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Record W4414331015 · doi:10.1021/acs.macromol.5c01801

Binary Macromolecular Mesocrystals via Designed Block Copolymer Blends

2025· article· en· W4414331015 on OpenAlexafffund
Jiayu Xie, Junyin Zhang, Feiyan Wu, Yiwang Chen, An‐Chang Shi

Bibliographic record

VenueMacromolecules · 2025
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCopolymerMacromoleculeBlock (permutation group theory)PolymerPolymer blend

Abstract

fetched live from OpenAlex

The theoretical prediction that various binary macromolecular mesocrystals composed of A and C spheres could be formed by B 1 AB 2 CB 3 pentablock terpolymers ( JACS 2014, 136, 2974–2977) offers a promising route to fabricate these intricately structured nanomaterials. However, experimental realization of this strategy has been impeded by the requirement of synthesizing precisely designed pentablock terpolymers. Here, we propose a conceptually new and technically simpler route to engineer binary macromolecular mesocrystals by using BA′/ABC/C′B ternary block copolymer blends that are designed to replicate the phase behavior of B 1 AB 2 CB 3 pentablock terpolymers. Using self-consistent field theory, we show that the ternary blends exhibit similar self-assembly behaviors as the pentablock copolymers, forming various mesocrystals with controllable coordination numbers. This study offers a simpler alternative to fabricating novel macromolecular mesocrystals and introduces a general design principle for emulating multiblock copolymers by block copolymer blends.

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), Insufficient 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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.239
Teacher spread0.233 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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