AUTHOR=Sahoo Anik , Baitalik Sujoy 

TITLE=Fuzzy Logic, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference Methodology for Soft Computation and Modeling of Ion Sensing Data of a Terpyridyl-Imidazole Based Bifunctional Receptor

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2022.864363

DOI=10.3389/fchem.2022.864363

ISSN=2296-2646

ABSTRACT=Anion- and cation sensing aspects of a terpyridyl-imidazole based receptor has been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, while the imidazole motif is utilized for anion sensing via hydrogen bonding interaction and/or anion-induced deprotonation and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as selective sensor for F- and Fe2+ among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe2+ and its decomplexation by F- as well as deprotonation of the receptor by F- and restoration to its initial form by acid is reversible and can be recycled. The receptor is able to mimic various logic operations such as combinatorial logic gate and Keypad lock by using its spectral responses through sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time consuming, laborious and expensive. To decrease the time and expenses of the investigations, soft computing approaches like Artificial Neural Networks (ANNs), Fuzzy-logic or Adaptive Neuro-Fuzzy Inference System (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to Artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation and other tools based on statistical as well as mathematical optimizations. In this study, the comparison of  Fuzzy, ANN and ANFIS outputs are applied for modeling of the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions (trimf) are used here for modelling the ANFIS methodology. Good correlation is observed between experimental and model output data. The testing root mean square errors (RMSE) for ANFIS model are 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.