Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing

التفاصيل البيبلوغرافية
العنوان: Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing
المؤلفون: Hakho Lee, Jungmo Ahn, Sang-Kee Choi, Eunha Kim, Kyoungha Min, Hyungi Kim, Changgi Hong, Hyungsoon Im, Hojeong Yu, JeongGil Ko, Sanghee Lee, Ik-Soo Shin
المصدر: Sens Actuators B Chem
بيانات النشر: Elsevier BV, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Materials science, 02 engineering and technology, 010402 general chemistry, medicine.disease_cause, 01 natural sciences, Multiplexing, Article, Light source, Materials Chemistry, medicine, Electrical and Electronic Engineering, Instrumentation, Point of care, business.industry, Metals and Alloys, Heavy metals, 021001 nanoscience & nanotechnology, Condensed Matter Physics, Fluorescence, 0104 chemical sciences, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials, Optoelectronics, 0210 nano-technology, business, Sensing system, Ultraviolet
الوصف: Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds. Using an indolizine structure as a fluorescent core skeleton, named Kaleidolizine (KIz), a library of 75 different fluorescent KIz derivatives were designed and synthesized. These KIz derivatives are simultaneously excited by a single ultraviolet (UV) light source and emit diverse fluorescence colors and intensities. For multiplexed POC sensing system, fluorescent compounds array on cellulose paper was prepared and the pattern of fluorescence changes of KIz on array were specific to target chemicals adsorbed on that paper. Furthermore, we developed a machine-learning algorithm for automated, rapid analysis of color and intensity changes of individual sensing arrays. We showed that the paper sensor arrays could differentiate 35 different volatile organic compounds using a smartphone-based handheld detection system. Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97% in the VOC detection. The highly multiplexed paper sensor could have favorable applications for monitoring a broad-range of environmental toxins, heavy metals, explosives, pathogens.
تدمد: 0925-4005
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a8e0ceb1709a2907455f57d777dbec2
https://doi.org/10.1016/j.snb.2020.129248
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....2a8e0ceb1709a2907455f57d777dbec2
قاعدة البيانات: OpenAIRE