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Record W2514511112 · doi:10.1021/acs.macromol.6b01437

Critical Point of Symmetric Binary Homopolymer Blends

2016· article· en· W2514511112 on OpenAlex
Russell K. W. Spencer, M. W. Matsen

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

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversity of Waterloo
FundersDivision of Chemistry
KeywordsCritical point (mathematics)Binary numberMonte Carlo methodFlory–Huggins solution theoryPolymer blendCutoffPhysicsRenormalization groupThermodynamicsPolymerizationDivergence (linguistics)Statistical physicsInvariant (physics)RenormalizationPolymerMathematicsMathematical physicsCopolymerQuantum mechanicsMathematical analysisStatistics

Abstract

fetched live from OpenAlex

Monte Carlo field-theoretic simulations (MC-FTS) are performed on structurally symmetric binary homopolymer blends for invariant polymerization indexes of N ≥ 10 3 . It is shown that the ultraviolet (UV) divergence that plagues MC-FTS at small N can be removed by an appropriate renormalization of the Flory–Huggins interaction parameter, χ, allowing one to extract meaningful results that are independent of the wavevector cutoff. Once the divergence is taken care of, the fluctuation corrections to mean-field theory are found to be exceptionally small. In particular, the disordered-state structure function, S ( k ), is virtually indistinguishable from the RPA prediction, and there is a slight shift in the critical point, ( χ N ) c, that roughly scales as N –1/2 . An implication of the small corrections is that previous experimental determinations of χ based on homopolymer blends should be relatively accurate.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.031
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.010
GPT teacher head0.252
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