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Record W4220934283 · doi:10.1063/5.0087283

Complex viscosity of poly[n]catenanes including olympiadanes

2022· article· en· W4220934283 on OpenAlexafffund
Diya Singhal, M. A. Kanso, S. J. Coombs, A. Jeffrey Giacomin

Bibliographic record

VenuePhysics of Fluids · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsQueen's University
FundersQueen's University
KeywordsCatenanePolystyreneViscosityInterlockingPhysicsPolymerComposite materialPolymer scienceMaterials scienceThermodynamicsMechanical engineering

Abstract

fetched live from OpenAlex

Chains of mechanically interlocking or intersecting organic rings, called poly[n]catenanes, afford interesting opportunities to study the role of orientation in suspensions. We call poly[5]catenanes olympiadanes. In this work, we use general rigid bead-rod theory to arrive at general expressions, from first principles, for the complex viscosity of poly[n]catenane suspensions. General rigid bead-rod theory relies entirely on suspension orientation to explain the elasticity of the liquid. We obtain analytical expressions for the complex viscosity of poly[n]catenanes for both n even and odd, for both mechanically interlocking and intersecting rings, and for identically sized rings. We restrict our analysis to evenly spaced poly[n]catenanes of orthogonal adjacency. We find that the parts of the complex viscosity for intersecting and interlocking rings, when made dimensionless with the polymer contribution to the zero-shear viscosity, match. We find good agreement with the available complex viscosity measurements for molten intersecting polystyrene poly[1,3]catenanes, but not so for poly[2]catenanes. We next calculate space filling equilibrium structures of these poly[1–3]catenanes, only to discover that each polystyrene ring looks more like a bead. We find that, for these polystyrene poly[n]catenanes and for good agreement with the available complex viscosity measurements, the shish-kebab theory suffices.

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 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 score0.190
Threshold uncertainty score0.432

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 science0.0000.000
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 head0.033
GPT teacher head0.303
Teacher spread0.271 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2022
Admission routes2
Has abstractyes

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