mode sensor, which combines distance and color readouts, enables intuitive and instrument-free detection of HAase without the need of chromogenic agents. It demonstrates high sensitivity to HAase, with detection limits of 0.12 U/mL (distance-based readout) and 0.16 U/mL (grayscale-based readout). Additionally, it is successfully validated through the quantification of HAase in human urine samples. Notably, its simplicity, low cost, and dual-readout reliability make it a promising POCT tool for early diagnosis and monitoring, especially in resource-limited settings. Therefore, this work highlights the potential of the development of dual-mode viscosity-based sensors for POCT applications.

ACS SensorsVol. 11(3)
Seoul National University (KR), Qilu University of Technology (CN), Bioanalytica (Switzerland) (CH), Qilu Normal University (CN), Shandong Academy of Sciences (CN), Tsinghua University (CN)
Openalex Percentile: Top 47%
Liquid Crystal Research Advancements
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