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Record W3203454550 · doi:10.1002/adfm.202107465

Lossless, Passive Transportation of Low Surface Tension Liquids Induced by Patterned Omniphobic Liquidlike Polymer Brushes

2021· article· en· W3203454550 on OpenAlexafffund
Mohammad Soltani, Kevin Golovin

Bibliographic record

VenueAdvanced Functional Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of TorontoOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of New Brunswick
FundersUniversity of Toronto
KeywordsMaterials scienceSurface tensionWettingMicrofluidicsContact angleNanotechnologyHysteresisMerge (version control)PolymerWedge (geometry)Composite materialOpticsComputer science

Abstract

fetched live from OpenAlex

Abstract Surfaces enabling directional liquid transportation are of great interest for a wide range of applications such as water collection, microfluidics, and heat transfer systems. Surfaces capable of lossless, long‐range passive transportation of low surface tension (LST) liquids using wettability patterned, liquidlike coatings with minimal contact angle hysteresis are reported. Lossless LST droplet travel distances over 150 mm are achieved, enabled by a two‐phase transportation mechanism: morphological transformation from a bulge to a channel shape, followed by directional transportation along the asymmetrical wedge‐shaped channel. The developed surfaces can split, merge, and precisely transport various low‐surface tension liquids, including alcohols, alkanes, and solvents. The developed transportation strategy can also enhance LST liquid dropwise condensation through continuous removal of the condensate, even on horizontally positioned surfaces without the assistance of gravity.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations100
Published2021
Admission routes2
Has abstractyes

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