Neuro-Fuzzification Architecture for Modeling of Electrochemical Ion-Sensing Data of Imidazole-Dicarboxylate-Based Ru(II)-Bipyridine Complex.

Abstract

Anion- and pH-sensing behaviors of an imidazole-dicarboxylate-based Ru(II)-bipyridine complex possessing a number of dissociable protons in its secondary coordination sphere are employed here for the creation of multiple Boolean and fuzzy logic systems. The absorption, emission, and electrochemical behaviors of the metalloreceptor were significantly modulated upon the influence of basic anions (such as F-, AcO-, and H2PO4-) as well as by altering the pH of the solution. Interestingly, the deprotonation of the metalloreceptor by selected anions or by alkaline pH, followed by its restoration to its original form by acid or acidic pH is reversible and could be repeated many times. The metalloreceptor is capable to demonstrate several advanced Boolean functions, namely, three-input OR gate, set-reset flip-flop logic, and traffic signal, by employing its electrochemical responses through proper use of different inputs. Administering exhaustive sensing experiments by changing the analyte concentration within a wide range is usually tedious as well as exorbitantly costly. To get rid of these difficulties, we employed here several soft computing approaches such as artificial neural networks (ANN), fuzzy logic systems (FLS), or adaptive neuro-fuzzy inference system (ANFIS) to foresee the experimental sensing data and to appropriately model the protonation-deprotonation behaviors of the metalloreceptor. Reasonably good correlation between the experimental and model output data is also reflected in their tested root-mean-square error values (0.115961 and 0.118894 for the ANFIS model).