Evidence map›Paper›PMID 40331479›Full record

ReviewSmall (Weinheim an der Bergstrasse, Germany)2025

Design Principles of Nanosensors for Multiplex Detection of Contaminants in Food.

Yang Zhang, Shipeng Gao, Haoran Li, Tianxi Yang, Kaiyi Zheng, Zhing Ming Guo, Jiyong Shi, Xiaowei Huang, Xiaobo Zou, Pierre Picchetti and 1 more

Abstract readReview
In one paragraph

Review in Small (Weinheim an der Bergstrasse, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

11 authors.

Yang ZhangSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.ORCID 0000-0002-0243-6989
Shipeng GaoSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Haoran LiSchool of Electrical and Information Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Tianxi YangFood, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
Kaiyi ZhengSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Zhing Ming GuoSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Jiyong ShiSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Xiaowei HuangSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Xiaobo ZouSchool of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
Pierre PicchettiInstitute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131, Karlsruhe, Germany.
Frank BiedermannInstitute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131, Karlsruhe, Germany.

Funding

Fonds der Chemischen IndustrieNatural Science Foundation of Jiangsu Province BK20210748Natural Science Foundation of Jiangsu Province BK20230525Senior Talent Foundation of Jiangsu University 21JDG055Senior Talent Foundation of Jiangsu University 23JDG022
6 · The paper itself

Abstract

The rapid and cost-effective detection of food contaminants such as toxins and pathogens is a major challenge and a key concern for food safety. To this end, innovative, fast, cost-effective, and easy-to-use sensors must be developed at the point where food is produced, distributed, and consumed. Therefore, timely detection and response to food contaminants can improve human health and reduce economic burden. However, affordable sensor technologies with specificity, sensitivity, and speed are required, which can be used by non-specialized personnel and enable high throughput analysis. In this respect, advances in the development of nanoparticle-based sensors, i.e., nanosensors, have shown the potential to provide the much-anticipated versatile sensors. In addition, multiplex detection, i.e., the ability to detect multiple targets simultaneously, is another strategy facilitated by nanoparticle-based sensors and will enable further improvements in sensor performance that are important for developing effective monitoring. This review summarizes the nanosensors for multiplex sensing of food samples with respect to hazardous contaminates reported over the past few years. In addition, special attention is paid to providing the reader with promising design principles and the current performance of the sensitivity and selectivity of such sensors for practical requirements, thereby inspiring new ideas for developing further advanced systems.

Indexed as

Biosensing TechniquesFood AnalysisFood ContaminationNanotechnologyHumansNanoparticlesfood safetymultiplexmultiplex detectionnanosensorsnanotechnology

Identifiers

PMID40331479
PMCPMC12232259

What OpenQuestion holds

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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.