Evidence map›Paper›PMID 35991606›Full record

ArticleFrontiers in chemistry2022

Sensitive electrochemical detection of p-nitrophenol by pre-activated glassy carbon electrode integrated with silica nanochannel array film.

Ruobing Su, Hongliang Tang, Fengna Xi

Abstract read
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Article in Frontiers in chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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.

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3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Electrochemiluminescence Sensor for Uric Acid Detection Using Natural Enzyme-Confined Nanozyme Cascade.Luminescence : the journal of biological and chemical luminescence · 2026
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  5. Self-cascade Mn-FeMikrochimica acta · 2026
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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.

Ruobing SuDepartment of Chemistry, Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, China.
Hongliang TangAffiliated Fangchenggang Hospital, Guangxi University of Chinese Medicine, Fangchenggang, China.
Fengna XiDepartment of Chemistry, Key Laboratory of Surface & Interface Science of Polymer Materials of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Convenient, rapid and sensitive detection of p-nitrophenol (p-NP), one of the priority environmental pollutants, in environmental samples is of great significance. Electrochemical sensor with simple fabrication process, high sensitivity and selectivity, good antifouling, and regeneration performance is highly desirable. Herein, an electrochemical sensing platform is demonstrated based on the integration of vertically-ordered mesoporous silica-nanochannel film (VMSF) on electrochemical pre-activated glassy carbon electrode (p-GCE), which is able to realize ultrasensitive detection of p-NP in environmental samples. Electrochemical pre-activation of GCE is achieved through a simple and green electrochemical polarization process including anodic oxidation at high voltage and the following cathodic reduction at low voltage. The p-GCE possesses enhanced active area and introduced active sites, and enables stable binding of VMSF. VMSF is easily grown on p-GCE through the electrochemically assisted self-assembly (EASA) method within 10 s. Owing to the hydrogen bonding between silanol groups and p-NP, VMSF nanochannels display strong enrichment effect for the detection of p-NP. The developed VMSF/p-GCE sensor can achieve sensitive detection of p-NP ranging from 10 nM to 1 μM and from 1 to 30 μM with a limit of detection (LOD) of 9.4 nM. Considering the antifouling ability of VMSF, detection of p-NP in pond water is achieved.

Indexed as

electrochemical sensornanochannel array filmp-nitrophenpre-activated glassy carbon electrodesensitive detection

Identifiers

PMID35991606
PMCPMC9388733

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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.