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Record W2623930691 · doi:10.1073/pnas.1701386114

Manipulating the ABCs of self-assembly via low-χ block polymer design

2017· article· en· W2623930691 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversity of Waterloo
FundersCamille and Henry Dreyfus FoundationArgonne National LaboratoryU.S. Department of DefenseNational Science Foundation
KeywordsLamellar structurePolymerConverseCopolymerMaterials scienceChemical physicsTransmission electron microscopyDomain (mathematical analysis)Block (permutation group theory)Morphology (biology)Self-assemblySoft matterMolar massLamellar phaseCrystallographyNanotechnologyPhysicsChemistryMathematicsColloidComposite material

Abstract

fetched live from OpenAlex

Significance Molecular sequence and interactions dictate the mesoscale structure of all self-assembling soft materials. Block polymers harness this relationship to access a rich variety of nanostructured materials but typically require energetically unfavorable (high-χ) interactions between blocks. Contrary to this convention, we demonstrate that ABC triblock terpolymers featuring low-χ interactions between end blocks can self-assemble into a unique mixed morphology that subverts the demands of chain connectivity. As a consequence of block–block mixing, the characteristic length scales of these self-assembled structures exhibit an unusual trend: As the total polymer size increases, the domain spacing decreases. These developments expand the vocabulary of block polymer design and open additional avenues for manipulating the self-assembly of synthetic macromolecules.

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.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.301
Teacher spread0.255 · 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