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Record W3015499971 · doi:10.1002/cjce.23761

<scp>MARTINI</scp> coarse‐grained model for poly‐ε‐caprolactone in acetone‐water mixtures

2020· article· en· W3015499971 on OpenAlexvenueno aff
Alessio Lavino, Paola Carbone, Daniele Marchisio

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsnot available
Fundersnot available
KeywordsRadius of gyrationAcetoneSolvationGyrationWater modelPolymerChain (unit)CaprolactoneThermodynamicsMolecular dynamicsSolventWork (physics)Materials scienceForce field (fiction)GranularityPolymer chemistryChemistryChemical engineeringComputational chemistryPhysicsOrganic chemistryGeometryComposite materialPolymerizationComputer science

Abstract

fetched live from OpenAlex

Abstract In this work we present the development of a MARTINI‐type coarse‐graining (CG) model for poly‐ε‐caprolactone (PCL) dissolved in a solvent binary mixture of acetone and water. A thermodynamic/conformational procedure is adopted to build up the CG model of the system, starting from the standard MARTINI force field. The single CG bead is parametrized upon solvation free energy calculations, whereas the conformation of the whole polymer chain is optimized using the radius of gyration values calculated at different chain lengths. The model is then able to reproduce the correct thermodynamics of the system, as well as the conformation of single PCL chains, especially in pure water and acetone. The results obtained here are then used to simulate the interactions between multiple longer PCL chains in solution. The model developed here can be used in the future to achieve deeper insight into the dynamics of the polymer self‐assembly.

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)0.001
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 score0.177
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.014
GPT teacher head0.203
Teacher spread0.190 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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