Tuning Hydrophobicity of Paper Substrates for Effective Colorimetric detection of Glucose and Nucleic acids.

This study investigated the colorimetric response of standard glucose, serum glucose, and nucleic acid assays on various paper surfaces with different wettability, including hydrophilic, hydrophobic, and nearly superhydrophobic surfaces. Water contact angles (WCA) formed by water droplets on each surface were measured using ImageJ software. The hydrophilic surface showed no contact angle, while the hydrophobic and nearly superhydrophobic surfaces exhibited contact angles of 115.667° and 133.933°, respectively. The colorimetric sensitivity of the standard glucose assay was analyzed on these surfaces, revealing enhanced sensitivity on the nearly superhydrophobic surface due to the high molecular crowding effect owing to its non-wetting behavior and eventually confined reaction product at the sample loading zone. The hydrophobic nature of the surface restricts the spreading and diffusion of the reaction product, leading to a controlled and localized concentration of the assay product leading to moderate colorimetric intensity. On the other hand, the hydrophilic surface showed the least enhancement in colorimetric sensitivity; this is attributed to the high wettability of the hydrophilic surface causing the reaction product to spread extensively, resulting in a larger area of dispersion and consequently a lower colorimetric intensity. The measured limit of detection (LOD) for nucleic acid on nearly superhydrophobic surfaces was found to be 16.15 ng/µL, which was almost four-fold lower than on hydrophilic surfaces (60.08 ng/µL). Additionally, the LODs of standard glucose and clinical serum samples were two-fold lower on nearly superhydrophobic surfaces compared to hydrophilic surfaces. Our findings clearly highlight the promising potential of utilizing superhydrophobic surfaces to significantly enhance colorimetric sensitivity in paper-based diagnostic applications. This innovative approach holds promise for advancing point-of-care diagnostics and improving disease detection in resource-limited settings.
(© 2023. The Author(s).)