Microbiologically influenced corrosion of copper-nickel alloys in marine environments: mechanisms, challenges, and mitigation strategies
Bibliographic record
Abstract
Copper-nickel (Cu-Ni) alloys are extensively employed in marine engineering and industrial systems due to their exceptional corrosion resistance and mechanical strength. Despite these advantages, the microbiologically influenced corrosion (MIC) in anaerobic environments remains a critical challenge to their long-term stability. The failure mechanisms of Cu-Ni alloys in service are highly complex, often involving the interplay of physical, chemical, and biological processes. The non-steady-state and nonlinear nature of surface and interfacial degradation, amplified by multi-medium and multi-scale coupling effects, has hindered a comprehensive understanding of the underlying mechanisms. This review systematically examines the impact of alloy material characteristics (e.g., elemental composition, surface roughness), environmental factors (e.g., flow rate, sulfur pollution), and service conditions (e.g., external stress, dissimilar metal connections, and crevice structures, etc.) on the MIC behavior of Cu-Ni alloys. Emphasis is placed on the multifactorial coupling mechanisms and the coexistence of dual MIC pathways, including Ni-mediated EET-MIC and Cu-mediated M-MIC, which together govern the corrosion progression. Furthermore, it provides a detailed overview of the current advancements in MIC detection and monitoring technologies, as well as a systematic summary of widely adopted MIC protection strategies, including optimization of material surface treatment processes, development of advanced antimicrobial coatings, application of microbiological inhibitors, and implementation of electrochemical interventions to mitigate corrosion. Future research directions are proposed, focusing on multiscale mechanistic analysis, the development of novel antimicrobial materials, and the design of integrated and intelligent protective systems for practical applications. By providing a comprehensive synthesis of current knowledge and emerging strategies, this review aims to serve as a robust theoretical foundation for enhancing the long-term reliability of Cu-Ni alloys and as a valuable reference for both academic research and industrial practice in MIC mitigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".