MétaCan
Menu
Back to cohort
Record W2783916336 · doi:10.1021/acs.iecr.7b04139

Effects of Compressed CO<sub>2</sub> and Cotton Fibers on the Crystallization and Foaming Behaviors of Polylactide

2018· article· en· W2783916336 on OpenAlexaff
Xiaoli Zhang, WeiDan Ding, Na Zhao, Jingbo Chen, Chul B. Park

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Henan ProvinceZhengzhou UniversityDivision of Materials ResearchChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsCrystallinityMaterials scienceCrystallizationComposite materialSaturation (graph theory)Cell sizeFiberMorphology (biology)Composite numberChemical engineering

Abstract

fetched live from OpenAlex

The foaming behavior and thermal properties of neat polylactide (PLA) and PLA/cotton-fiber composite foams were investigated in this study. CO 2 saturation pressure, temperature, and fibers content significantly affected PLA’s crystallinity and foaming behaviors. At the same saturation temperature (140 °C), a low CO 2 pressure generated nonuniform foam and a large unfoamed area due to too high crystallinity with a close-packed structure. At an intermediate pressure, a fine-cell structure was developed due to the presence of numerous less closely packed crystals served as cell nucleating agents. A high CO 2 pressure also led to a uniform cell structure but with larger cell sizes due to cell deterioration. Similar to the effect of saturation pressure, an intermediate temperature generated the uniform fine-cell structures. The PLA’s cell morphology was improved by the addition of cotton fibers at a low content because of the increased local stresses through the fibers and transcrystals surrounding the fibers.

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)0.001
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 score0.001
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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

Citations35
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicPolymer Foaming and CompositesFrench-language works237,207