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Record W3159823243 · doi:10.1002/mats.202100014

Self‐Assembly of Nonfrustrated ABCBA Linear Pentablock Terpolymers

2021· article· en· W3159823243 on OpenAlexaff
Yun‐Tse Lo, Chin‐Hung Chang, Hsuan‐Hung Liu, Ching‐I Huang, An‐Chang Shi

Bibliographic record

VenueMacromolecular Theory and Simulations · 2021
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcMaster University
FundersMinistry of Science and Technology, Taiwan
KeywordsCopolymerStyreneIsopreneEthylene oxideMaterials scienceMatrix (chemical analysis)Polymer chemistryPhase (matter)Polymer sciencePolymerChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The effects of compositions, interaction parameters, and number of blocks on the self‐assembly of nonfrustrated ABCBA linear pentablock terpolymer melts are investigated and compared with the corresponding ABC linear triblock terpolymers using the self‐consistent field theory. For the case of symmetric interaction parameters ( χ AB N = χ BC N ) and majority B‐blocks, both ABC triblocks and ABCBA pentablocks can form intriguing A‐ and C‐domains embedded in the B‐matrix. While these morphologies persist in the ABC systems when the segregation strength is decreased, the A blocks tend to mix with the B blocks in the ABCBA pentablocks due to the chain topology, resulting in the formation of C‐domains embedded in the AB‐mixed matrix. Different phase behavior is observed for the case of asymmetric interaction parameters ( χ AB N < χ BC N ). Aside from the core–shell type structures, a number of morphologies arise due to the separation between C and AB‐mixed domains in ABCBA pentablock copolymers. The theoretical results not only capture the self‐assembling behavior of poly(isoprene‐ b ‐styrene‐ b ‐ethylene oxide‐ b ‐styrene‐ b ‐isoprene) pentablocks, but also provide concrete suggestions for designing diverse microstructures in the linear ABC‐type terpolymers.

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 score0.020
Threshold uncertainty score0.877

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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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