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Water-induced failure in polymer coatings: Mechanisms, impacts and mitigation strategies—A comprehensive review

2024· review· en· W4404056014 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymer Degradation and Stability · 2024
Typereview
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsDalhousie University
FundersMitacsDalhousie University
KeywordsPolymerMaterials scienceEnvironmental scienceComposite material

Abstract

fetched live from OpenAlex

• Water-induced failure mechanisms in polymer coatings are reviewed • Diffusion processes and water-polymer interactions are elaborated • Distinct and interrelated impacts of damage mechanisms are discussed • Mitigation strategies for reducing coating damage are then provided • Finally suggestions are made for future research avenues Among the various strategies implemented to improve the integrity of metallic structures, polymer coatings have emerged as a compelling choice due to their capacity to offer cost-effective and enduring protection. However, humidity is a critical environmental factor that poses a risk to the integrity of polymer coatings, manifesting in dimensional alterations, induced internal stresses, diminished adhesion strength between coating and substrate, microstructural changes, and hydrolytic degradation. Many of these detrimental changes remain concealed until the advanced stages of coating failure. Comprehending the water-induced degradation mechanisms in polymer coatings is crucial for improving their protective effectiveness, ensuring safety and dependability, minimizing economic and environmental impacts, and promoting innovation and regulatory compliance. This study rigorously explores the water-induced failure mechanisms in polymeric coatings, highlighting both visible signs of degradation, such as corrosion, cathodic delamination, and blistering, and less apparent phenomena like hydrolysis, swelling, and plasticization. From the initial stages of water diffusion to the eventual delamination of the coating, diffusion mechanisms and the interaction between water molecules and coating constituents are scrutinized. Moreover, this review explores the distinctive and interrelated impacts of each phenomenon on the integrity of the coating, along with potential mitigation strategies. The review culminates with practical recommendations aimed at bolstering the integrity of coated structures.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.047
GPT teacher head0.317
Teacher spread0.270 · 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