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Smartphone enabled upconversion nanoparticle-based lateral flow strip for ultra-low concentration of methamphetamine detection

稳健性(进化) 软件可移植性 计算机科学 重复性 灵敏度(控制系统) 检出限 检测点注意事项 材料科学 共发射极 纳米技术 光电子学 电子工程 化学 色谱法 工程类 生物化学 生物 免疫学 基因 程序设计语言
作者
Wei Wang,Zihan Ye,Xing Ma,Jinhong Guo
出处
期刊:Sensors and Actuators B-chemical [Elsevier]
卷期号:370: 132421-132421 被引量:12
标识
DOI:10.1016/j.snb.2022.132421
摘要

The combination of upconversion nanoparticles (UCNPs) and Lateral Flow Assays (LFAs) is becoming one of the most desirable quantitative assays for point-of-care testing (POCT) due to their excellent chemical and optical properties, which make them commercially viable as fast, sensitive and portable. We propose upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) combined with computer vision for the instantaneous detection of small molecule targets in a commercial medical device. The device overcomes the traditional lengthy pre-processing and ambiguous qualitative results, where computer vision algorithms improve the sensitivity and robustness of quantitative results. Compared with the standard quantitative testing process, the accuracy of its quantitative test results was improved by 17.4%. The device detected and reported the quantitative results of Methamphetamine (MET) concentration with the lower limit of detection (LOD) of 0.1 ng/ml in 20 s in field tests. Its success demonstrated good sensitivity, repeatability, and robustness. Most importantly, the entire device measures only 100 mm*120 mm*74 mm and weighs 351.2 g, combining portability and immediacy and providing quantitative data for small molecule targets at a lower concentration. This makes our system an extraordinarily creative and attractive approach, especially for applications in complex field environments with chaotic personnel and special emergencies. • A portable and ultra-sensitive up-conversion fluorescent probe-based commercial-type biosensor for in situ detection was designed; • A computer vision-based image denoising model was constructed to apply it to fluorescence intensity extraction, significantly improving specificity and sensitivity; • A waveform reconstruction method based on Gaussian function is proposed, and the sensitivity and robustness of the quantitative detection results of the target test substance are greatly improved by this innovative signal processing method; • A complete and pervasive IoT solution based on up-conversion nanoparticles detection is established.
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