Post ferric-substitution detection method optimization for Ni(II)-organic complexes measurement: Simulation,...

Area of Science:

• Analytical Chemistry
• Environmental Science
• Computational Chemistry

Background:

• Quantifying nickel (Ni(II)) complexes, like Ni-EDTA, is challenging due to low concentrations and weak UV absorbance, hindering conventional spectrophotometry.
• Existing Fe(III) substitution methods lack systematic optimization, limiting their sensitivity and practical use in environmental monitoring.

Purpose of the Study:

• To systematically refine and optimize the Fe(III) substitution method for sensitive detection of Ni(II) complexes in environmental samples.
• To enhance the practical application of Ni(II) quantification in water quality analysis.

Main Methods:

• Utilized simulation-guided experimental design, incorporating thermodynamic simulations and Density Functional Theory (DFT) calculations.
• Employed machine learning, including Random Forest Regression (RFR), for variable importance analysis and predictive modeling.
• Applied the optimized method to diverse environmental water samples (surface, ground, wastewater).

Main Results:

• Achieved a low detection limit of 1 × 10⁻³ mM for Ni-EDTA under optimized conditions.
• Demonstrated strong linearity (R² > 0.96) and good matrix tolerance across various water types.
• RFR model (R² = 0.951) identified pH and water bath duration as critical factors for method performance.

Conclusions:

• Successfully developed and optimized a reliable Fe(III) substitution method for environmental Ni(II) complex monitoring.
• The combined simulation and machine learning approach significantly advances water quality analysis.
• Provides a promising strategy for analyzing challenging Ni(II) complexes in complex aqueous environments.