Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review

Area of Science:

• Environmental Science
• Analytical Chemistry
• Biotechnology

Background:

• Water pollution poses significant threats to human health and ecosystems.
• Traditional water quality testing methods are often lab-bound and not suitable for field applications.
• Microfluidic devices offer a portable and efficient alternative for water analysis.

Purpose of the Study:

• To review the integration of artificial intelligence (AI) with microfluidic devices for water pollutant detection.
• To highlight advancements in AI algorithms and microfluidic platform technologies.
• To discuss the application of these combined technologies for monitoring heavy metals, pesticides, microplastics, and microalgae.

Main Methods:

• Introduction to AI algorithms and microfluidic fabrication techniques.
• Review of AI-driven detection methods on microfluidic platforms.
• Analysis of recent research combining AI and microfluidics for specific pollutant detection.

Main Results:

• AI and microfluidics provide a synergistic approach for sensitive and rapid water quality assessment.
• The combined technology is effective for detecting a wide range of contaminants, including heavy metals, pesticides, microplastics, and microalgae.
• Significant progress has been made in developing integrated systems for real-time monitoring.

Conclusions:

• AI-integrated microfluidic devices represent the next generation of water quality monitoring systems.
• Further research is needed to address current challenges and optimize detection methods.
• This technology holds great promise for enhanced environmental protection and public health.