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Published on: January 23, 2013
Semi-Automatic System for ZnO Nanoflakes Synthesis via Electrodeposition Using Bioinspired Neuro-Fuzzy Control.
Yazmín Mariela Hernández-Rodríguez1, Yunia Veronica Garcia-Tejeda1, Esperanza Baños-López2
1Department Advanced Technologies, UPIITA-Instituto Politécnico Nacional, Av. IPN 2580, CDMX, Ciudad de Mexico C.P. 07340, Mexico.
This study introduces a semi-automatic electrophoretic deposition (EPD) system for zinc oxide (ZnO) microstructures, using an adaptive neuro-fuzzy inference system (ANFIS) controller. The ANFIS system accurately predicts optimal conditions for ZnO nanoflake synthesis, enhancing process control and material properties.
Area of Science:
- Materials Science and Engineering
- Chemical Engineering
- Control Systems
Background:
- Electrophoretic deposition (EPD) is a versatile technique for fabricating ceramic coatings.
- Controlling EPD parameters like temperature is crucial for achieving desired microstructure and properties.
- Automation and intelligent control strategies can optimize EPD processes for enhanced material synthesis.
Purpose of the Study:
- To develop and characterize a semi-automatic EPD system for zinc oxide (ZnO) microstructure synthesis.
- To implement and evaluate a bioinspired adaptive neuro-fuzzy inference system (ANFIS) for optimizing EPD parameters.
- To demonstrate the system's accuracy in predicting optimal conditions for ZnO nanoflake deposition.
Main Methods:
- A semi-automatic EPD system was designed using a potentiostate-controlled chemical reactor.
- An ANFIS controller was developed and trained using experimental data correlating deposition parameters with ZnO coating properties (thickness, porosity).
- Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), and Energy-Dispersive X-Ray Spectroscopy (EDS) were used for material characterization.
Main Results:
- The synthesized ZnO coatings exhibited a flake-like morphology with uniform composition.
- The ANFIS controller demonstrated high accuracy in predicting optimal deposition temperatures for specific ZnO flake densities.
- Temperature-dependent variations in thickness and porosity were classified into four working sets based on ZnO flake density.
Conclusions:
- The bioinspired ANFIS control strategy significantly improves the adaptability and predictive power of the EPD process.
- The developed system offers a versatile tool for producing ZnO microstructures with precise characteristics.
- Future work includes refining the control system and expanding its application to other materials and complex deposition scenarios.
