Evidence map›Paper›PMID 41169995›Full record

ArticleRSC advances2025

A novel MOF-based electrochemical sensor for simultaneous quantification of nitrophenols in binary mixtures using artificial neural networks: challenges and opportunities.

Abolfazl Kiasadr, S Maryam Sajjadi, Mehdi Baghayeri

Abstract read
In one paragraph

Article in RSC advances, 2025. 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

3 authors.

Abolfazl KiasadrFaculty of Chemistry, Semnan University Semnan Iran.
S Maryam SajjadiDepartment of Chemical Engineering, Lamerd Higher Education Center, Shiraz University of Technology Lamerd Fars Iran sajjadi@sutech.ac.ir (+98) 71 52726630 (+98) 71 52731135.ORCID https://orcid.org/0000-0001-9180-6904
Mehdi BaghayeriDepartment of Chemistry, Faculty of Science, Hakim Sabzevari University Sabzevar Iran.ORCID https://orcid.org/0000-0003-3004-1324

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces an artificial neural network (ANN)-assisted electrochemical strategy for the simultaneous quantification of 2-nitrophenol (2-NP) and 4-nitrophenol (4-NP), two environmentally hazardous pollutants with highly overlapping voltammetric responses. A glassy carbon electrode (GCE) was modified with a multilayer nanocomposite (Ni-MOF-74/Fe

Identifiers

PMID41169995
PMCPMC12570034

What OpenQuestion holds

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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.