A machine vision tool for multi-color H₂O₂ sensing by MoOx nanoparticles with oxygen vacancies

Area of Science:

• Materials Science
• Analytical Chemistry
• Nanotechnology

Background:

• Accurate hydrogen peroxide (H2O2) detection is crucial for daily life and industrial processes.
• Current methods for H2O2 detection often lack speed, portability, and ease of use.

Purpose of the Study:

• To develop a rapid, visual, and portable method for hydrogen peroxide (H2O2) detection.
• To quantify H2O2 concentrations using a colorimetric approach with machine vision.

Main Methods:

• Utilized molybdenum oxide (MoOx) nanoparticles with oxygen vacancies for H2O2 detection.
• Employed machine vision and UV-visible spectrophotometry for analysis.
• Characterized the sensing mechanism using XPS, EPR, and DFT.

Main Results:

• Achieved a visible color change (blue to green to yellow) with increasing H2O2 concentrations.
• Demonstrated high sensitivity with a linear detection range of 0.1-600 μmol/L.
• Developed a Hue, Saturation, Value (HSV) visual analysis system for practical H2O2 monitoring.

Conclusions:

• The MoOx nanoparticle-based system provides a sensitive, selective, and portable solution for H2O2 detection.
• This approach offers a low-cost and convenient method for H2O2 determination in various applications.
• The developed technology has significant potential for real-world use in daily life and industry.