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Record W4391738490 · doi:10.1021/acs.macromol.3c01805

Entropy-Induced Localization and Sliding Dynamics of Rings on Polyrotaxane

2024· article· en· W4391738490 on OpenAlexaff
Yan Wang, Hui Lu, Xiang‐Meng Jia, An‐Chang Shi, Jiajia Zhou, Guojie Zhang, Hong Liu

Bibliographic record

VenueMacromolecules · 2024
Typearticle
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsMcMaster University
FundersSongshan Lake Materials LaboratoryNational Natural Science Foundation of ChinaAlexander von Humboldt-Stiftung
KeywordsElectromagnetic coilMolecular dynamicsEntropy (arrow of time)Ring (chemistry)Materials scienceMolecular physicsChemical physicsCrystallographyPhysicsThermodynamicsChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Regulating the position and sliding dynamics of rings on the polyrotaxane (PR) backbone plays a crucial role in determining the properties and/or functions of PR and PR-based soft materials. In this work, we use molecular dynamics simulations to reveal that the features of localization and sliding dynamics of rings on a PR modeled by a rod–coil–rod triblock copolymer are regulated by the entropy effect of the coil block. The distribution of the rings along the rod–coil–rod PR backbone is found to be highly heterogeneous and can be described by a two-state model characterized by a (free) energy gap, Δ E, which depends on the three characteristic parameters of the PR system, Δ E = Δ E (α, μ, ρ ring ), where α is the ratio of the rod to the coil strand length, μ is a quantity of measuring the stretching degree of the coil block, and ρ ring is the overall ring coverage along the PR backbone. A theoretical model is proposed to describe the origin of this universality, the prediction of which is quantitatively consistent with simulation results for the single-ring rod–coil–rod PR system. The existence of an energy gap also gives a model for the dynamics of ring sliding along the PR backbone.

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 score0.023
Threshold uncertainty score0.605

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 head0.010
GPT teacher head0.231
Teacher spread0.222 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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