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Record W1976727335 · doi:10.1021/la0629274

Ordered CdSe Nanoparticles within Self-Assembled Block Copolymer Domains on Surfaces

2007· article· en· W1976727335 on OpenAlexafffund
Shan Zou, Ruijiang Hong, Todd Emrick, Gilbert C. Walker

Bibliographic record

VenueLangmuir · 2007
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversity of Toronto
FundersArmy Research OfficeDivision of ChemistryNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of CanadaOffice of Naval ResearchMultidisciplinary University Research InitiativeNational Institutes of HealthNational Science Foundation
KeywordsCopolymerNanoparticleTrioctylphosphine oxideMaterials scienceScanning electron microscopePolystyreneChemical engineeringNanotechnologySelf-assemblyPolymer chemistryChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Hierarchical, high-density, ordered patterns were fabricated on Si substrates by self-assembly of CdSe nanoparticles within approximately 20-nm-thick diblock copolymer films in a controlled manner. Surface-modified CdSe nanoparticles formed well-defined structures within microphase-separated polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) domains. Trioctylphosphine oxide (TOPO)-coated CdSe nanoparticles were incorporated into PS domains and polyethylene glycol-coated CdSe nanoparticles were located primarily in the P2VP domains. Nearly close-packed CdSe nanoparticles were clearly identified within the highly ordered patterns on Si substrates by scanning electron microscopy (SEM). Contact angle measurements together with SEM results indicate that TOPO-CdSe nanoparticles were partially placed at the air/copolymer interface.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2007
Admission routes2
Has abstractyes

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