MétaCan
Menu
Back to cohort
Record W2598731932 · doi:10.1039/c6sm02808j

Self-assembly of gradient copolymers synthesized in semi-batch mode by nitroxide mediated polymerization

2017· article· en· W2598731932 on OpenAlexaff
Kevin Wylie, Ian Bennett, Milan Marić

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCopolymerDispersityMonomerNitroxide mediated radical polymerizationPolymerizationMethyl methacrylatePolymer chemistryMaterials scienceStyreneDegree of polymerizationChemical engineeringRadical polymerizationPolymerComposite material

Abstract

fetched live from OpenAlex

The effect of diffuse compositional interfaces on copolymer self-assembly was studied via gradient copolymers (GCP). Poly(methyl methacrylate)-grad-(styrene) (PMMA-grad-PSt) copolymers were synthesized in semi-batch mode using nitroxide-mediated polymerization (NMP) with varied monomer injection protocols to produce varied diffuse interfaces (number average molecular weights (Mn) ranged from 62 000 g mol−1 to 94 000 g mol−1 with dispersities (Đ) between 1.35 and 1.59). The GCPs were spun into thin films on substrates made neutral by (St-ran-MMA-ran-hydroxyethyl methacrylate) terpolymers and annealed at elevated temperature to produce vertically oriented microphase-separated domains. The GCPs were found to have domain spacing larger than equivalent monodisperse BCPs, due to their polydisperse nature. This effect was partially offset by the decrease in χ due to the gradient. GCPs synthesized with a single-injection protocol (i.e. less diffuse interfaces) were found to self-assemble into ordered domains. However, GCPs synthesized with long injection times (i.e. more diffuse interfaces) exhibited poor self-assembly attributed to their predicted statistical-copolymer-like middle sequence, which caused a reduction of the effective enthalpic interaction parameter.

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 categoriesMeta-epidemiology (narrow)
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 score0.012
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.006
GPT teacher head0.238
Teacher spread0.231 · 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.

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

Citations11
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSoft MatterSame topicBlock Copolymer Self-AssemblyFrench-language works237,207