| Title: | Construction of High-Active SERS Cavities in a TiO(2) Nanochannels-Based Membrane: A Selective Device for Identifying Volatile Aldehyde Biomarkers |
| Author(s): | Xu J; Xu Y; Li J; Zhao J; Jian X; Xu J; Gao Z; Song YY; |
| Address: | "College of Science, Northeastern University, Shenyang 110819, China" |
| DOI: | 10.1021/acssensors.3c01061 |
| ISSN/ISBN: | 2379-3694 (Electronic) 2379-3694 (Linking) |
| Abstract: | "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 TiO(2) nanochannel membrane (Au/TiO(2) NM) as the substrate, a ZIF-8-covered Au/TiO(2) NM SERS sensing substrate is designed for the detection of exhaled volatile organic compounds (VOCs). Au/TiO(2) 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)" |
| Keywords: | Humans Aldehydes Gold *Metal Nanoparticles Biomarkers Gases *Lung Neoplasms/diagnosis exhaled volatile organic compounds machine learning nanochannel membrane reactive cavities surface-enhanced Raman scattering; |
| Notes: | "MedlineXu, Jing Xu, Ying Li, Junhan Zhao, Junjian Jian, Xiaoxia Xu, Jingwen Gao, Zhida Song, Yan-Yan eng Research Support, Non-U.S. Gov't 2023/08/29 ACS Sens. 2023 Sep 22; 8(9):3487-3497. doi: 10.1021/acssensors.3c01061. Epub 2023 Aug 29" |
