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Record W4200383678 · doi:10.1002/anie.202115238

Sustainable Superhydrophobic Surface with Tunable Nanoscale Hydrophilicity for Water Harvesting Applications

2021· article· en· W4200383678 on OpenAlex

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

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersUniversity Postgraduate ProgrammeNatural Sciences and Engineering Research Council of Canada
KeywordsNanoscopic scaleMaterials scienceContact angleNanotechnologyWettingChemical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Superwettable surfaces show great potential in water harvesting applications, however, a scalable water harvesting surface remains elusive due to the trade‐off between water deposition and transport. Herein, we report a unique superhydrophobic surface with tunable nanoscale hydrophilicity constructed by structured Pickering emulsions. Preferential exposure of the cellulose nanocrystal's outer surface and wax microspheres accelerates droplet deposition allowing for the manipulation of droplet mobility. Appropriate tuning of the wetting characteristics of the surfaces, optimizing the hydrophobicity and density of the water affinity nanodomains enhance the water deposition rate without the sacrifice of water transport rate, achieving an optimal water harvesting flux of 3.402 L m −2 h −1 for a plate and 5.02 L m −2 h −1 for a mesh. This hydrophilic/superhydrophobic surface allows the controllable manipulation of droplet nucleation and removal to enhance the water harvesting efficiency.

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 categoriesInsufficient payload (model declined to judge)
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 score0.076
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.243
Teacher spread0.229 · 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