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Record W2065895311 · doi:10.1021/la800729c

A Facile Method of Forming Nanoscale Patterns on Poly(ethylene glycol)-Based Surfaces by Self-Assembly of Randomly Grafted Block Copolymer Brushes

2008· article· en· W2065895311 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

VenueLangmuir · 2008
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCopolymerEthylene glycolAtom-transfer radical-polymerizationMaterials scienceMethacrylatePolymer chemistryNanoscopic scaleContact angleChemical engineeringMethyl methacrylateSelf-assemblyPolymerizationWaferSolventNanotechnologyPolymerChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Poly(oligo(ethylene glycol) methacrylate) (POEGMA) block poly(methyl methacrylate) (PMMA) brushes were synthesized on the silicon wafer surfaces by the surface-initiated atom transfer radical polymerization (ATRP) method. Atomic force microscopy, ellipsometry, and water contact angle methods were employed to study the surface morphology and stimulus-response behavior. It was found that simple solvent treatments could induce phase segregation of the POEGMA and PMMA segments thus introducing nanoscale patterns. The feature size could be less than 10 nm and was tunable on the nanoscale. Various patterns including spherical aggregates, wormlike aggregates, stripe patterns, perforated layers, and complete overlayers were obtained through adjusting the upper block layer thickness. These patterns could switch between the different morphologies reversibly after the treatment with selective solvents.

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 score0.002
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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