Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/226831
Title: Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for High-Throughput Screening of Cannabidiol
Authors: Yap, Stephanie Hui Kit 
Pan, Jieming 
Dao, Viet Linh 
Zhang, Xiangyu 
Wang, Xinghua 
Teo, Wei Zhe 
Zamburg, Evgeny 
Tham, Chen-Khong 
Yew, Wen Shan 
Poh, Chueh Loo 
Thean, Aaron Voon-Yew 
Keywords: Science & Technology
Physical Sciences
Technology
Chemistry, Multidisciplinary
Chemistry, Physical
Nanoscience & Nanotechnology
Materials Science, Multidisciplinary
Physics, Applied
Physics, Condensed Matter
Chemistry
Science & Technology - Other Topics
Materials Science
Physics
aptamers
biosensing
cannabidiol
interdigitated electrodes
physics-guided machine learning
Issue Date: 6-May-2022
Publisher: WILEY-V C H VERLAG GMBH
Citation: Yap, Stephanie Hui Kit, Pan, Jieming, Dao, Viet Linh, Zhang, Xiangyu, Wang, Xinghua, Teo, Wei Zhe, Zamburg, Evgeny, Tham, Chen-Khong, Yew, Wen Shan, Poh, Chueh Loo, Thean, Aaron Voon-Yew (2022-05-06). Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for High-Throughput Screening of Cannabidiol. SMALL 18 (22). ScholarBank@NUS Repository.
Abstract: The recent legalization of cannabidiol (CBD) to treat neurological conditions such as epilepsy has sparked rising interest across global pharmaceuticals and synthetic biology industries to engineer microbes for sustainable synthetic production of medicinal CBD. Since the process involves screening large amounts of samples, the main challenge is often associated with the conventional screening platform that is time consuming, and laborious with high operating costs. Here, a portable, high-throughput Aptamer-based BioSenSing System (ABS3) is introduced for label-free, low-cost, fully automated, and highly accurate CBD concentrations’ classification in a complex biological environment. The ABS3 comprises an array of interdigitated microelectrode sensors, each functionalized with different engineered aptamers. To further empower the functionality of the ABS3, unique electrochemical features from each sensor are synergized using physics-guided multidimensional analysis. The capabilities of this ABS3 are demonstrated by achieving excellent CBD concentrations’ classification with a high prediction accuracy of 99.98% and a fast testing time of 22 µs per testing sample using the optimized random forest (RF) model. It is foreseen that this approach will be the key to the realistic transformation from fundamental research to system miniaturization for diagnostics of disease biomarkers and drug development in the field of chemical/bioanalytics.
Source Title: SMALL
URI: https://scholarbank.nus.edu.sg/handle/10635/226831
ISSN: 1613-6810
1613-6829
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