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Record W4246068781 · doi:10.1002/anie.201914201

Athermal and Soft Multi‐Nanopatterning of Azopolymers: Phototunable Mechanical Properties

2019· article· en· W4246068781 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

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNanoimprint lithographyMaterials scienceFabricationPhotolithographyNanotechnologySoft lithographyPolymerLithographyNanolithographyGlass transitionOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Abstract Imprinting nanopatterns on flexible substrates has diverse applications in advanced fabrication. However, the traditional thermal nanoimprint lithography (T‐NIL) often causes shrinkage upon cooling. Here, a simple yet versatile method is introduced to fabricate multiple nanopatterns on a flexible substrate coated with an azopolymer by combining athermal nanoimprint lithography (AT‐NIL) and photolithography. The azopolymer has various mechanical properties upon photoirradiation: 1) phototunable glass‐transition temperatures ( T g ) and concomitantly photoinduced switch from glassy plastic to viscoplastic polymer; 2) prominent modulation of viscoplasticity under light illumination at different wavelengths. Regionally selective multiple nanopatterns are conveniently fabricated, presenting angle‐dependent structural color images on poly(ethylene terephthalate) (PET) substrates. The flexible, athermal and multiple nanopatterning method has the potential for on‐demand fabrication of complex nanopatterns.

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 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 score0.353

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 head0.011
GPT teacher head0.214
Teacher spread0.202 · 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