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Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor
Published on: February 16, 2018
Selective electrochemical detection of cannabidiol (CBD) and tetrahydrocannabinol (THC) at molecular-imprinted
Watinee Nunthakitgoson1, Natthaphong Kaewwan1, Sopon Butcha2
1School of Energy Science and Engineering, Vidyasirimedhi Institute of Science and Technology, 21210, Rayong, Thailand.
Abstract:
In this work, we present the elaboration and application of a novel electrochemical sensor based on molecular-imprinted mesoporous Pt-Ir alloys for the selective detection of cannabinoids, including cannabidiol (CBD) and tetrahydrocannabinol (THC). The electrodes are obtained by metal electrodeposition in the simultaneous presence of CBD or THC-models and a lyotropic liquid crystal of a non-ionic surfactant as molecular and supramolecular templates, respectively. This biomimetic approach replicates natural molecular recognition by employing molecularly and supramolecularly imprinted mesoporous metal surfaces, leading to enhanced selectivity and sensitivity in electrochemical detection, enabling discrimination between CBD and THC-models with distinct oxidation potentials. Differential pulse voltammetry (DPV) reveals detection limits as low as 1.2 and 1.7 μM for CBD and THC model, respectively, within a linear range of 10-30 μM. Additionally, the sensors maintain excellent performance in real cannabis oil samples and are reusable with minimal loss of sensitivity. These findings open up new perspectives for the development of molecular-imprinted metal electrodes as an interesting alternative to traditional chromatographic methods. Although this study demonstrates the sensor's performance in cannabis oil samples, it is expected that more complex matrices (e.g., plant materials or biological fluids) would require preliminary extraction steps prior to analysis. Nonetheless, the platform shows significant potential for rapid and precise cannabinoid detection for pharmaceutical and quality control applications.

