Evidence map›Paper›PMID 28425441›Full record

ArticleScientific reports2017

Methyl parathion detection in vegetables and fruits using silver@graphene nanoribbons nanocomposite modified screen printed electrode.

Mani Govindasamy, Veerappan Mani, Shen-Ming Chen, Tse-Wei Chen, Ashok Kumar Sundramoorthy

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Review
  3. Fabrication of FeVOACS omega · 2022
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  5. Review
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  16. VXC-72R/ZrOMaterials (Basel, Switzerland) · 2019
    Article
  17. Article
  18. 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

5 authors.

Mani GovindasamyDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei, Taiwan 106 ROC.
Veerappan ManiDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei, Taiwan 106 ROC.
Shen-Ming ChenDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei, Taiwan 106 ROC.
Tse-Wei ChenDepartment of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei, Taiwan 106 ROC.
Ashok Kumar SundramoorthySRM Research Institute, Department of Chemistry, SRM University, Kattankulathur, 603203, Chennai, Tamilnadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have developed a sensitive electrochemical sensor for Organophosphorus pesticide methyl parathion (MP) using silver particles supported graphene nanoribbons (Ag@GNRs). The Ag@GNRs nanocomposite was prepared through facile wet chemical strategy and characterized by TEM, EDX, XRD, Raman, UV-visible, electrochemical and impedance spectroscopies. The Ag@GNRs film modified screen printed carbon electrode (SPCE) delivers excellent electrocatalytic ability to the reduction of MP. The Ag@GNRs/SPCE detects sub-nanomolar concentrations of MP with excellent selectivity. The synergic effects between special electrocatalytic ability of Ag and excellent physicochemical properties of GNRs (large surface area, high conductivity, high area-normalized edge-plane structures and abundant catalytic sites) make the composite highly suitable for MP sensing. Most importantly, the method is successfully demonstrated in vegetables and fruits which revealed its potential real-time applicability in food analysis.

Indexed as

NanocompositesNanotubes, CarbonBiosensing TechniquesFruitMethyl ParathionVegetablesMethyl ParathionNanotubes, Carbon

Identifiers

PMID28425441
PMCPMC5397841

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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.