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Record W4407923160 · doi:10.1016/j.cej.2025.160929

Superhydrophobic surfaces exhibiting low interfacial toughness with ice

2025· article· en· W4407923160 on OpenAlex
Qimeng Yang, Samuel Au, Zahra Azimi Dijvejin, Kamran Alasvand Zarasvand, Ali Dolatabadi, Kevin Golovin

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

VenueChemical Engineering Journal · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
FundersMinistère de la Défense NationaleCanada Foundation for Innovation
KeywordsToughnessMaterials scienceComposite materialNanotechnology

Abstract

fetched live from OpenAlex

• Three superhydrophobic surfaces were examined for their large-scale de-icing capability. • Mechanically durable micro/nanostructures with high graft density low-surface-energy formed. • Durability and high graft density were essential to observe a low interfacial toughness with ice. • Surface could repel ∼ 40 µL water droplets at low tilting angles under highly condensing conditions. Superhydrophobic surfaces (SHPs) demonstrate superior water repellency and promising surface properties like self-cleaning, anti-fouling, and drag-reducing characteristics. However, it remains unclear whether SHPs can effectively lower the adhesion of ice accumulated on a surface, particularly for large-scale interfaces (>1 cm) where the delamination of ice is controlled by interfacial toughness. Here, three categories of SHP surfaces were evaluated. It was found that the mechanical durability of commercially available SHP sprays was insufficient even for collecting reproducible ice adhesion results using the push-off test. While roughening a piece of a hydrophobic surface like Teflon did increase the water repellency and result in superhydrophobicity, the adhesion of ice on the textured surface was dramatically increased due to mechanical interlocking of the ice. Instead, a SHP surface was designed with both micro- and nano-structures conformally coated by a silica layer, followed by a perfluoropolyether (PFPE) top coat. This SHP, due to its durability and robust Cassie Baxter state, demonstrated promising large-scale ice repellency for the first time, i.e. a low interfacial toughness with ice of 0.6 J/m 2 under freezing conditions at −20 °C, even after more than 40 repeated icing/de-icing tests. Compared to a similar SHP surface without the silica layer, the silica-primed SHP maintained low friction with water droplets under condensing conditions (T = 2 °C, relative humidity > 50 %) for over 10 min. The hierarchical structure in addition to the high grafting density of the PFPE on the silica-primed SHP surface was confirmed by cross-sectional transmission electron microscope imaging and X-ray photoelectron spectroscopy, respectively.

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)0.000
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 score0.028
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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