Multimodal LSPR-enhanced crayfish-type optical fiber sensor for ultra-sensitive detection of <i>Shigella sonnei</i> using hybrid nanomaterials

This paper designs a biophotonic sensor that utilizes the localized surface plasmon resonance (LSPR) effect to detect Shigella sonnei (S. sonnei) with high sensitivity, featuring a novel crayfish-type optical fiber structure. Diseases and food safety caused by S. sonnei have become a public health issue of common concern around the world. This sensor is specifically designed for the detection of S. sonnei. This sensor has the advantage of being easy to operate, requires no labeling, and has high specificity. Excite the LSPR effect using gold nanoparticles (AuNPs). To enhance the LSPR effect, a fusion structure of multimode fiber and seven-core fiber was utilized, as was a crayfish-type optical fiber structure. Using Rsoft to simulate the crayfish-type optical fiber structure, it is concluded that the structure has excellent evanescent field. S. sonnei antibodies were used to improve the specificity of the sensor. Tungsten disulfide thin layer (WS2-thin layer) and zinc oxide nanowires were used to increase the surface area for antibody attachment. The linear range of the sensor was 1 × 100–1 × 107 CFU/ml, the sensitivity was 0.378 nm/lg (CFU/ml), and the limit of detection was 4.78 CFU/ml. The reproducibility, reusability, selectivity, and stability of the sensor were tested. The test results showed that the sensor had excellent performance. In addition, the sensor was tested with real food samples. This research has far-reaching significance for biophotonic sensors and human health.

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Publication Details

Journal
APL Photonics
Published
2024-12-01
DOI
https://doi.org/10.1063/5.0242975
Citations
2
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
0.37
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article

Multimodal LSPR-enhanced crayfish-type optical fiber sensor for ultra-sensitive detection of Shigella sonnei using hybrid nanomaterials

Santosh Kumar, Rui Min, Bingyuan Zhang, Jan Nedoma et al.
2 citations
APL Photonics
Biosensors and Analytical Detection
0.37
article

Multimodal LSPR-enhanced crayfish-type optical fiber sensor for ultra-sensitive detection of Shigella sonnei using hybrid nanomaterials

Santosh Kumar, Rui Min, Bingyuan Zhang, Jan Nedoma, Carlos Marques, Ragini Singh, Qi Zhang
article en
2 citations

Abstract

This paper designs a biophotonic sensor that utilizes the localized surface plasmon resonance (LSPR) effect to detect Shigella sonnei (S. sonnei) with high sensitivity, featuring a novel crayfish-type optical fiber structure. Diseases and food safety caused by S. sonnei have become a public health issue of common concern around the world. This sensor is specifically designed for the detection of S. sonnei. This sensor has the advantage of being easy to operate, requires no labeling, and has high specificity. Excite the LSPR effect using gold nanoparticles (AuNPs). To enhance the LSPR effect, a fusion structure of multimode fiber and seven-core fiber was utilized, as was a crayfish-type optical fiber structure. Using Rsoft to simulate the crayfish-type optical fiber structure, it is concluded that the structure has excellent evanescent field. S. sonnei antibodies were used to improve the specificity of the sensor. Tungsten disulfide thin layer (WS2-thin layer) and zinc oxide nanowires were used to increase the surface area for antibody attachment. The linear range of the sensor was 1 × 100–1 × 107 CFU/ml, the sensitivity was 0.378 nm/lg (CFU/ml), and the limit of detection was 4.78 CFU/ml. The reproducibility, reusability, selectivity, and stability of the sensor were tested. The test results showed that the sensor had excellent performance. In addition, the sensor was tested with real food samples. This research has far-reaching significance for biophotonic sensors and human health.

APL PhotonicsVol. 9(12)
VSB - Technical University of Ostrava (CZ), Liaocheng University (CN), Beijing Normal University (CN), University of Aveiro (PT), Koneru Lakshmaiah Education Foundation (IN)
Openalex Percentile: Top 42%
Biosensors and Analytical Detection
0.37
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