Multiscale Photonic Crystal Enhanced Core-Shell Plasmonic Nanomaterial for Rapid Vapor-Phase Detection of Explosives

ACS APPLIED NANO MATERIALS(2020)

引用 13|浏览33
暂无评分
摘要
Surface-enhanced Raman scattering (SERS) has started to attract attention in vapor sensing; however, practical applications require shorter response time and better sensitivity. Herein, we report a facile multiscale SERS substrate for trace-level detection of vapors using a portable Raman spectrometer through the synergistic integration of biologically fabricated diatom photonic crystals and gold-silica core-shell nanoparticles. The multiscale substrate is composed of (1) a micrometer-scaled, 3-dimensional, diatom biosilica frustule enabling efficient vaporsubstrate interaction for rapid sensing, (2) periodic pores, on the order of 100 nm, inducing plasmonic-photonic coupled resonances for enhanced SERS signals, (3) gold nanoparticle cores, with a diameter on the order of 10 nm, contributing plasmonic field enhancements, and (4) porous 1 nm thick silica core-shells enabling analyte vapor adsorption and concentration. The combination of the hierarchal, multiscale features results in a SERS substrate capable of rapid and sensitive detection of target vapors in air. The multiscale substrates functionality is characterized using the polycyclic aromatic hydrocarbon pyrene, and the contribution from each scale is verified by using a stagnant vapor chamber. The sensor equilibrates in only 3 min, and detection is achieved down to 1 ppm. The sensor is then applied to the detection of explosive 2,4-dinitrotoluene vapor below 100 ppb in an airflow chamber to replicate practical detection conditions, achieving detection in under 3 min at room temperature and under 1 min when heated. This work successfully demonstrates detection of explosive vapor and represents a significant advancement toward widespread vapor sensing via SERS.
更多
查看译文
关键词
multiscale nanomaterial,photonic crystals,core-shell nanoparticles,explosives detection,surface-enhanced Raman scattering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要