Evidence map›Paper›PMID 41828761›Full record

ArticleMolecules (Basel, Switzerland)2026

ZIF-9-Derived Cobalt and Nitrogen-Doped Carbon Nanocomposites for Sensitive Electrochemical Nitrite Determination.

Yuan Li, Shaoqian Jia, Yuxin Shi, Lingxin Kong, Yichun Su, Guangxun Zhang, Bingyi Yan, Huan Pang, Feng Yu

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

9 authors.

Yuan LiKey Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, School of Chemistry and Chemical Engineering, Shihezi University, Shihezi 832003, China.
Shaoqian JiaFood and Pharmaceutical Research Institute, Jiangsu Food & Pharmaceutical Science College, Huai'an 223300, China.
Yuxin ShiSchool of Materials Science and Engineering, Suzhou University of Technology, Changshu 215500, China.ORCID 0009-0005-0391-8698
Lingxin KongSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.
Yichun SuSchool of Petrochemical Engineering, Changzhou University, Changzhou 213164, China.
Guangxun ZhangSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.
Bingyi YanSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.
Huan PangSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.ORCID 0000-0002-5319-0480
Feng YuKey Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, School of Chemistry and Chemical Engineering, Shihezi University, Shihezi 832003, China.ORCID 0000-0002-8140-8344

Funding

National Natural Science Foundation of China 52371240Xinjiang Science and Technology Program 2023TSYCCX0118
6 · The paper itself

Abstract

The accurate monitoring of nitrite levels is critically important for safeguarding public health and ensuring food safety, as excessive intake presents severe risks. In this study, we developed a highly sensitive electrochemical sensor for nitrite detection utilizing a cobalt-embedded porous carbon material derived from zeolitic imidazolate frameworks (ZIFs) of ZIF-9. The precursor was subjected to pyrolysis at various temperatures, revealing that the sample carbonized at 800 °C (ZIF-9-800) exhibited superior electrocatalytic performance. This enhancement is attributed to its optimized graphitization degree, high specific surface area, and the well-dispersed active sites resulting from the in situ generated cobalt nanoparticles. The ZIF-9-800-based sensor demonstrated outstanding electrochemical performance, achieving a broad linear detection range of 0.2-7000 μM, high sensitivity (848.6 μA mM

Indexed as

electrochemical sensormetal–organic frameworksnanoparticlesnitrite detection

Identifiers

PMID41828761
PMCPMC12985869

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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.