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 物理 地质学 光学 海洋学 生物化学
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:47
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).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
Xin发布了新的文献求助10
2秒前
天天快乐应助王者采纳,获得10
2秒前
烂漫念柏发布了新的文献求助10
2秒前
2秒前
李健的小迷弟应助黄桂川采纳,获得10
3秒前
cynthia完成签到,获得积分10
4秒前
邵泉颖完成签到,获得积分10
4秒前
任柯岩发布了新的文献求助10
4秒前
地瓜儿完成签到,获得积分10
4秒前
科研通AI6.3应助Yingqian_Zhang采纳,获得10
5秒前
6秒前
best完成签到,获得积分10
8秒前
8秒前
8秒前
xxm发布了新的文献求助10
9秒前
Jacqueline777完成签到,获得积分10
9秒前
Zllu发布了新的文献求助20
11秒前
Spy_R完成签到,获得积分10
11秒前
官捷完成签到,获得积分20
11秒前
best发布了新的文献求助10
11秒前
隐形曼青应助科研通管家采纳,获得10
12秒前
CodeCraft应助科研通管家采纳,获得10
12秒前
赘婿应助科研通管家采纳,获得10
12秒前
顾矜应助科研通管家采纳,获得10
12秒前
ding应助科研通管家采纳,获得10
12秒前
汉堡包应助xxm采纳,获得10
12秒前
千空应助科研通管家采纳,获得10
12秒前
12秒前
浮游应助科研通管家采纳,获得10
12秒前
上官若男应助科研通管家采纳,获得10
12秒前
Jasper应助科研通管家采纳,获得10
12秒前
充电宝应助科研通管家采纳,获得10
12秒前
酷波er应助科研通管家采纳,获得10
12秒前
慕青应助科研通管家采纳,获得10
13秒前
千空应助科研通管家采纳,获得10
13秒前
星辰大海应助科研通管家采纳,获得20
13秒前
科研通AI2S应助科研通管家采纳,获得10
13秒前
今后应助科研通管家采纳,获得30
13秒前
FashionBoy应助子奇采纳,获得10
14秒前
高分求助中
Inorganic Chemistry Eighth Edition 1200
Standards for Molecular Testing for Red Cell, Platelet, and Neutrophil Antigens, 7th edition 1000
HANDBOOK OF CHEMISTRY AND PHYSICS 106th edition 1000
ASPEN Adult Nutrition Support Core Curriculum, Fourth Edition 1000
The Psychological Quest for Meaning 800
Signals, Systems, and Signal Processing 610
脑电大模型与情感脑机接口研究--郑伟龙 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6301797
求助须知:如何正确求助?哪些是违规求助? 8118792
关于积分的说明 16999807
捐赠科研通 5362194
什么是DOI,文献DOI怎么找? 2848060
邀请新用户注册赠送积分活动 1825650
关于科研通互助平台的介绍 1679637