Design and optimization of highly sensitive and tunable nanostructure biosensor for heavy metal detection using...

Area of Science:

• Biomedical Engineering
• Environmental Science
• Analytical Chemistry

Background:

• Copper (Cu²⁺) ions are essential but toxic at high concentrations, necessitating accurate detection methods.
• Conventional biosensors face limitations in sensitivity and specificity for heavy metal detection.
• Optical Surface Plasmon Resonance (SPR)-based refractive index sensors offer rapid and selective detection of Cu²⁺.

Purpose of the Study:

• To design and evaluate a Four-Quadrant Circular Grid Refractive Index Biosensor (FQCGRIB) for Cu²⁺ detection.
• To integrate a machine learning approach for enhanced heavy metal detection accuracy and efficiency.
• To assess the sensor's performance in terms of sensitivity, detection range, quality factor, and detection limit.

Main Methods:

• Design of a Four-Quadrant Circular Grid Refractive Index Biosensor (FQCGRIB).
• Utilizing Surface Plasmon Resonance (SPR) principles for refractive index sensing.
• Application of a machine learning model for data analysis and prediction of Cu²⁺ concentrations.

Main Results:

• Achieved significant sensitivity values ranging from 719.85 to 763.35 nm/RIU for different Cu²⁺ concentrations.
• Obtained a wide detection range (1175.14–1189.56) and high quality factors (827.72–843.21 nm/RIU).
• The machine learning approach yielded a high predicted value (0.981494) with a low mean square error (0.001987) for Cu²⁺ detection.

Conclusions:

• The FQCGRIB demonstrates enhanced accuracy, sensitivity, specificity, and detection efficiency for Cu²⁺ ions.
• The integrated machine learning approach significantly improves the predictive performance for heavy metal detection.
• The compact sensor design offers a promising solution for sensitive and efficient heavy metal ion monitoring.