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Record W2979601876 · doi:10.5772/intechopen.89616

Multi-Field Synergy Process for Polymer Plasticization: A Novel Design Concept for Screw to Facilitate Phase-to-Phase Thermal and Molecular Mobility

2019· book-chapter· en· W2979601876 on OpenAlexaff
Ranran Jian, Hongbo Chen, Weimin Yang

Bibliographic record

VenueIntechOpen eBooks · 2019
Typebook-chapter
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsPhase (matter)Materials scienceProcess (computing)PlasticizerPolymerField (mathematics)ThermalPolymer scienceChemical engineeringComputer scienceEngineeringThermodynamicsComposite materialOrganic chemistryChemistryPhysicsMathematics

Abstract

fetched live from OpenAlex

A novel concept of screw design has been proposed considering the multi-field synergy principle to facilitate phase-to-phase thermal and molecular mobility; subsequently, a torsion element has been designed. This new screw design allows an innovative and effective way to resolve a growing challenge in polymer process engineering, especially for nanocomposites or biopolymers, that is, an inadequate control of mass transfer and thermal management for multicomponent melt flows through narrow channels during extrusion or injection. The adaption of torsion element in the screw facilitated the plasticization mixing and thermal distribution in polymer melts, and the torsional flow induced by the torsion elements shows a synergistic effect on the melt-phase mass flow and the thermal flow field. The latter effect enhances the mass and heat transfer of heterogeneous polymer systems and realizes effective heat management to achieve properly uniform temperature field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.100
GPT teacher head0.323
Teacher spread0.224 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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