Work

Taguchi optimization of CPTES chemistry for photonic crystal-based biosensors: Detection of anti-SARS-CoV-2 antibodies in human serum

Year: 2025
Type: article
Abstract: Surface functionalization is a pivotal step in the development of biosensors, as it directly influences the immobilization efficiency of biological recognition elements and, consequently, the overall performance of the biosensing platforms. In this study, we employed the Taguchi method to systematically optimize the functionalization parameters of a silicon oxide surface using an organosilane compound. The biosensing platform is based on a one-dimensional photonic crystal structure capable of sustaining Bloch surface waves, which enable highly sensitive optical detection. The use of the Taguchi orthogonal array design allowed us to efficiently explore a limited and representative set of parameter combinations, significantly reducing experimental complexity. Once the optimal functionalization conditions were identified, the biosensors were applied to the analysis of human serum samples (diluted 1:50) collected from both COVID-19 patients and healthy individuals. The system demonstrated the ability to specifically detect IgM antibodies related to SARS-CoV-2 virus, which are typically present in trace amounts. This work presents the first Taguchi-based optimization of a surface functionalization strategy via label-free molecular interactions for biosensing. The approach provides a platform for both optimization and sensitive detection of clinically relevant biomarkers, with clear diagnostic potential. • Taguchi-based surface chemistry optimization on 1D photonic crystal biosensors using CPTES. • Label-free binding of SARS-CoV-2 nucleocapsid antibodies guides efficient surface chemistry on viral protein–immobilized spots. • Dual label-free and fluorescence detection enabled analysis of bio-interactions and sensitive identification of IgM antibodies. • Successful detection of low-abundance IgM antibodies in serum from COVID-19 recovered individuals.
Language: English
Open Access status: hybrid
Estimated APC paid: $5,010
OpenAlex ID: W4415214376
FWCI: 0.741
Cites: 31
Cited by: 2
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