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Record W4413739576 · doi:10.1039/9781837673940-00495

Packings of Soft Spheres Self-assembled from Block Copolymers

2025· book-chapter· en· W4413739576 on OpenAlexaff
An‐Chang Shi

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCopolymerSPHERESBlock (permutation group theory)Materials sciencePolymer scienceComposite materialEngineeringGeometryMathematicsPolymerAerospace engineering

Abstract

fetched live from OpenAlex

Block copolymers, obtained when two or more chemically distinct sub-chains or blocks are covalently linked together, tend to organize into assemblies of different shapes (spheres, cylinders, and lamellae) due to the competition between block–block repulsion and chain connectivity. The packing of these malleable assemblies leads to the formation of various ordered phases. For the case of spherical assemblies resembling soft spheres, their packing leads to, besides the commonly observed simple body-centred-cubic phase, the emergence of several complex spherical packing phases, i.e. the Frank–Kasper phases. Several mechanisms, including conformational asymmetry, copolymer architecture, and blending of different polymeric species, have been identified to stabilize the complex spherical packing phases. These developments shed light on our understanding of the packing of soft spheres self-assembled from soft matter.

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: Other · Consensus signal: Other
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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