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Record W7116318217 · doi:10.1016/j.nanoms.2025.11.016

Microbiologically influenced corrosion of copper-nickel alloys in marine environments: mechanisms, challenges, and mitigation strategies

2025· article· en· W7116318217 on OpenAlexaff
Yanan Pu, Bo Zhang, Caichang Dong, Delin Tang, Hongbo Zeng, Shougang Chen

Bibliographic record

VenueNano Materials Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Shandong ProvinceKey Technology Research and Development Program of ShandongNational Natural Science Foundation of China
KeywordsCorrosionCrevice corrosionCurrent (fluid)Surface engineeringLead (geology)Titanium alloy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.251
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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