Development of molecularly imprinted photonic crystal hydrogel based smart sensor for selective uric acid detection

• Published in: Microchemical journal Vol. 201; p. 110693
• Main Authors: Sanker S S, Sree, Thomas, Subin, Nalini, Savitha, Jacob, Dhanya P, V S, Suniya, Madhusoodanan, K N
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Molecular imprinting; Photonic crystal opal film; Uric acid; Photonic crystal opal film; Molecular imprinting; Uric acid
• ISSN: 0026-265X; 1095-9149
• DOI: 10.1016/j.microc.2024.110693

[Display omitted] •A molecularly imprinted photonic crystal hydrogel (MIPCH) based colourimetric sensor is developed for the selective and sensitive detection of uric acid (UA).•The rebinding of the UA analyte to the MIPCH-UA sensor causes the hydrogel to swell, leading to a significant redshift in its structural colour.•The change in structural colour serves as an easily observable indication of the sensor response.•The MIPCH-UA sensor exhibits a low detection limit and high level of selectivity for detecting UA molecules.•Real time application of the sensor is also investigated using biological fluids.•Employed CNN based deep learning algorithm to quantitatively predict the target molecule concentration. Determination of uric acid (UA) concentration is important in clinical analysis, as its variation is an indication of several health-related conditions. Here, we discuss the development of a Molecularly imprinted photonic crystal hydrogel (MICPH)-based colourimetric sensor for the selective detection of UA. The developed MIPCH-UA sensor displays a bright structural colour, which varies as the sensor swells upon rebinding the UA molecules. The structural colour change is directly related to the concentration of the target molecule and can serve as an easy readout of the sensor response. This colour change can also be recorded spectrally, providing a quantitative measure of the sensor response. With a low limit of detection (LoD) of 1.01×10-9M, the sensor is capable of detecting low concentrations of UA. Moreover, the sensor endows the advantage of fast responsiveness and reusability when employed for UA assays. The sensor performance remains consistently reliable over an extended duration of up to one month, thereby ensuring cost-effectiveness in monitoring UA levels. The specific detection capability of the sensor allows the accurate and reliable measurement of UA in the presence of various interfering molecules and complex sample matrices. The structural colour change of the sensor was used to train a CNN-based deep learning algorithm to quantitatively predict the UA concentration. The integration of the machine learning algorithm with the developed sensor provides an accurate and efficient smart sensor platform well-suited for real-time sensing of uric acid.