Enhanced detection of glucose with carbon quantum dot-modified copper oxide: Computational insight and machine learning modeling of electrochemical sensing

量子点 碳量子点 电化学 氧化铜 氧化物 材料科学 纳米技术 碳纤维 化学 冶金 电极 复合材料 物理化学 复合数
作者
Naeem Ullah khan,B.P. Sharma,Sadam Hussain Tumrani,Mehvish Zahoor,Razium Ali Soomro,Tarık Küçükdeniz,Selcan Karakuş,Eman Ramadan Elsharkawy,Jun Lu,Salah M. El‐Bahy,Zeinhom M. El‐Bahy
出处
期刊:Microchemical Journal [Elsevier BV]
卷期号:204: 110936-110936
标识
DOI:10.1016/j.microc.2024.110936
摘要

Poor conductivity and surface passivation pose critical challenges in metal oxide structures during their application for non-enzymatic oxidation. To address this, we systematically employed in-situ deposition of carbon-quantum dots (C-dots) over copper oxide (CuO), enhancing its electrocatalytic properties for direct non-enzymatic glucose oxidation in alkaline media. The process involved the systematic deposition of varying wt.% of C-dots onto the CuO nanostructure. The electrode's sensing capability was assessed through CV, DPV, and amperometric measurements, evaluating its suitability in high (0.1 to 0.85 mM) and low glucose concentration levels (15 to 225 nM) with a representative LOD of 1.4 nM (17142.86 µA mM−1 cm−2). Additionally, the CuO-Cdot-16.6 protective coating allowed for long-term working capability, with chronoamperometric measurement confirming a 99 % current retention ability compared to pristine CuO's 39 % retention during 3500 s of continuous measurement. DFT calculations further confirmed the efficacy of CuO substrate as a scaffold for glucose adsorption. The stable CuO-glucose complex formed due to energetically favorable conditions further strengthens its potential as a sensor. Successful recoveries of spiked glucose serum samples validated the sensor's practical usage in complex matrices. Moreover, Machine learning was also adopted to validate the accuracy of glucose detection, where artificial neural network (ANN) model emerged as a suitable model to interpret the DPV derived data relationships, adding in sensor working capability and promising its future application in precision/intelligent healthcare.
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
孟德尔吃豌豆完成签到,获得积分10
刚刚
江宜完成签到 ,获得积分10
1秒前
2秒前
张岱帅z完成签到,获得积分10
2秒前
2秒前
sheep完成签到,获得积分10
2秒前
木子木子李完成签到,获得积分10
3秒前
景妙海完成签到 ,获得积分10
4秒前
心心完成签到,获得积分10
4秒前
CY03完成签到,获得积分10
6秒前
taoze完成签到,获得积分10
6秒前
张小度ever完成签到 ,获得积分10
7秒前
hutian完成签到,获得积分10
7秒前
大姿兰卡眼睛完成签到 ,获得积分10
8秒前
Sky完成签到,获得积分10
9秒前
静静子完成签到,获得积分10
9秒前
随安完成签到,获得积分20
9秒前
大意的雨双完成签到 ,获得积分10
9秒前
Dr.Shan完成签到,获得积分10
9秒前
JIASHOUSHOU完成签到,获得积分10
10秒前
一颗煤炭完成签到 ,获得积分10
10秒前
Cipher完成签到 ,获得积分10
11秒前
脑洞疼应助LIUYONG采纳,获得10
11秒前
可乐完成签到,获得积分10
11秒前
12秒前
koukousang完成签到,获得积分10
13秒前
乐乐应助张无缺采纳,获得10
14秒前
三三完成签到,获得积分10
14秒前
平常荷花完成签到 ,获得积分10
16秒前
量子星尘发布了新的文献求助10
17秒前
阿胡发布了新的文献求助30
17秒前
水晶李完成签到 ,获得积分10
18秒前
脑洞疼应助静静子采纳,获得100
21秒前
新年好完成签到,获得积分10
22秒前
爆米花应助Michelle采纳,获得10
25秒前
25秒前
LYSM应助晴栀采纳,获得10
26秒前
Bean完成签到,获得积分10
26秒前
26秒前
26秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
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 4038388
求助须知:如何正确求助?哪些是违规求助? 3576106
关于积分的说明 11374447
捐赠科研通 3305798
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029