Construction of High-Active SERS Cavities in a TiO2 Nanochannels-Based Membrane: A Selective Device for Identifying Volatile Aldehyde Biomarkers

拉曼散射 分析物 拉曼光谱 纳米技术 基质(水族馆) 表面增强拉曼光谱 胶体金 材料科学 分子 纳米颗粒 气体分析呼吸 化学 色谱法 有机化学 生物化学 物理 海洋学 光学 地质学
作者
Jing Xu,Ying Xu,Junhan Li,Junjian Zhao,Xiaoxia Jian,Jingwen Xu,Zhida Gao,Yan‐Yan Song
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:8 (9): 3487-3497 被引量:8
标识
DOI:10.1021/acssensors.3c01061
摘要

The accurate, sensitive, and selective on-site screening of volatile aldehyde biomarkers for lung cancer is of utmost significance for preclinical cancer diagnosis and treatment. Applying surface-enhanced Raman scattering (SERS) for gas sensing remains difficult due to the small Raman cross section of most gaseous molecules and interference from other components in exhaled breath. Using an Au asymmetrically coated TiO2 nanochannel membrane (Au/TiO2 NM) as the substrate, a ZIF-8-covered Au/TiO2 NM SERS sensing substrate is designed for the detection of exhaled volatile organic compounds (VOCs). Au/TiO2 NM provides uniformly amplified Raman signals for trace measurements in this design. Importantly, the interfacial nanocavities between Au nanoparticles (NPs) and metal-organic frameworks (MOFs) served as gaseous confinement cavities, which is the key to enhancing the capture and adsorption ability toward gaseous analytes. Both ends of the membrane are left open, allowing gas molecules to pass through. This facilitates the diffusion of gaseous molecules and efficient capture of the target analyte. Using benzaldehyde as a typical gas marker model of lung cancer, the Schiff base reaction with a Raman-active probe molecule 4-aminothiophene (4-ATP) pregrafted on Au NPs enabled trace and multicomponent detection. Moreover, the combination of machine learning (ML) and Raman spectroscopy eliminates subjective assessments of gaseous aldehyde species with the use of a single feature peak, allowing for more accurate identification. This membrane sensing device offers a promising design for the development of a desktop SERS analysis system for lung cancer point-of-care testing (POCT).
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