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Record W4241557623 · doi:10.1002/ange.201904430

Staged Surface Patterning and Self‐Assembly of Nanoparticles Functionalized with End‐Grafted Block Copolymer Ligands

2019· article· en· W4241557623 on OpenAlexaff
Christian Roßner, Ekaterina B. Zhulina, Eugenia Kumacheva

Bibliographic record

VenueAngewandte Chemie · 2019
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversity of Toronto
FundersAlexander von Humboldt-Stiftung
KeywordsCopolymerSelf-assemblyMicelleNanoparticleStyreneSurface modificationMaterials scienceChemistryPolymer chemistryNanotechnologyPolymerOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Abstract Using two orthogonal external stimuli, programmable staged surface patterning and self‐assembly of inorganic nanoparticles (NPs) was achieved. For gold NPs capped with end‐grafted poly(styrene‐block‐(4‐vinylbenzoic acid)), P(St‐block‐4VBA), block copolymer ligands, surface‐pinned micelles (patches) formed from NP‐adjacent PSt blocks under reduced solvency conditions (Stimulus 1); solvated NP‐remote P(4VBA) blocks stabilized the NPs against aggregation. Subsequent self‐assembly of patchy NPs was triggered by crosslinking the P(4VBA) blocks with copper(II) ions (Stimulus 2). Block copolymer ligand design has a strong effect on NP self‐assembly. Small, well‐defined clusters assembled from NPs functionalized with ligands with a short P(4VBA) block, while NPs tethered with ligands with a long P(4VBA) block formed large irregularly shaped assemblies. This approach is promising for high‐yield fabrication of colloidal molecules and their assemblies with structural and functional complexity.

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 score0.019
Threshold uncertainty score0.985

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)0.0010.000

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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