Evidence map›Paper›PMID 40509485›Full record

ReviewFoods (Basel, Switzerland)2025

Nanozyme-Powered Multimodal Sensing for Pesticide Detection.

Binfeng Yin, Zhuoao Jiang, Rashid Muhammad, Jun Liu, Junjie Wang

Abstract readReview
In one paragraph

Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Liquid Metal-Enabled Soft Bioelectronics for Human Health Monitoring.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  3. Review
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

5 authors.

Binfeng YinSchool of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.ORCID 0000-0002-0484-4951
Zhuoao JiangSchool of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Rashid MuhammadSchool of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.ORCID 0000-0001-5218-0689
Jun LiuSuqian Product Quality Supervision and Inspection Institute, Suqian 223800, China.
Junjie WangSchool of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.

Funding

"High-end Talent Support Plan" of Yangzhou UniversityKey Research and Development Program of Suqian L20240Market Supervision Administration Science and Technology Fund of Jiangsu Province KJ2023076Natural Science Foundation of the Jiangsu Higher Education Institutions of China 22KJB150050
6 · The paper itself

Abstract

The detection of pesticide residues in food is crucial for ensuring food safety, safeguarding public health, and promoting sustainable development. Overusing pesticides on agricultural crops can lead to the emergence of various diseases. Traditional methods for detecting pesticides offer high precision with limitations like high cost, the requirement of expert technicians, and tedious analytical procedures. To address these issues, nanozymes have been widely applied due to their advantages such as low cost, high stability, and high sensitivity. This review summarizes the research progress of nanozymes in the detection of pesticide residues in food over the last decade, focusing on the synthesis strategies and catalytic mechanisms of carbon-based, metal-based, metal-oxide-based, metal-organic framework (MOF)-based, fluorescence-based, and other X-based nanozymes. This review covers the application of multimodal sensing based on nanozymes in the detection of pesticides, including colorimetric/fluorescence, fluorescence/photothermal, photothermal/colorimetric, and other multimodal sensing techniques. Finally, this review discusses the main challenges currently faced by nanozymes in the detection of pesticides and the current applications of using AI with nanozymes. It also presents future development prospects, with the aim of providing references for the selection of X-based nanozymes and the choice of appropriate detection methods when dealing with traditional and new pesticides in combination with AI.

Indexed as

AIfood safetymultimodal sensingnanozymespesticide residues

Identifiers

PMID40509485
PMCPMC12154461

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.