Effect of Voltage, Concentration and Temperature on Aluminum Oxide Nanopores using Fuzzy Inference System
The Nano applications demand customized nanoporous template. The morphology of such membranes is exclusively inclined to anodizing parameters. Getting suitable nanostructures involves many trials of experiments, which is time-consuming and very expensive. In the proposed study, the predictive system is developed using the statistical method and rule-based fuzzy inference technique that can forecast the settings of the anodizing parameters to attain the desirable morphology of the nanomembrane suitable for the required application. The statistical and fuzzy results are compared with programmed geometrical and manual results obtained from all the FESEM images, considered in the experiment with varying anodizing parameters, namely; voltage (30V to 50V ), concentration (3% to 6% ) and temperature (150C to 300C). It is observed that the results obtained from the geometrical analysis, statistical analysis and fuzzy analysis for varying voltage in the experimental images for wall thickness and nanopore size, the values are ranging from 35nm to 46nm and 8nm to 41nm, respectively. The concentration variation shows wall thickness ranging from 52nm to 49nm and pore size from 55nm to 61nm. Change in temperature showcase wall thickness range from 49nm to 58nm and pore size from 58nm to 85nm and are almost similar to the manual results obtained from the chemist. This proves the efficacy of the proposed study.