Speaker
Description
Microorganisms play a pivotal role in corrosion processes, exerting profound effects on the integrity of metallic surfaces across agricultural machinery, transportation infrastructure, and energy systems, leading to substantial economic losses and environmental risks. Depending on the species and environmental context, microbial activity can either accelerate or inhibit corrosion, making their role complex and multifaceted. Understanding the interactions between biofilms, hydrogels, and metallic surfaces is essential for elucidating the mechanisms driving corrosion and for developing effective mitigation strategies. In this work, we introduce a mathematical biology framework for the study of Microbiologically Influenced Corrosion (MIC), based on a spatiotemporal modeling approach. This framework enables systematic investigation of how environmental factors influence microbial activity and corrosion dynamics, while also providing a platform for evaluating potential strategies to inhibit or control MIC. The approach integrates mechanistic modeling with predictive analysis, offering new insights into corrosion processes and informing targeted interventions.
