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Record W2066003717 · doi:10.1039/c4sm02584a

Swelling-induced surface instability patterns guided by pre-introduced structures

2015· article· en· W2066003717 on OpenAlexaff
Qingguo Du, Ying Guan, X. X. Zhu, Yongjun Zhang

Bibliographic record

VenueSoft Matter · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversité de Montréal
FundersProgram for New Century Excellent Talents in UniversityNational Natural Science Foundation of China
KeywordsInstabilityMaterials scienceSwellingHexagonal crystal systemPattern formationMorphology (biology)WavelengthSurface (topology)Line (geometry)Substrate (aquarium)NanotechnologyChemical physicsComposite materialCrystallographyGeometryGeologyOptoelectronicsChemistryMechanicsMathematicsPhysics

Abstract

fetched live from OpenAlex

Swelling-induced, spontaneously generated surface instability patterns in substrate-attached hydrogel films can be harnessed for advanced applications, however, methods to control their formation and morphology are missing. Here we propose that their generation may be guided by intentionally pre-introduced line structures. While uniform gel films produce irregular polygonal instability patterns, instability patterns generated in pre-patterned films with hexagonal line structures are regular hexagons with long-range order. The pre-introduced line structures act as defects in the generation of the surface instability patterns, which determine the position of the creases, regulate their rearrangement and determine their final morphology. The contrast between the pre-introduced structures and the surrounding area should be high enough for the pre-introduced structures to act as defects. Only when the characteristic wavelength of the pre-introduced pattern matches with the one of the gel film, perfect hexagonal patterns can be obtained. The gel films with uniform topographic features may find various advanced applications.

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)0.000
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 score0.037
Threshold uncertainty score0.829

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 head0.019
GPT teacher head0.246
Teacher spread0.226 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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