The integration of smartphones with optical nanoprobes has emerged as a powerful approach for developing portable and cost-effective analytical platforms. Paper-based microfluidic analytical devices (µPADs) and microfluidic chips further enhance the practicality of smartphone-integrated optical sensors. These portable sensors enables on-site, and point-of-care testing offering a sustainable and green alternative to conventional analytical methods by minimizing energy consumption, reducing analysis time and cost, and eliminating the need for sophisticated equipment. This review explores various types of optical nanosensors, including plasmonic nanoparticles, quantum dots, metal–organic frameworks (MOFs), upconversion nanoparticles, and carbon quantum dots (CQDs), highlighting their unique optical properties. The review also discusses different detection methods for these sensors such as colorimetric, fluorescence, and ratiometric fluorescence assays, emphasizing their role in enhancing sensitivity and selectivity. Additionally, the integration of machine learning algorithms in nanosensor analysis is explored, demonstrating its potential for handling complex data and improving detection performance. The review highlights key applications in biosensing, heavy metal detection, and food contaminant analysis while addressing critical challenges such as reproducibility, imaging optimization, and data processing. Overcoming these challenges will be crucial for the widespread adoption of smartphone-integrated optical nanoprobes in real-world applications.
Elagamy et al. (2025) studied this question.