LSPR-susceptible metasurface platform for spectrometer-less and AI-empowered diagnostic biomolecule detection

被引:1
|
作者
Li, Jinke [1 ,2 ]
Kim, Jin Tae [3 ]
Li, Hongliang [1 ,2 ]
Cho, Hyo-Young [4 ]
Kim, Jin-Soo [5 ]
Choi, Duk-Yong [6 ]
Wang, Chenxi [1 ,2 ]
Lee, Sang-Shin [1 ,2 ]
机构
[1] Kwangwoon Univ, Dept Elect Engn, Seoul 01897, South Korea
[2] Kwangwoon Univ, Nano Device Applicat Ctr, Seoul 01897, South Korea
[3] Elect & Telecommun Res Inst, Quantum Technol Res Dept, Daejeon 34129, South Korea
[4] Elect & Telecommun Res Inst, Digital Biomed Res Div, Daejeon 34129, South Korea
[5] Korea Univ, Dept Phys, Nano Opt Lab, Seoul 02841, South Korea
[6] Australian Natl Univ, Res Sch Phys, Dept Quantum Sci & Technol, Canberra, ACT 2601, Australia
基金
新加坡国家研究基金会;
关键词
Artificial intelligence; Diagnostic biomolecule detection; Gold nanoparticle; Localized surface plasmon resonance; Metasurface; C-REACTIVE PROTEIN; SIZE-DISTRIBUTION; NANOPARTICLES; BIOSENSORS;
D O I
10.1016/j.aca.2024.343094
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In response to the growing demand for biomolecular diagnostics, metasurface (MS) platforms based on high-Q resonators have demonstrated their capability to detect analytes with smart data processing and image analysis technologies. However, high-Q resonator meta-atom arrays are highly sensitive to the fabrication process and chemical surface functionalization. Thus, spectrum scanning systems are required to monitor the resonant wavelength changes at every step, from fabrication to practical sensing. In this study, we propose an innovative dielectric resonator-independent MS platform that enables spectrometer-less biomolecule detection using artificial intelligence (AI) at a visible wavelength. Functionalizing the focused vortex MS to capture gold nanoparticle (AuNP)-based sandwich immunoassays causes the resulting vortex beam profiles to be significantly affected by the localized surface plasmon resonance (LSPR) occurring between AuNPs and meta-atoms. The convolutional neural network algorithm was carefully trained to accurately classify the AuNP concentration- dependent focused vortex beam, facilitating the determination of the concentration of the targeted diagnostic biomolecule. Successful in situ identification of various biomolecule concentrations was achieved with over 99 % accuracy, indicating the potential of combining an LSPR-susceptible MS platform and AI for continuously tracking various chemical and biological compounds.
引用
收藏
页数:9
相关论文
empty
未找到相关数据