Highly Sensitive Detection and Discrimination of Cell Suspension Based on a Metamaterials-Based Biosensor Chip

Metamaterials (MMs)-based terahertz (THz) biosensors hold promise for clinical diagnosis, featuring label-free operation, simple, rapid detection, low cost, and multi-cell-type discrimination. However, liquid around cells causes severe interference to sensitive detection. Most existing MMs-based cell biosensors detect dead cells without culture medium (losing original morphology), hindering stable, sensitive multi-cell discrimination. Here, a terahertz biosensor composed of a microcavity and MMs can be used to detect and discriminate multiple cell types within suspension. Its detection mechanism relies on cellular size (radius)/density in suspension, which induces effective permittivity (<i>ε</i><sub>eff</sub>) differences. By designing MMs' split rings with luxuriant gaps, the biosensor achieves a theoretical sensitivity of ~328 GHz/RIU, enabling sensitive responses to suspended cells. It shows a robust, increasing frequency shift (610-660 GHz) over 72 h of cell apoptosis. Moreover, it discriminates nerve cells, glioblastoma (GBM) cells, and their 1:1 mixture with obviously distinct frequency responses (~650, ~630, ~620 GHz), which suggests effective and reliable multi-cell-type recognition. Overall, this study and its measurement method should pave the way for metamaterial-based terahertz biosensors for living cell detection and discrimination, and this technology may inspire further innovations in tumor investigation and treatment.

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Publication Details

Journal
Biosensors
Published
2026-01-08
DOI
https://doi.org/10.3390/bios16010050
Primary Topic
Terahertz technology and applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Highly Sensitive Detection and Discrimination of Cell Suspension Based on a Metamaterials-Based Biosensor Chip

Lu Hu, Kanglong Chen, Xiaofang Zhao, Jie Sun et al.
Biosensors
Terahertz technology and applications
article

Highly Sensitive Detection and Discrimination of Cell Suspension Based on a Metamaterials-Based Biosensor Chip

Lu Hu, Kanglong Chen, Xiaofang Zhao, Jie Sun, Qinggang Ge, Jun Yang, Qian Wang
article en

Abstract

Metamaterials (MMs)-based terahertz (THz) biosensors hold promise for clinical diagnosis, featuring label-free operation, simple, rapid detection, low cost, and multi-cell-type discrimination. However, liquid around cells causes severe interference to sensitive detection. Most existing MMs-based cell biosensors detect dead cells without culture medium (losing original morphology), hindering stable, sensitive multi-cell discrimination. Here, a terahertz biosensor composed of a microcavity and MMs can be used to detect and discriminate multiple cell types within suspension. Its detection mechanism relies on cellular size (radius)/density in suspension, which induces effective permittivity (<i>ε</i><sub>eff</sub>) differences. By designing MMs' split rings with luxuriant gaps, the biosensor achieves a theoretical sensitivity of ~328 GHz/RIU, enabling sensitive responses to suspended cells. It shows a robust, increasing frequency shift (610-660 GHz) over 72 h of cell apoptosis. Moreover, it discriminates nerve cells, glioblastoma (GBM) cells, and their 1:1 mixture with obviously distinct frequency responses (~650, ~630, ~620 GHz), which suggests effective and reliable multi-cell-type recognition. Overall, this study and its measurement method should pave the way for metamaterial-based terahertz biosensors for living cell detection and discrimination, and this technology may inspire further innovations in tumor investigation and treatment.

BiosensorsVol. 16(1)
Zhejiang International Studies University (CN), Peking University (CN), Peking University Third Hospital (CN), Beihang University (CN)
Beihang University, Beijing Municipal Science and Technology Commission
Reduced inequalities
Openalex Percentile: Top 94%
Terahertz technology and applications
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Highly Sensitive Detection and Discrimination of Cell Suspension Based on a Metamaterials-Based Biosensor Chip — Lu Hu, Kanglong Chen, et al. · Biosensors (2026) | TGRS Research Map | TGRS