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Record W4362663369 · doi:10.1002/mats.202300008

Volumetric and Energetic Properties of Polystyrene and Polyethylene Oxide Affected by Thermal Cycling

2023· article· en· W4362663369 on OpenAlexafffund
Benoît Minisini, Armand Soldera

Bibliographic record

VenueMacromolecular Theory and Simulations · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolystyreneGlass transitionPolymerMaterials sciencePolyethyleneWork (physics)ThermodynamicsVolume (thermodynamics)HysteresisAtmospheric temperature rangeThermal expansionTemperature cyclingThermalHeat capacityComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract Polymers are known to exhibit hysteresis in their thermal and volumetric properties between cooling and heating at the glass transition. A thorough investigation of this hysteresis using atomistic simulation is not proposed until now. In this work, therefore, the glass transition is studied through heating and cooling protocols at constant rate for two polymers, polystyrene (PS) and polyethylene oxide (PEO), with different molecular weights. To achieve this objective, the analysis is carried out by plotting against temperature, specific volume, coefficient of thermal expansion, total energy, and constant volume heat capacity. The calculated properties for PS and PEO are found to be in good agreement with experimental data, confirming the accuracy of the TraPPE force field for these polymers. The glass transition temperature ( T g ) range remains the same regardless of the properties. Moreover, the difference in properties between heating and cooling processes systematically leads to a peak at the same temperature, associated with T g . Finally, starting from a low temperature, the polymer chains remain mainly in a potential well as the temperature rises, while during cooling the exploration of the configuration space continues up to the temperature where no torsional changes are observed.

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 score0.010
Threshold uncertainty score0.394

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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
Published2023
Admission routes2
Has abstractyes

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