Nanosensors as Diagnostic Tools: Emerging Concepts, Opportunities, and Design Barriers

Abstract

Nanosensors have become a revolutionary tool, enabling early diagnosis and continuous monitoring of diseases with high accuracy. These tiny devices, operating at the nanoscale (typically between 1 and 100 nm), serve as signal generators to detect minute changes that traditional diagnostic tools might miss. The combination of nanoscale precision and their multifunctional capabilities shows a substantial advancement in nanotechnology and its practical applications. Nanotechnology is increasingly used across various fields, including healthcare, environmental monitoring, and manufacturing. However, significant challenges persist in the design and fabrication of nanosensors, particularly in achieving high precision, sensitivity, and selectivity, as well as in managing the inherent complexities of operation at atomic and molecular scales. To address these challenges, this paper explores various fabrication techniques, advances in material development, and strategies to enhance sensor feedback and responsiveness through a comprehensive knowledge system, known as the function-context-behavior-principle-state-structure (FCBPSS) framework. This framework is employed to categorize information and insights related to nanosensor development for early disease detection. One contribution of this paper is to critically examine the functions and principles that drive the development of nanosensors in biomedical systems, as well as their behavior and structural performance. Another contribution is documenting recent advancements in nanosensor fabrication, design, and materials towards future research and development in this field.

Article information

Article type
Critical Review
Submitted
22 Nov 2025
Accepted
23 Dec 2025
First published
29 Dec 2025
This article is Open Access
Creative Commons BY license

Anal. Methods, 2026, Accepted Manuscript

Nanosensors as Diagnostic Tools: Emerging Concepts, Opportunities, and Design Barriers

B. O. Omiyale, A. Ogbeyemi, W. Zhang, M. A. Ashraf, K. Song and H. yu, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D5AY01942G

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