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Record W2582943917 · doi:10.3390/app7020130

Supercooled Water Droplet Impacting Superhydrophobic Surfaces in the Presence of Cold Air Flow

2017· article· en· W2582943917 on OpenAlexaff
Morteza Mohammadi, Moussa Tembely, Ali Dolatabadi

Bibliographic record

VenueApplied Sciences · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsConcordia University
Fundersnot available
KeywordsSupercoolingWettingMaterials scienceDrop (telecommunication)Contact angleAir waterIce nucleusNucleationThermodynamicsMechanicsComposite materialPhysics

Abstract

fetched live from OpenAlex

In the present work, an investigation of stagnation flow imposed on a supercooled water drop in cold environmental conditions was carried out at various air velocities ranging from 0 (i.e., still air) to 10 m/s along with temperature spanning from −10 to −30 °C. The net effect of air flow on the impacting water droplet was investigated by controlling the droplet impact velocity to make it similar with and without air flow. In cold atmospheric conditions with temperatures as low as −30 °C, due to the large increase of both internal and contact line viscosity combined with the presence of ice nucleation mechanisms, supercooled water droplet wetting behavior was systematically affected. Instantaneous pinning for hydrophilic and hydrophobic surfaces was observed when the spread drop reached the maximum spreading diameter (i.e., no recoiling phase). Nevertheless, superhydrophobic surfaces showed a great repellency (e.g., contact time reduction up to 30% where air velocity was increased up to 10 m/s) at temperatures above the critical temperature of heterogeneous ice nucleation (i.e., −24 °C). However, the freezing line of the impacting water droplet was extended up to 2-fold at air velocity up to 10 m/s where substrate temperature was maintained below the aforementioned critical temperature (e.g., −30 °C).

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)0.004
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 score0.007
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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