Evidence map›Paper›PMID 38832283›Full record

ArticleHeliyon2024

Study of graphene incorporation into ZnO-PVA nanocomposites modified electrode for sensitive detection of cadmium.

Abrar Ismardi, Theresia Deviyana Gunawan, Asep Suhendi, Indra Wahyudin Fathona

Abstract read
In one paragraph

Article in Heliyon, 2024. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Abrar IsmardiDepartment of Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia.
Theresia Deviyana GunawanDepartment of Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia.
Asep SuhendiDepartment of Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia.
Indra Wahyudin FathonaDepartment of Engineering Physics, School of Electrical Engineering, Telkom University, Bandung, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The presence of heavy metals often causes significant health risks, particularly cadmium, which is known for its high toxicity. In this study, a glassy carbon electrode was successfully modified by incorporating ZnO-PVA-Graphene nanocomposite, leveraging the excellent electrical properties and electron mobility of the material. Comprehensive material analysis, including XRD, confirmed that ZnO maintained its hexagonal wurtzite crystal structure despite the addition of graphene. Moreover, FESEM analysis showed that increasing graphene concentration led to a reduction in ZnO particle size by 85, 68, and 52 nm, respectively, accompanied by a decrease in band gap energy, as verified by UV-Vis measurements. Photoluminescence tests were also conducted and the result showed a noticeable blue shift in ZnO-PVA-Graphene nanocomposites compared to ZnO-PVA, specifically in the near band-edge (NBE) UV emission within the 374-379 nm wavelength range. Through I-V characterization, the optimal graphene concentration for cadmium detection was identified as 1.5 wt% in ZnO-PVA-Graphene nanocomposites, showing an approximate ohmic response. Meanwhile, square-wave voltammetry analysis of cadmium concentrations ranging from 0 to 80 ppm produced a coefficient of determination of 0.98926 and a Limit of Detection (LOD) of 9.88 ppm. These results showed the significant potential of ZnO-PVA-Graphene nanocomposites as a promising material for further development as an effective electrode modifier, enhancing the sensitivity of detection systems.

Identifiers

PMID38832283
PMCPMC11145211

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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.