Intelligent corrosion control 2023
The global losses due to corrosion, remain both staggering and increasing; giving rise to demands for stronger, smarter and more sustainable anti-corrosion technologies. In this regard, smart materials that can respond to mechanical loads, to oxidation or degradation, or to the variation of external conditions (temperature, light, humidity, etc.) are receiving wide interest in the development of next-generation coatings/surfaces capable of sensing, reporting and healing corrosion damage with minimal human intervention. On another front, the emergent research field associated with the ‘materials genome engineering’ has spurred development in high-throughput experiments as well as computational and data-driven approaches - which are transforming the corrosion research paradigm. For example, machine learning models are revealing their power to diagnose corrosion mechanisms and more accurately predict corrosion progress under complex influencing factors. Digital and intelligent tools are replacing traditional ‘trial-and-error’ methods for design and screening corrosion-resistant materials according to the specific requirements from service environments.
This new themed Collection of npj Materials Degradation aims to report important advances for intelligent corrosion control (inclusive of intelligent systems in corrosion) in the broadest sense of the term, by gathering original research articles, review papers and perspectives including but not limited to the following topics:
- Smart coatings/surfaces with self-healing, corrosion-sensing or other functional properties;
- Advanced computational modeling for design/evaluation of protective coatings/surfaces;
- High-throughput/automated experiments for corrosion evaluation and materials design;
- Corrosion databases and machine learning for corrosion detection, diagnosis and predictive maintenance;
All submissions will be subject to the same rigorous peer-review process and editorial standards as regular npj Materials Degradation articles. Review articles and perspectives are by invitation only. The Guest Editors declare no competing interests with the submissions which they have handled through the peer-review process.
Since the publication of this Collection, a new Collection has been launched to capture the latest updates in this research area: Intelligent corrosion control 2024
Editors
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Dawei Zhang
Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, University of Science and Technology Beijing.
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Mikhail Zheludkevich
Institute of Surface Science, Helmholtz-Zentrum Hereon, Germany
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Ingrid Milošev
Department of Physical and Organic Chemistry, Jožef Stefan Institute in Ljubljana, Slovenia
