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Record W2989650894 · doi:10.1126/science.aax9075

Tailored multifunctional micellar brushes via crystallization-driven growth from a surface

2019· article· en· W2989650894 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

VenueScience · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Victoria
FundersScience and Technology Commission of Shanghai MunicipalityShanghai Municipal Education CommissionNational Natural Science Foundation of China
KeywordsSurface modificationMaterials sciencePolymerCrystallizationNanotechnologyMicelleColloidal goldChemical engineeringCopolymerOxideNanoparticlePolymer chemistryChemistryOrganic chemistryAqueous solutionComposite material

Abstract

fetched live from OpenAlex

The creation of nanostructures with precise chemistries on material surfaces is of importance in a wide variety of areas such as lithography, superhydrophobicity, and cell adhesion. We describe a platform for surface functionalization that involves the fabrication of cylindrical micellar brushes on a silicon wafer through seeded growth of crystallizable block copolymers at the termini of immobilized, surface-confined crystallite seeds. The density, length, and coronal chemistry of the micellar brushes can be precisely tuned, and post-growth decoration with nanoparticles enables applications in catalysis and antibacterial surface modification. The micellar brushes can also be grown on ultrathin two-dimensional materials such as graphene oxide nanosheets and further assembled into a membrane for the separation of oil-in-water emulsions and gold nanoparticles.

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 categoriesInsufficient payload (model declined to judge)
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 score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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 head0.237
Teacher spread0.227 · 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