Evidence map›Paper›PMID 31037355›Full record

ArticleMikrochimica acta2019

Electrochemically reduced graphene oxide and gold nanoparticles on an indium tin oxide electrode  for voltammetric sensing of dopamine.

Xin Huang, Weishan Shi, Ning Bao, Chunmei Yu, Haiying Gu

Abstract read
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In one paragraph

Article in Mikrochimica acta, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

6 citing papers in PubMed, 30 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Xin HuangSchool of Public Health, Nantong University, Nantong, 226019, People's Republic of China.
Weishan ShiSchool of Public Health, Nantong University, Nantong, 226019, People's Republic of China.
Ning BaoSchool of Public Health, Nantong University, Nantong, 226019, People's Republic of China.
Chunmei YuSchool of Public Health, Nantong University, Nantong, 226019, People's Republic of China. cmyu@ntu.edu.cn.
Haiying GuSchool of Public Health, Nantong University, Nantong, 226019, People's Republic of China. hygu@ntu.edu.cn.
Nantong University · CN

Funding

the Application Research Item of Nantong City MS12017023-1the National Natural Science Foundation of China 31741051
6 · The paper itself

Abstract

The authors describe an electrochemical dopamine sensor that is based on the use of electrochemically co-reduced graphene oxide (Er-GO) and gold nanoparticles (AuNPs) on an indium-tin oxide (ITO) electrode. The synergistic effects of Er-GO and Er-AuNPs promote electron transport in the modified ITO. This results in an excellent performance for voltammetric sensing of dopamine (DA). Under the optimum conditions and a typical working potential of -0.05 V (vs. Ag/AgCl), the ITO electrode has a linear response in the 0.02-200 μM DA concentration range and a low detection limit of 15 nM. The sensor also showed a good selectivity over ascorbic acid and uric acid. The feasibility of the method was studied by analyzing DA in cerebrospinal fluid of rats. Graphical abstract Schematic presentation of one-step electrochemical co-reduction of graphene oxide (GO) and gold nanoparticles (AuNPs) on an ITO electrode for voltammetric sensing of dopamine.

Indexed as

AnimalsDopamineElectrochemistryGoldGraphiteLimit of DetectionMetal NanoparticlesModels, MolecularMolecular ConformationRatsTin CompoundsDopamineGoldgraphene oxideGraphiteindium tin oxideTin CompoundsCerebrospinal fluidDifferential pulse voltammetryDisposable electrodeElectrochemical sensorSurface electrochemical co-reduction

Identifiers

PMID31037355
OpenAlexW2943817120

What OpenQuestion holds

Textmetadata
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.