Nanozyme-Powered Multimodal Sensing for Pesticide Detection

Area of Science:

• Analytical Chemistry
• Materials Science
• Food Science

Background:

• Pesticide residue detection is vital for food safety and public health.
• Traditional detection methods are precise but costly and labor-intensive.
• Nanozymes present a promising alternative due to their low cost, high stability, and sensitivity.

Purpose of the Study:

• To review the advancements in nanozyme-based pesticide residue detection over the past decade.
• To explore various nanozyme types, their synthesis, and catalytic mechanisms.
• To discuss multimodal sensing strategies and the role of AI in this field.

Main Methods:

• Review of research on carbon-based, metal-based, metal-oxide-based, MOF-based, and fluorescence-based nanozymes.
• Analysis of multimodal sensing techniques (e.g., colorimetric/fluorescence, fluorescence/photothermal).
• Examination of nanozyme applications in conjunction with artificial intelligence (AI).

Main Results:

• Nanozymes demonstrate significant potential for sensitive and cost-effective pesticide detection.
• Various nanozyme platforms (carbon, metal, MOF, etc.) show diverse catalytic mechanisms.
• Multimodal sensing and AI integration enhance detection capabilities and address challenges.

Conclusions:

• Nanozymes are a viable and advancing technology for detecting pesticide residues in food.
• Further research is needed to overcome current challenges and optimize AI integration.
• This review provides guidance for selecting nanozymes and detection methods for pesticide analysis.