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Record W4404794799 · doi:10.1016/j.apsusc.2024.161951

Enhancing anti-icing efficacy in hybrid polyurethane coatings: Evaluating the significance of molecular weight, chemical structure, and content of PEG/PDMS

2024· article· en· W4404794799 on OpenAlex
Mohammad Ali Bakhtiari, Ehsan Bakhshandeh, Reza Jafari, Gelareh Momen

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

VenueApplied Surface Science · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyurethaneMaterials sciencePEG ratioChemical structureComposite materialChemical engineeringChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

• The potential of PEG/PDMS copolymers to significantly enhance the anti-icing properties of polyurethane coatings. • Evaluating the role of PEG/PDMS copolymers in enhancing anti-icing properties. • Identifying a QLL that inhibits ice nucleation and decreases ice adhesion melting in an unfrozen interfacial layer. • Assessing how molecular weight and chemical structure of PEG/PDMS copolymers influence the anti-icing efficacy. This study investigates the advantages of adding polydimethyl siloxane/polyethylene glycol (PEG/PDMS) copolymers to polyurethane coatings, with a particular focus on optimizing anti-icing efficacy. A range of characterization techniques are applied, including attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS) analysis, surface roughness measurements, wettability analysis, tensile testing, and ice adhesion measurements, to elucidate the intricate relationships between copolymer molecular weight, chemical structure, and content and their collective effect on the anti-icing properties of the developed coatings. Tailored PEG/PDMS copolymers significantly reduce ice nucleation temperatures and enhance the anti-icing properties of polyurethane coatings. Adding PEG/PDMS copolymers to polyurethane alters the surface roughness, wettability, and mechanical properties of the coatings to improve anti-icing performance. The presence of copolymers decreases ice adhesion strength (<50 kPa), attributed to the formation of a quasi-liquid layer that acts as a lubricant between the ice and the coatings, and delays ice formation. Furthermore, the enhanced durability of copolymer-containing coatings ensures a long-lasting anti-icing effect after multiple icing/de-icing cycles, although some degradation was observed over time. The tailored PEG/PDMS copolymers demonstrate potential for maximizing the anti-icing properties of polyurethane coatings and advancing anti-icing technologies.

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)0.003
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 score0.011
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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