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Record W3005013378 · doi:10.1002/adfm.201907772

Recent Progress and Future Directions of Multifunctional (Super)Wetting Smooth/Structured Surfaces and Coatings

2020· article· en· W3005013378 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.

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsQueen's University
Fundersnot available
KeywordsNanotechnologyMaterials scienceWettingFocus (optics)Biochemical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Research on superwetting surfaces/coatings that artificially mimic biological surfaces/systems has a long history, and still garners significant worldwide interest as it is expected to provide superior solutions to conventional engineering approaches that attempt to solve challenges facing mankind. To broaden the utility of these superwetting surfaces/coatings, there is a strong demand for these surfaces to exhibit multiple practical functionalities. Here, the progress being made in multifunctional surfaces with superwettability is explored. In each section, state‐of‐the‐art works are summarized and the concepts, materials, processes, and the effects of both physical (smooth or structured surfaces) and chemical (low or high surface energies) factors on the resulting surface are described. Finally, the outlook of this prospective research field is considered, and its future directions briefly discussed, with a focus on preserving longevity in both functionality and structural integrity to produce truly useful biomimetic surfaces/coatings.

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 score0.095
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 communication0.0000.000
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 head0.020
GPT teacher head0.243
Teacher spread0.222 · 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