An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions

ABSTRACT

The Quantitative Ion Character–Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (q_max) of multi-walled carbon for heavy metals. The experimental values of q_max for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with q_max values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method–orthogonal correction signal–PLS model, with RMSEC = 0.733, r²_c = 0.999 and r²_p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.

KEYWORDS:

• Maximum adsorption capacity
• quantitative ion character–activity relationships
• heavy metal ions
• partial least squares
• enhanced replacement method
• orthogonal signal correction

Disclosure statement

No potential conflict of interest was reported by the authors.