Evidence map›Paper›PMID 35702427›Full record

ArticleRSC advances2022

Facile one-pot method of AuNPs/PEDOT/CNT composites for simultaneous detection of dopamine with a high concentration of ascorbic acid and uric acid.

Shaohua Chen, Wenliang Chen, Yihua Wang, Xiufang Wang, Yi Ding, Donglin Zhao, Jiyu Liu

Abstract read
In one paragraph

Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

7 authors.

Shaohua ChenAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.ORCID https://orcid.org/0000-0001-7461-8354
Wenliang ChenAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.
Yihua WangAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.
Xiufang WangAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.ORCID https://orcid.org/0000-0002-9614-8477
Yi DingAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.
Donglin ZhaoAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.
Jiyu LiuAnhui Key Laboratory of Advanced Building Materials, Anhui Jianzhu University Hefei 230022 Anhui Province P. R. China chshaohua@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this research, a facile one-pot method was used to synthesize gold/poly-3,4-ethylene-dioxythiophene/carbon nanotube (AuNPs/PEDOT/CNTs) composite material. The composite material was investigated by Fourier Transform Infrared spectroscopy (FTIR), X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). Then the synthesized nanocomposite material was dropped on a bare glassy carbon electrode (GCE) to improve the detection performance of dopamine with a high concentration of ascorbic acid and uric acid. The electrochemical behavior of AuNPs/PEDOT/CNTs/GCE was studied by Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and electrochemical impedance spectroscopy (EIS). Under optimum conditions, AuNPs/PEDOT/CNTs/GCE showed a good linear response in the concentration range from 9.14 to 29.704 μM with a detection limit (LOD) and sensitivity of 0.283 μM and 1.557 μA μM

Identifiers

PMID35702427
PMCPMC9115873

What OpenQuestion holds

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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.