Eco-friendly and portable sensing: a review of advances in smartphone-integrated optical nanoprobes

Publikation: Beiträge in ZeitschriftenÜbersichtsarbeitenForschung

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Eco-friendly and portable sensing: a review of advances in smartphone-integrated optical nanoprobes. / Elagamy, Samar H.; Fuente-Ballesteros, Adrián; Hasan Obaydo, Reem et al.
in: Green Chemistry Letters and Reviews, Jahrgang 18, Nr. 1, 2548507, 2025.

Publikation: Beiträge in ZeitschriftenÜbersichtsarbeitenForschung

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@article{8819cb35b54c4a3c84b4509f836181fd,
title = "Eco-friendly and portable sensing: a review of advances in smartphone-integrated optical nanoprobes",
abstract = "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.",
keywords = "machine learning, microfluidic, nanoprobes, paper, Smartphone, Chemistry",
author = "Elagamy, \{Samar H.\} and Adri{\'a}n Fuente-Ballesteros and \{Hasan Obaydo\}, Reem and \{Mahmoud Lotfy\}, Hayam",
note = "Publisher Copyright: {\textcopyright} 2025 The Author(s). Published by Informa UK Limited, trading as Taylor \& Francis Group.",
year = "2025",
doi = "10.1080/17518253.2025.2548507",
language = "English",
volume = "18",
journal = "Green Chemistry Letters and Reviews",
issn = "1751-8253",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Eco-friendly and portable sensing

T2 - a review of advances in smartphone-integrated optical nanoprobes

AU - Elagamy, Samar H.

AU - Fuente-Ballesteros, Adrián

AU - Hasan Obaydo, Reem

AU - Mahmoud Lotfy, Hayam

N1 - Publisher Copyright: © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - machine learning

KW - microfluidic

KW - nanoprobes

KW - paper

KW - Smartphone

KW - Chemistry

UR - https://www.scopus.com/pages/publications/105014084225

U2 - 10.1080/17518253.2025.2548507

DO - 10.1080/17518253.2025.2548507

M3 - Scientific review articles

AN - SCOPUS:105014084225

VL - 18

JO - Green Chemistry Letters and Reviews

JF - Green Chemistry Letters and Reviews

SN - 1751-8253

IS - 1

M1 - 2548507

ER -

DOI