Evidence map›Paper›PMID 38138429›Full record

ArticleMolecules (Basel, Switzerland)2023

Ag@AuNP-Functionalized Capillary-Based SERS Sensing Platform for Interference-Free Detection of Glucose in Urine Using SERS Tags with Built-In Nitrile Signal.

Yanmei Si, Hua Wang, Yehao Yan, Bingwen Li, Zeyun Ni, Hongrui Shi

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Yanmei SiInstitute of Forensic Medicine and Laboratory Medicine, Jining Medical University, Jining 272067, China.
Hua WangSchool of Life Science, Huzhou University, Huzhou 313000, China.
Yehao YanSchool of Public Health, Jining Medical University, Jining 272067, China.
Bingwen LiShandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
Zeyun NiShandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
Hongrui ShiShandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
Dezhou University · CNJining Medical University · CNHuzhou University · CNInstitute of Biophysics · CN

Funding

National Natural Science Foundation of China 22006064National Natural Science Foundation of China 22074079Natural Science Foundation of Shandong Province ZR2021QB184
6 · The paper itself

Abstract

A Ag@AuNP-functionalized capillary-based surface-enhanced Raman scattering (SERS) sensing platform for the interference-free detection of glucose using SERS tags with a built-in nitrile signal has been proposed in this work. Capillary-based SERS capture substrates were prepared by connecting 4-mercaptophenylboronic acid (MBA) to the surface of the Ag@AuNP layer anchored on the inner wall of the capillaries. The SERS tags with a built-in interference-free signal could then be fixed onto the Ag@AuNP layer of the capillary-based capture substrate based on the distinguished feature of glucose, which can form a bidentate glucose-boronic complex. Thus, many "hot spots" were formed, which produced an improved SERS signal. The quantitative analysis of glucose levels was realized using the interference-free SERS intensity of nitrile at 2222 cm

Indexed as

GlucoseMetal NanoparticlesCapillariesGoldSpectrum Analysis, RamanGlucoseGoldAg@AuNPsfunctionalized capillaryglucoseinterference-free signalSERS sensing analysisSERS tags

Identifiers

PMID38138429
PMCPMC10745321
OpenAlexW4389341808

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.