Back to Search Start Over

Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning.

Authors :
Yashan Feng
Yafang Tian
Yongxin Yang
Yufang Zhang
Haiwei Guo
Jing'an Li
Source :
Computers, Materials & Continua; 2024, Vol. 79 Issue 1, p263-278, 16p
Publication Year :
2024

Abstract

Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibriumcalculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment. This approach surpasses convention by acknowledging the intricate interplay between the acidity and pH levels within the coating formulation, thereby optimizing metal-phosphate-based conversion coatings and transforming corrosion mitigation strategies. To authenticate the TA/pH concept, the study comprehensively compares its findings with existing research, rigorously validating the theoretical framework and reinforcing the correlates among TA/pH values and observed corrosion resistance in the coatings. The influence of mutations that occur naturally in the detergent solution on persistent phosphorus changes is shown by empirical confirmation, which improves corrosion resistance. This realization advances the field of materials and the field's knowledge of coated generation, particularly anticorrosion converter layers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15462218
Volume :
79
Issue :
1
Database :
Complementary Index
Journal :
Computers, Materials & Continua
Publication Type :
Academic Journal
Accession number :
176916247
Full Text :
https://doi.org/10.32604/cmc.2024.047004