Advances in sensor arrays for ions: From discrimination to application

Area of Science:

• Analytical Chemistry
• Materials Science
• Environmental Science

Background:

• Sensor arrays enable multiplexed ion identification via unique response patterns.
• Advanced materials (QDs, NCs, MOFs, probes) and machine learning (KNN, DT, RF) are key components.
• Existing reviews lack comprehensive integration of sensor array research from identification to application.

Purpose of the Study:

• To establish the first complete classification framework for sensor arrays in multiplexed ion identification.
• To link ion identification by sensor arrays to practical problem-solving.
• To explore the utility of sensor array technology in pollution control and product differentiation.

Main Methods:

• Utilizing diverse sensing materials like quantum dots (QDs), nanoclusters (NCs), metal-organic frameworks (MOFs), and molecular probes.
• Employing machine learning algorithms such as K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) for pattern recognition.
• Developing a comprehensive classification framework for sensor arrays in multiplexed ion identification.

Main Results:

• The review presents a novel classification framework for sensor arrays in multiplexed ion identification.
• Established a link between target ion identification and practical applications including environmental risk assessment, disease diagnosis, and product traceability.
• Highlighted the potential of sensor array technology for real-time monitoring and decision support.

Conclusions:

• Sensor array technology, integrating advanced materials and intelligent algorithms, offers significant potential for precise, rapid ion identification.
• The developed framework provides a systematic approach to understanding and applying sensor arrays for diverse practical challenges.
• Future advancements in multifunctional materials, intelligent algorithms, and system integration will enhance sensor array utility in complex matrices.