Gapsari, Femiana and Utaminingrum, Fitri and Lai, Chin Wei and Anam, Khairul and Sulaiman, Abdul M. and Haidar, Muhamad F. and Julian, Tobias S. and Ebenso, Eno E. (2024) A convolutional neural network-VGG16 method for corrosion inhibition of 304SS in sulfuric acid solution by timoho leaf extract. Journal of Materials Research and Technology-JMR&T, 30. pp. 1116-1127. ISSN 2238-7854, DOI https://doi.org/10.1016/j.jmrt.2024.03.156.
Full text not available from this repository.Abstract
A corrosion inhibition test, coupled with a quantification of in-situ H2 evolution, can be used to evaluate an organic inhibitor such as Timoho leaf extract (TLE). TLE is a biodegradable and effective corrosion inhibitor because of its potential to protect 304SS against sulfuric acid. TLE corrosion inhibitor was studied through systematic electrochemical experiments and morphological characterization, with a concentration range of 0-6g L-1. Convolutional Neural Network (CNN)-VGG16 was one of the machine learning approaches used to classify and predict physical changes in hydrogen gas bubbles. Constituents of the TLE and 304SS surfaces were analyzed by FT-IR and UV-Vis tests. The results suggested that 3 g L-1 TLE inhibitor was able to reduce the corrosion rate by 99.37 %. The TLE's inhibition mechanism on 304SS was mixed adsorption and mixed type inhibitor that followed the Isothermal Freundlich Equation. The prediction model by CNN-VGG16 for corrosion tests at varied inhibitor doses was 96% accurate. SEM tests revealed that TLE constituent adsorption on the 304SS surface had a smooth surface morphology with few degraded spots.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Research and Technology of the Republic of Indonesia (RISTEK) |
| Uncontrolled Keywords: | Convolutional neural network (CNN); Corrosion inhibitor; Machine learning; Timoho leaf extract; VGG16 |
| Subjects: | Q Science > Q Science (General) Q Science > QD Chemistry |
| Divisions: | Deputy Vice Chancellor (Research & Innovation) Office > Nanotechnology & Catalysis Research Centre |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Sep 2024 07:03 |
| Last Modified: | 30 Sep 2024 07:03 |
| URI: | http://eprints.um.edu.my/id/eprint/45263 |
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