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Record W2102175589 · doi:10.1002/polb.20634

Crystal nucleation of polymers confined to droplets: Memory effects

2005· article· en· W2102175589 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

VenueJournal of Polymer Science Part B Polymer Physics · 2005
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials Research
Fundersnot available
KeywordsNucleationDewettingCrystallizationMaterials scienceCrystallization of polymersPolymerChemical physicsCrystal (programming language)ThermalThin filmNanotechnologyThermodynamicsComposite materialChemistryPhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract We study crystal nucleation within an ensemble of polyethylene droplets created through the dewetting of a thin film on an unfavorable substrate. In particular, we employ standard thermal treatment procedures to induce self‐nucleation in samples to gain some insight into the nature of this enhanced crystallization. The novel sample‐geometry enables the monitoring of each droplet throughout successive experiments, and hence the changes in their nucleation mechanism for various thermal treatments. We find a consistent self‐nucleated crystallization temperature of ∼101 °C under all conditions, suggesting uniformity in the centers which facilitate nucleation. Using correlation plots, we demonstrate that the observed melt‐memory effects have a stochastic rather than deterministic nature; self‐nucleation is a random process. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 3438–3443, 2005

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)0.001
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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