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Record W4406868670 · doi:10.3390/cryst15020139

Progress in Icephobic Coatings for Wind Turbine Protection: Merging Chemical Innovation with Practical Implementation

2025· article· en· W4406868670 on OpenAlexafffund
Ghazal Minoofar, Amirhossein Jalali Kandeloos, Mohammad Sadegh Koochaki, Gelareh Momen

Bibliographic record

VenueCrystals · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbineTurbine bladeMaterials scienceAutomotive engineeringProcess engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Ice accumulation on wind turbine blades poses a significant challenge to turbine performance and safety, and these issues have led to extensive research on developing effective anti-icing methods. Polymer-based icephobic coatings have emerged as promising solutions, given their passive nature and low energy requirements. However, developing effective icephobic coatings is a complex task. In addition to anti-icing properties, factors such as mechanical strength, durability, and resistance to UV, weathering, and rain erosion must be carefully considered to ensure these coatings withstand the harsh conditions faced by wind turbines. The main challenge in coating engineering is mastering the chemistry behind these coatings, as it determines their performance. This review provides a comprehensive analysis of the suitability of current icephobic coatings for wind turbine applications, emphasizing their alignment with present industrial standards and the underlying coating chemistry. Unlike previous works, which primarily focus on the mechanical aspects of icephobicity, this review highlights the critical yet underexplored role of chemical composition and explores recent advancements in polymer-based icephobic coatings. Additionally, earlier studies largely neglect the specific standards required for industrial applications on wind turbines. By demonstrating that no existing coating fully meets all necessary criteria, this work underscores both the urgency of developing icephobic coatings with improved durability and the pressing need to establish robust, application-specific standards for wind turbines. The review also combines insights from cutting-edge research on icephobic coatings that are coupled with active de-icing methods, known as the hybrid approach. By organizing and summarizing these innovations, the review aims to accelerate the development of reliable and efficient wind energy systems to pave the way for a cleaner and more sustainable future.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.343
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2025
Admission routes2
Has abstractyes

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