Multiplexed Lab-on-Chip Biosensors for Viral Detection
Year: 2025
Type: article
Abstract: Multiplexed lab-on-chip (LOC) biosensors have emerged as a transformative class of analytical devices, enabling the concurrent detection of multiple viral biomarkers within a single, miniaturized platform. This capability is pivotal for differential diagnosis of co-circulating pathogens such as Dengue, Zika, chikungunya, and SARS-CoV-2, where overlapping clinical symptoms can hinder timely and accurate patient management. Integrating microfluidics with electrochemical, optical, and hybrid transduction modalities, multiplex LOC systems reduce assay time, minimize sample volume, and support deployment in resource-limited settings. Recent advances have demonstrated the use of nanomaterial-enhanced surfaces, gold nanoparticles, graphene derivatives, and metal–organic frameworks to increase active surface area, improve electron transfer kinetics, and enable high-density probe immobilization. Aptamer-based recognition elements are increasingly favored over antibodies due to their superior stability, facile chemical modification, and compatibility with multiplexing chemistries such as thiol–gold self-assembly, carbodiimide coupling, and π–π stacking. Representative platforms employ screen-printed electrode arrays, graphene field-effect transistor chips, and paper–microfluidic hybrids, achieving sub-nanogram per milliliter limits of detection with assay times under 30 minutes. This review critically evaluates the design strategies, immobilization approaches, and performance metrics of state-of-the-art multiplex LOC biosensors, identifies key challenges in cross-reactivity and standardization, and outlines future directions toward clinically validated, AI-integrated, and wearable diagnostic systems for rapid viral surveillance and outbreak control.
Authors: Abhishek Futane, Norhashimah Mohd Saad, Vigneswaran Narayanamurthy, Kok Swee Leong
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077274153
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