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Record W2087050936 · doi:10.1103/physreve.80.021112

Study of entropy-driven self-assembly of rigid macromolecules

2009· article· en· W2087050936 on OpenAlexafffund
Issei Nakamura, An‐Chang Shi

Bibliographic record

VenuePhysical Review E · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMacromoleculeAdsorptionDimerEntropy (arrow of time)ThermodynamicsMoleculeEnthalpyDesorptionChemical physicsMaterials scienceHeat capacityConfiguration entropyChemistryPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

A simple model composed of two rigid macromolecules (adsorbents) immersed in a large number of small molecules (adsorbates) is used to study entropy-driven association processes. The surfaces of the adsorbents are capable of adsorbing the smaller adsorbates. The partition function of the model is obtained analytically. The probability of dimerization and the number of adsorbed molecules are shown to depend on the enthalpy and the entropy differences between the assembled and the disassembled states. Under certain conditions, dimerization of the macromolecules occurs with increasing temperature. This entropy-driven self-assembly is originated from an overall entropy gain due to the release of the adsorbed small molecules, leading to a large peak in the heat capacity due to the dimer formation. The desorption of the adsorbates induces a sharp change in the first-order derivative of the free energy, resulting in another large peak in the heat capacity. A temperature-induced re-entrance into the dimer state is also contained in the model.

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 score0.002
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.015
GPT teacher head0.315
Teacher spread0.299 · 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

Citations9
Published2009
Admission routes2
Has abstractyes

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