Neuro Fuzzy in Predicting the Characteristics of Some Nanomaterials

Abstract

Unveiling the impressive capabilities of the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study effectively predicts key properties of engineered nanomaterials, opening doors to innovative applications across various industries. We initially investigate the cytotoxic effects of TiO2 and ZnO nanoparticles on immortalized human lung epithelial cells, employing ANFIS to establish correlations between nanoparticle size and behaviour in different media and the resulting cellular membrane damage, quantified by lactate dehydrogenase release. Next, to predict the compressive strength of geopolymers, analysing over previous experimental datasets focused on critical chemical ratios. This model demonstrates its capability to optimize formulations for enhanced mechanical performance in sustainable construction materials. Additionally, we apply ANFIS to evaluate the size of silver nanoparticles in montmorillonite/starch bio nanocomposites, identifying significant factors such as AgNO3 concentration. The ANFIS models achieved high accuracy across all applications, underscoring their utility in predicting material behaviour and optimizing formulations for improved performance and safety. Collectively, these findings illustrate the potential of ANFIS as a robust tool in nanomaterial research and development.