Silver chalcogenide loaded V2CTx MXene-molecularly imprinted polymer-based novel ratiometric sensor for the early predictive cancer marker: L-Fucose

化学 分子印迹聚合物 色谱法 核化学 选择性 材料科学 生物化学 催化作用
作者
Sathish Panneer Selvam,Sungbo Cho
出处
期刊:Chemical Engineering Journal [Elsevier]
卷期号:469: 144016-144016 被引量:17
标识
DOI:10.1016/j.cej.2023.144016
摘要

Fucosylated-haptoglobin (Hpt) and urinary free L-fucose (FU) are primary markers in the progression of tumor and metastasis-related to cancer of the liver (Hepatocellular carcinoma), pancreatic, prostate, colon and oral cancer. The p-aminobenzoic acid (PABA)-FU containing molecularly imprinted polymer (MIP) cavities were accessed to detect FU molecules with signal amplifying layer of novel silver selenide (Ag2Se) doped vanadium carbide (V2CTx) MXene. The selection of suitable monomers through theoretical calculations and optimization of factors involved in the fabrication of a MIP-based sensor via Taguchi orthogonal array (5 × 5) was abundant in improvising the sensor performance. Ratiometry of the oxidation response of FU (IFU) and Ag2Se@V2CTx (IM) played a vital role in eliminating the interference of serum proteins which includes Hpt (undigested), thus sample pretreatment is not required. The extraordinary selectivity with remarkable LOD (1.85 μM) was achieved for FU detection in 100 mM PBS (pH = 6.0) and structurally mimicking interferents failed to show an impactful response against the sensor. Urinary-free FU (spiked) and serum FU (unspiked and spiked) were analyzed in which the LOD of urinary-free FU was as low as 2.22 μM. In addition, normal human serum showed 440.2 μM of FU and the %recovery of FU-spiked samples was between 98.0 and 101.5%.
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
new发布了新的文献求助10
1秒前
刘澳发布了新的文献求助10
1秒前
SciGPT应助灵ling采纳,获得10
1秒前
2秒前
汤圆发布了新的文献求助10
2秒前
完美世界应助阿拉采纳,获得10
3秒前
二萌发布了新的文献求助10
3秒前
4秒前
4秒前
轨迹应助远_09采纳,获得20
5秒前
科研通AI2S应助无辜的醉波采纳,获得10
5秒前
tejing1158发布了新的文献求助10
6秒前
星鱼发布了新的文献求助20
6秒前
英俊的铭应助家欣采纳,获得10
7秒前
7秒前
Ava应助Lionnn采纳,获得10
8秒前
傲娇而又骄傲完成签到 ,获得积分10
8秒前
科研通AI6.2应助Amanda采纳,获得30
9秒前
小蚊子完成签到,获得积分10
9秒前
10秒前
10秒前
jijijibibibi完成签到,获得积分10
11秒前
kl完成签到,获得积分10
12秒前
12秒前
CodeCraft应助医学小牛马采纳,获得10
13秒前
沐啊完成签到 ,获得积分10
14秒前
14秒前
14秒前
CodeCraft应助汤圆采纳,获得10
14秒前
14秒前
本草石之寒温完成签到 ,获得积分10
15秒前
Lpyyy发布了新的文献求助10
15秒前
16秒前
Shin完成签到,获得积分20
18秒前
18秒前
19秒前
勤恳浩然发布了新的文献求助30
20秒前
20秒前
安静诗柳完成签到,获得积分10
21秒前
后巷的知识份子完成签到,获得积分10
21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
Kinesiophobia : a new view of chronic pain behavior 2000
Research for Social Workers 1000
Mastering New Drug Applications: A Step-by-Step Guide (Mastering the FDA Approval Process Book 1) 800
The Social Psychology of Citizenship 600
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5912187
求助须知:如何正确求助?哪些是违规求助? 6831436
关于积分的说明 15785215
捐赠科研通 5037204
什么是DOI,文献DOI怎么找? 2711599
邀请新用户注册赠送积分活动 1661950
关于科研通互助平台的介绍 1603905