Discriminating Analysis of Metal Ions via Multivariate Curve Resolution-Alternating Least Squares Applied to Silver...

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Nanotechnology

Background:

• Heavy metals pose significant environmental and health risks due to their toxicity and persistence.
• Bioaccumulation of heavy metals in organisms is a major concern for ecosystem and human health.
• Accurate and rapid detection methods for heavy metals in water are crucial for environmental safety.

Purpose of the Study:

• To develop a sensitive and selective method for detecting multiple heavy metals in water.
• To utilize a colorimetric sensor functionalized with silver nanoparticles for heavy metal analysis.
• To apply multivariate curve resolution-alternating least squares (MCR-ALS) for spectral data analysis.

Main Methods:

• Fabrication of a colorimetric sensor using mercaptoundecanoic acid functionalized silver nanoparticles (AgNPs@11MUA).
• Acquisition of UV-Vis raw spectra from water samples containing heavy metals.
• Analysis of spectral data using multivariate curve resolution-alternating least squares (MCR-ALS).

Main Results:

• The developed sensor successfully identified and quantified Cd2+, Cu2+, Mn2+, Ni2+, and Zn2+ in water samples.
• The MCR-ALS analysis demonstrated adequate sensitivity and selectivity for heavy metal detection.
• The combined sensor and analysis approach proved effective for multi-analyte heavy metal determination.

Conclusions:

• The AgNPs@11MUA sensor combined with MCR-ALS is a promising analytical tool for heavy metal detection.
• This method offers rapid, sensitive, and selective quantification of multiple heavy metals in aqueous environments.
• The approach is suitable for both laboratory and field applications, aiding environmental safety and public health monitoring.