Evidence map›Paper›PMID 41658148›Full record

ArticleACS omega2026

Photonic Crystal Microsphere-Based SERS Biosensor for Ultrasensitive Detection of Aflatoxin B1 in Feed.

Yuxuan Chen, Ruipeng Chen, Hui Wang, Dongxia Pan, Mengting Zhou, Liang Yang, Zhen Ma, Fan Zhang, Jiawen Zhao, Xiangfang Tang and 1 more

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Yuxuan ChenState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.ORCID https://orcid.org/0009-0008-9506-0554
Ruipeng ChenState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.ORCID https://orcid.org/0000-0001-7104-6629
Hui WangState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.ORCID https://orcid.org/0000-0002-0041-5957
Dongxia PanState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Mengting ZhouState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Liang YangState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Zhen MaSchool of Agricultural Engineering, Jiangsu University, No. 301 Xuefu Road, Jingkou District, Zhenjiang 212013, China.
Fan ZhangState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.ORCID https://orcid.org/0000-0003-4275-4786
Jiawen ZhaoState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Xiangfang TangState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.
Benhai XiongState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China.ORCID https://orcid.org/0000-0003-2773-5739

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aflatoxin B1 (AFB1), a highly carcinogenic mycotoxin, poses significant threats to feed safety and human health, even at trace levels. To address the demand for trace detection of AFB1, we proposed a surface-enhanced Raman scattering (SERS) sensor based on photonic crystal microspheres (PCMs) for a wide-range and ultrasensitive quantitative detection of AFB1. This sensor employed SERS nanotags of gold-silver core-shell nanostars embedded with Raman reporter molecules as the enhancement substrate. Its surface protrusions can generate strong plasmon resonance, which further forms extremely intense "hot spots"a structural feature that renders it significantly superior to spherical nanoparticles in trace detection. Experimental results demonstrate that the developed sensor exhibits an excellent detection performance. Furthermore, benefiting from the unique structural color of PCMs, the sensor is capable of detecting trace amounts of AFB1 in complex matrices. The limit of detection (LOD) reached 0.988 pg/mL, and the linear dynamic range (LDR) was from 1 pg/mL to 100 ng/mL. Additionally, the sensor exhibits excellent specificity, reproducibility, and stability. After 14 days of storage, the sample retained 89.2% of its initial performance. In practical applications, it showed good consistency with commercial enzyme-linked immunosorbent assay (ELISA) kits. All of these results demonstrate that the proposed method has great potential for trace detection and is expected to provide a novel integrated analytical platform with low background, high sensitivity, and a wide detection range for feed safety monitoring.

Identifiers

PMID41658148
PMCPMC12878288

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Registered trials

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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.