Evidence map›Paper›PMID 41688825›Full record

ReviewMikrochimica acta2026

Research progress on functional carbon quantum dots fluorescent probes for detecting pesticide residues in food.

Jiamin Tian, Ruirui Yang, Xiaoyun Ren, Qiulin Wang, Jingyi Qing, Wang Li, Ran Wang, Zhiwei Su, Chenzhao Wang, Xiaotong Lin and 5 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Mikrochimica acta, 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

15 authors.

Jiamin TianCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Ruirui YangBinzhou People's Hospital, Binzhou, 256610, China.
Xiaoyun RenBinzhou People's Hospital, Binzhou, 256610, China.
Qiulin WangCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Jingyi QingCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Wang LiCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Ran WangCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Zhiwei SuCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Chenzhao WangCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Xiaotong LinCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Xiaomeng LiuCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Wenlu LiCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Yao JiaCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China.
Chunlong SunCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China. chunlongsun@163.com.
Wen DuCollege of Biological and Pharmaceutical Engineering, Shandong University of Aeronautics, Binzhou, 256603, China. duwen6688@163.com.

Funding

Shandong Provincial Natural Science Foundation ZR2023MD117
6 · The paper itself

Abstract

Pesticide residues in food pose potential threats to human health and the ecological environment. Developing rapid and sensitive detection technologies is crucial for ensuring food safety and ecological balance. Traditional detection methods suffer from high equipment costs, complex sample pretreatment, and lengthy analysis cycles, making them unsuitable for grassroots supervision and on-site rapid screening. Carbon quantum dots (CQDs/CDs) as a new type of fluorescent carbon nanomaterial, have become a core research direction in the field of food pesticide residue detection due to their high quantum yield, excellent photothermal stability, abundant raw material sources, and green and simple synthesis processes. This article systematically reviews the structural characteristics, fluorescence properties, preparation pathways, and detection advantages of CQDs, analyzes the preparation of four types of probes, and explains the detection mechanisms such as Förster resonance energy transfer and the inner filter effect. On this basis, the latest application progress of CQDs fluorescent probes in the detection of pesticides, herbicides, fungicides, and antibiotic residues was comprehensively reviewed, covering various food matrices, and highlighting their significant competitiveness in detection limit, response speed, operational convenience, and cost control through quantitative comparison. Besides, the review objectively analyzed the challenges faced by current technology, future research directions, and outlooks. This research not only provides a reference for developing and applying new CQDs detection technologies for pesticide residues in food but also holds significant practical value for improving food safety regulatory systems, reducing health risks from pesticide residues, and promoting high-quality development in the food industry.

Indexed as

Carbon Quantum DotsFluorescent DyesFood AnalysisFood ContaminationPesticide ResiduesQuantum DotsFluorescence Resonance Energy TransferHumansLimit of DetectionFluorescent DyesPesticide ResiduesCarbon quantum dotsFluorescent probesFood analysisPesticide residues

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.