Evidence map›Paper›PMID 35454502›Full record

ArticleMaterials (Basel, Switzerland)2022

Electrochemical Self-Assembled Gold Nanoparticle SERS Substrate Coupled with Diazotization for Sensitive Detection of Nitrite.

En Han, Maoni Zhang, Yingying Pan, Jianrong Cai

Open access · goldAbstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

En HanSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0001-6395-3560
Maoni ZhangSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Yingying PanSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Jianrong CaiSchool of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Jiangsu University · CN

Funding

China Agriculture Research System of MOF and MARA CARS-27-01ANational Natural Science Foundation of China 51975259Natural Science Foundation of Jiangsu University 12JDG038
6 · The paper itself

Abstract

The accurate determination of nitrite in food samples is of great significance for ensuring people's health and safety. Herein, a rapid and low-cost detection method was developed for highly sensitive and selective detection of nitrite based on a surface-enhanced Raman scattering (SERS) sensor combined with electrochemical technology and diazo reaction. In this work, a gold nanoparticle (AuNP)/indium tin oxide (ITO) chip as a superior SERS substrate was obtained by electrochemical self-assembled AuNPs on ITO with the advantages of good uniformity, high reproducibility, and long-time stability. The azo compounds generated from the diazotization-coupling reaction between nitrite, 4-aminothiophenol (4-ATP), and N-(1-naphthyl) ethylenediamine dihydrochloride (NED) in acid condition were further assembled on the surface of AuNP/ITO. The detection of nitrite was realized using a portable Raman spectrometer based on the significant SERS enhancement of azo compounds assembled on the AuNP/ITO chip. Many experimental conditions were optimized such as the time of electrochemical self-assembly and the concentration of HAuCl

Indexed as

diazo reactionelectrochemical depositionnitriteself-assembled AuNPssurface-enhanced Raman scattering (SERS)

Identifiers

PMID35454502
PMCPMC9028913
OpenAlexW4223458577

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.