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Record W2790753918 · doi:10.1002/macp.201700608

Large Area, Highly Transparent, and Mechanically Stable Adhesive Films with Tunable Refractive Indices

2018· article· en· W2790753918 on OpenAlexfundno aff
Dan Wang, Jianfu Zhang, Yuanyuan He, Wenfei Li, Shitao Li, Xiuhua Fu, Ming Tian, Yang Zhou, Zhanhai Yao

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsnot available
FundersUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsAdhesiveMaterials scienceCationic polymerizationRefractive indexScanning electron microscopeAcrylic acidLayer (electronics)Composite materialPolymer chemistryOptical microscopeChemical engineeringPolymerOptoelectronicsCopolymer

Abstract

fetched live from OpenAlex

Abstract Optical bonding with both excellent mechanical and optical properties is highly desirable for many advanced optical device applications. This paper presents a facile method for fabricating large‐area, highly transparent, and mechanically stable adhesive films with a tunable refractive index (RI) by the layer‐by‐layer (LbL) assembly of cationic branched poly(ethylenimine) (PEI) and an anionic blend of poly(acrylic acid) (PAA) and poly(4‐styrenesulfonic acid) (PSS). Scanning electron microscopy and atomic force microscopy studies indicate that the resulting (PEI/PAA–PSS)* n adhesive films with n PEI/PAA–PSS deposition layers are smooth and homogeneous. A satisfactory bonding strength is demonstrated for both two‐sided and one‐sided bonding methods, which provide a bonding strength greater than 5.71 ± 0.71 MPa. A linearly tunable RI from 1.5410 to 1.5792 is achieved with a transparency greater than 94% in the visible region. Moreover, the preparation and debonding of adhesive films can be conducted in water, which is convenient and environmentally friendly.

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 score0.009
Threshold uncertainty score0.718

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 head0.012
GPT teacher head0.245
Teacher spread0.232 · 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
Published2018
Admission routes1
Has abstractyes

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