Predictive Models for Characterizing and Evaluating Risk of Engineered Nanomaterials Using Computational Intelligence Approach

Odedele TO and Ibrahim HD

Published on: 2022-06-20


Nanotechnology is the fabrication and manipulation of novel material at a size of 100 nm or less and is increasingly becoming a promising area in various human endeavors because of its novel and unique characteristics. Nano-materials are commonly applied in medicine, Engineering, and agricultural industries. The unique properties of these materials can be manipulated for beneficial purposes and at the same time may also have side effects through toxicological and environmental impacts. Considering the Environmental and Health implications, nanomaterials could be harmful because of their distribution through the environment, aquatic, and human systems. Their novel and unique properties have made its transportation and distribution easy into human body system through the skin, lungs, and gastrointestinal tract. However, many toxicological studies have shown the inherent toxicity of some nanoparticles to living organisms, and their potentially harmful effects on the environment and aquatic systems (ecotoxicity) for which relatively tedious animal testing procedures have been documented for their characterization. Considering the increasing number of nanoparticles manufactured and the variety of their intrinsic properties especially sizes, and coatings, it is, therefore, necessary to explore an alternative approach that avoids conducting a test on every nanoparticle produced. The apprehensions of the potentially harmful effects of nanomaterials constitute a serious setback to nanotechnology commercialization. The objective of the study is to develop a screening protocol to assess, evaluate, and manage the inherent risks. To achieve this, it is imperative to develop models, tools, and an acceptable mechanism for screening, predicting, and monitoring the application of nanomaterials. Given these side effects, there is therefore the need to design and develop classification and nanomaterials toxicity predictive models using computational intelligent and adaptive hybrid systems. This paper, therefore, focuses on the capability of machine learning, fuzzy logic and evolutionary computing techniques to model physicochemical properties and toxic effects of nanomaterials. Furthermore, computational intelligent systems are applied to predict/assess the toxicity of nanomaterials. Hence, the main motivation of this research work is to assist the users of nanomaterials in classifying, assessing, and determining the risk of nanomaterials toxicity.