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Record W4385320839 · doi:10.6000/1929-5995.2023.12.07

Development and Characterization of Supercooled Polyethylene Naphthalate

2023· article· en· W4385320839 on OpenAlexvenueno aff
Jian Wang, Hang Li, Pan Dai

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesGeorgia Institute of Technology
KeywordsSupercoolingMaterials scienceDifferential scanning calorimetryPolyethylene naphthalateComposite materialPolyethyleneExtrusionPolymerThermodynamics

Abstract

fetched live from OpenAlex

The utilization of undercooled or supercooled polymers presents a promising approach for the creation of single-polymer composites (SPCs), applicable not only to compaction processing but also to extrusion, injection molding, and 3D printing techniques. This study focuses on the development and characterization of supercooled polyethylene naphthalate (PEN) through differential scanning calorimetry (DSC) and rheological measurements. By employing predetermined conditions, a supercooling degree of 50 ˚C for PEN was achieved. The impact of maximum heating temperature, cooling rate, and shear rate on the supercooling degree was examined, revealing that higher supercooling degrees of PEN can be attained by increasing these factors. Additionally, the flow behavior of supercooled polymer melts at various temperatures was analyzed. The supercooling state of PEN exhibited remarkable stability for a minimum duration of half an hour at temperatures exceeding 250 ˚C.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.061
GPT teacher head0.353
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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