Reversible Swell–Shrink Hydrogel Microspheres for High-Selectivity Digital SERS Analysis of Nonvolatile Fentanyl in Simulated Breath Aerosols

化学 选择性 膨胀 微球 色谱法 化学工程 有机化学 海洋学 地质学 工程类 催化作用
作者
Yuzhu Li,Zhongxiang Ding,Hongyan Wang,Cheng Qu,Guangping Li,Honglin Liu
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:97 (6): 3579-3588 被引量:4
标识
DOI:10.1021/acs.analchem.4c05999
摘要

In clinical diagnostics, human breath presents an alternative and more convenient sample than biofluids for detecting the ingestion of nonvolatile drugs. Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy technique with high sensitivity based on molecular fingerprinting. However, the low affinity of traditional SERS substrates for aerosols and the stochastic fluctuation of the SERS signal at low concentrations limit their application in breath aerosol analysis. In this study, we synthesized hydrogel microsphere SERS substrates with highly reversible swelling/shrinking properties that enhance target analyte accumulation in breath aerosols and promote plasmonic nanoparticle aggregation for intense Raman hotspot formation. Furthermore, these hydrogel microsphere SERS substrates function as a three-in-one system, enabling multilevel selectivity based on size, charge, and hydrophilicity for target molecules simultaneously without pretreatment. Notably, by "digitizing" the SERS signal of each individual hydrogel microsphere and calculating the proportion of positive microspheres, the hydrogel microspheres can serve as a digital SERS platform that circumvents the low stability issues resulting from fluctuations in SERS signal intensity. Consequently, the digital SERS platform achieved a detection limit of 0.5 ppm for fentanyl in simulated breath aerosols. This innovative sensing strategy not only demonstrates a promising approach for screening nonvolatile drugs but also simplifies the sampling process, holding great potential for clinical diagnosis of breath aerosols.
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
科研通AI2S应助张雅雅采纳,获得10
刚刚
Dr.Dream完成签到,获得积分10
刚刚
lulu发布了新的文献求助10
1秒前
王奕钦完成签到,获得积分10
1秒前
1秒前
悦耳念梦完成签到,获得积分10
1秒前
yizhi猫完成签到,获得积分10
1秒前
Zzyj完成签到 ,获得积分10
2秒前
科研通AI6.1应助accerue采纳,获得10
3秒前
3秒前
石头完成签到,获得积分10
4秒前
朴实如冰发布了新的文献求助10
4秒前
FashionBoy应助songym采纳,获得20
4秒前
科研通AI2S应助kll采纳,获得10
5秒前
SciGPT应助科研通管家采纳,获得10
6秒前
咕噜仔应助科研通管家采纳,获得10
6秒前
乐乐应助科研通管家采纳,获得10
6秒前
Orange应助科研通管家采纳,获得10
6秒前
完美世界应助科研通管家采纳,获得10
6秒前
英俊的铭应助科研通管家采纳,获得10
7秒前
7秒前
7秒前
7秒前
咕噜仔应助科研通管家采纳,获得10
7秒前
7秒前
7秒前
完美世界应助科研通管家采纳,获得10
7秒前
钱学森发布了新的文献求助10
7秒前
7秒前
打打应助科研通管家采纳,获得10
7秒前
7秒前
打打应助科研通管家采纳,获得10
7秒前
CodeCraft应助科研通管家采纳,获得10
7秒前
爆米花应助科研通管家采纳,获得10
7秒前
咕噜仔应助科研通管家采纳,获得10
7秒前
大模型应助科研通管家采纳,获得10
7秒前
我是老大应助科研通管家采纳,获得10
8秒前
zzc7应助科研通管家采纳,获得20
8秒前
结实晓蕾应助科研通管家采纳,获得50
8秒前
烟花应助科研通管家采纳,获得10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 生物化学 化学工程 物理 计算机科学 复合材料 内科学 催化作用 物理化学 光电子学 电极 冶金 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6022368
求助须知:如何正确求助?哪些是违规求助? 7641266
关于积分的说明 16169051
捐赠科研通 5170476
什么是DOI,文献DOI怎么找? 2766754
邀请新用户注册赠送积分活动 1750008
关于科研通互助平台的介绍 1636827