Innovative Microfluidic Technologies for Rapid Heavy Metal Ion Detection

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring.

Authors

Institutions

Publication Details

Journal
Chemosensors
Published
2025-04-18
DOI
https://doi.org/10.3390/chemosensors13040149
Citations
7
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
4.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Innovative Microfluidic Technologies for Rapid Heavy Metal Ion Detection

Muhammad Rauf, Muhammad Kamran Rauf, Jin‐Ming Lin, Zhenlei Lin
7 citations
Chemosensors
Biosensors and Analytical Detection
4.72
article

Innovative Microfluidic Technologies for Rapid Heavy Metal Ion Detection

Muhammad Rauf, Muhammad Kamran Rauf, Jin‐Ming Lin, Zhenlei Lin
article en
7 citations

Abstract

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring.

ChemosensorsVol. 13(4)
Beijing University of Technology (CN), Tsinghua University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 5%
Biosensors and Analytical Detection
4.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.