Analysis and prediction of anion- and temperature responsive behaviours of luminescent Ru(ii)-terpyridine complexes by using Boolean, fuzzy logic, artificial neural network and adapted neuro fuzzy inference models

Abstract

Anion- and temperature responsive behaviors of three luminescent Ru(II)-terpyridine complexes are utilized here to demonstrate multiple Boolean (BL) and fuzzy logic (FL) operations. Taking advantage of the imidazole NH protons, anion-promoted alteration of the photophysical characteristics of the complexes was thoroughly investigated via absorption, and emission spectral and lifetime measurements. In their free state, the complexes display luminescence representing the “on-state”, whereas quenching of luminescence is observed with anions demonstrating the “off-state”. Likewise, lowering of temperature induces a substantial increase of luminescence and lifetime demonstrating the “on-state”, while the increase of temperature induces a significant decrease of emission intensity and lifetime indicating the “off-state” and the process is reversible in both cases. The complexes thus can act as anion- and temperature-responsive molecular switches. The spectral signatures of the complexes under the influence of anions and temperature were employed to mimic multiple BL and FL functions. Performing very detailed sensing studies by varying the analyte concentration within a wide domain is very tedious, time-consuming and expensive. In order to overcome the lacuna, we implemented machine learning and soft computing tools such as artificial neural networks (ANNs), fuzzy-logic and adaptive neuro-fuzzy inference system (ANFIS) to predict the experimental anion sensing data of the complexes.