清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Graphene and metal–organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

材料科学 纳米技术 石墨烯 生物标志物 计算机科学 化学 生物化学
作者
Anh Tuan Trong Tran,Kamrul Hassan,Trần Thanh Tùng,Ashis Tripathy,Ashok Mondal,Dušan Lošić
出处
期刊:Nanoscale [Royal Society of Chemistry]
卷期号:16 (18): 9084-9095 被引量:6
标识
DOI:10.1039/d4nr00174e
摘要

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
量子星尘发布了新的文献求助10
4秒前
航行天下完成签到 ,获得积分10
8秒前
xianyaoz完成签到 ,获得积分0
11秒前
脑洞疼应助科研通管家采纳,获得30
11秒前
CodeCraft应助科研通管家采纳,获得10
11秒前
柯伊达完成签到 ,获得积分10
16秒前
顺鑫完成签到 ,获得积分10
16秒前
slayers完成签到 ,获得积分10
22秒前
柯一一应助红箭烟雨采纳,获得10
25秒前
27秒前
28秒前
宇文雨文完成签到 ,获得积分10
32秒前
简单发布了新的文献求助10
32秒前
zjq完成签到 ,获得积分10
34秒前
桐桐应助amin采纳,获得10
46秒前
lifenghou完成签到 ,获得积分10
47秒前
50秒前
51秒前
今夕何夕发布了新的文献求助10
55秒前
58秒前
波波波波波6764完成签到 ,获得积分10
1分钟前
amin发布了新的文献求助10
1分钟前
tyro完成签到,获得积分10
1分钟前
husky完成签到,获得积分10
1分钟前
量子星尘发布了新的文献求助10
1分钟前
红箭烟雨完成签到,获得积分10
1分钟前
aldehyde应助刘家小姐姐采纳,获得10
1分钟前
32429606完成签到 ,获得积分10
1分钟前
orchid完成签到,获得积分10
1分钟前
Ray完成签到 ,获得积分10
1分钟前
虞无声完成签到,获得积分10
1分钟前
amin完成签到,获得积分10
1分钟前
1分钟前
1分钟前
香香丿完成签到 ,获得积分10
2分钟前
阿连完成签到,获得积分10
2分钟前
丘比特应助WRX采纳,获得10
2分钟前
黄花完成签到 ,获得积分10
2分钟前
江南春完成签到 ,获得积分10
2分钟前
量子星尘发布了新的文献求助10
2分钟前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 1000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 310
宽量程高线性度柔性压力传感器的逆向设计 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3980994
求助须知:如何正确求助?哪些是违规求助? 3524672
关于积分的说明 11222589
捐赠科研通 3262273
什么是DOI,文献DOI怎么找? 1801153
邀请新用户注册赠送积分活动 879609
科研通“疑难数据库(出版商)”最低求助积分说明 807449