Evidence map›Paper›PMID 39718606›Full record

ArticleMikrochimica acta2024

Ultra-sensitive detection of 4-chloro-2-methylphenoxyacetic acid herbicide using a porous Co-1,4-benzenedicarboxylate /montmorillonite nanocomposite sensor.

Mona Elfiky, Moa'mena Abdo, Mona Darwesh, Nehal Salahuddin

Abstract read
In one paragraph

Article in Mikrochimica acta, 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. 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

4 authors.

Mona ElfikyChemistry Department, Faculty of Science, Tanta University, Tanta, Egypt. elfiky_mona@science.tanta.edu.eg.
Moa'mena AbdoBasic Sciences Department, the Higher Institute of Engineering, Kafr El-Sheikh, Egypt.
Mona DarweshPhysics Mathematical Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
Nehal SalahuddinChemistry Department, Faculty of Science, Tanta University, Tanta, Egypt. nehal.attaf@science.tanta.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The detection of 4-chloro-2-methylphenoxyacetic acid (CMPA) herbicide is crucial due to the potential health risks linked to exposure through drinking water, air, and food, which may adversely affect liver and kidney functions. To address this environmental concern and promote sustainable agriculture, a sensitive carbon paste sensor incorporating a composite material was developed. The composite sensor is based on porous cobalt-1,4-benzenedicarboxylate metal-organic framework and exfoliated montmorillonite nanolayers (Co-OF/MMt). This sensor enables the voltammetric detection of CMPA in real soil samples using linear sweep adsorptive anodic stripping voltammetry (LS-AdASV), facilitating early and accurate monitoring of herbicide levels. The Co-OF/MMt nanocomposite was synthesized via a hydrothermal method involving the precipitation of Co-OF in the presence of MMt. Comprehensive characterization of the synthesized materials was carried out using Fourier transform infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD), scanning electron microscopy (SEM), SEM-Energy dispersive X-ray spectroscopy (SEM-EDX) and surface area analysis (BET). The resulting modified carbon paste sensor, utilizing 1.0% Co-OF/MMt nanocomposite, exhibited superior electrochemical properties compared with the bare carbon paste sensor, possessing an electroactive surface area of 1004.1 m

Indexed as

4-Chloro-2-methylphenoxyacetic acidEnvironmental monitoringHerbicideMetal–organic frameworkModified carbon paste electrodeMontmorilloniteStripping voltammetry

Identifiers

PMID39718606
PMCPMC11668838

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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.