Evidence map›Paper›PMID 41758422›Full record

ArticleJournal of materials science. Materials in medicine2026

Sustainable development of ZnO nanoparticles and nanocellulose-modified CPE for electrochemical sensing of tinidazole.

Mohamed Abd-Elsabour, Mortaga M Abou-Krisha, Abdulrahman G Alhamzani, Ehab A Abdelrahman

Abstract read
In one paragraph

Article in Journal of materials science. Materials in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mohamed Abd-ElsabourChemistry Department, Faculty of Science, Luxor University, Luxor, 85951, Egypt. m.sabour28@sci.svu.edu.eg.ORCID http://orcid.org/0000-0002-7261-9326
Mortaga M Abou-KrishaChemistry Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia.
Abdulrahman G AlhamzaniChemistry Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia.
Ehab A AbdelrahmanChemistry Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The detection of tinidazole (TIZ) in environmental and pharmaceutical samples remains constrained by conventional electrochemical sensors, which often rely on energy-intensive synthesis routes and toxic modifiers, undermining their sustainability. To bridge this gap, this study introduces a green synthesis approach for sensor fabrication, leveraging the concept of waste-to-value by converting banana peel into nanocellulose (PNC) and using it as a sustainable scaffold for zinc oxide nanoparticles (ZnO NPs). The enhanced performance of the PNC-ZnO/CPE sensor originates from a synergistic interplay between the high surface area and conductivity of ZnO NPs and the dispersive and stabilizing properties of PNC, which collectively facilitate the electron transfer kinetics for TIZ reduction. The prepared samples were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX). The sensor demonstrated a linear response to TIZ concentrations ranging from 9.0 ×10

Indexed as

CelluloseElectrochemical TechniquesMetal NanoparticlesNanoparticlesTinidazoleZinc OxideElectrodesGreen Chemistry TechnologyLimit of DetectionMicroscopy, Electron, ScanningSpectrometry, X-Ray EmissionX-Ray DiffractionCelluloseTinidazoleZinc Oxide

Identifiers

PMID41758422
PMCPMC12960463

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.