Harnessing Deep Neural Networks to Analyze Multi-Channel Anion Sensing Characteristics of a Ru(II)-Pyrazolyl-Bis(Benzimidazole) Complex

In this work, the anion-responsive conduct of a Ru(II)-bipyridine complex incorporating pyrazolyl-bis (benzimidazole) ligand is thoroughly investigated in acetonitrile and water via absorption and emission spectroscopy as well as by square-wave voltammetry (SWV). Substantial alteration of the photo-redox behavior of the complex is observed in the presence of the selected anions. The free form of the complex exhibits emission indicating the on-state, while inclusion of anions leads to quenching of emission and represents the off-state. The restoration of the initial state of the complex is feasible in the presence of acid and the process is reversible and can be recycled. In essence, the complex functions as anion- and acid-responsive molecular switches. Additionally, we applied herein neural network based deep learning methodologies, viz. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS)} for thorough analysis and fully understand the multi-channel anion sensing behavior of the complex.

Automated summary

The anion-responsive conduct of a Ru(II)-bipyridine complex incorporating pyrazolyl-bis(benzimidazole) ligand was investigated in acetonitrile and water using absorption and emission spectroscopy and square-wave voltammetry. The photo-redox behavior of the complex was significantly altered in the presence of selected anions, with emission being quenched in the off-state. The complex functions as an anion- and acid-responsive molecular switch, and its anion sensing behavior was thoroughly analyzed using neural network based deep learning methodologies.