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Record W4229375384 · doi:10.1002/app.52522

Toughness enhancement of polyamide 6,12 with intermolecular hydrogen bonding with <scp>3‐pentadecylphenol</scp>

2022· article· en· W4229375384 on OpenAlexaff
Jing Lu, Amirjalal Jalali, Jianqi Yao, Qin Ma, Jiajie Yin, Ruiyan Zhang, Faliang Luo

Bibliographic record

VenueJournal of Applied Polymer Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyamideMaterials scienceIntermolecular forceCrystallizationToughnessHydrogen bondComposite materialBrittlenessChemical engineeringMoleculeChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Due to the brittle performance of polyamide 6,12 (PA6,12) in high impact environment, we report a facile, efficient, and scalable method to toughen PA6,12 by establishing intermolecular hydrogen bonding (H‐bonding) interaction with 3‐pentadecylphenol (PDP). Interestingly, the intermolecular H‐bonding interaction obviously hindered PA crystallization, which was evidenced by decrease of crystalline temperature from 186.8 to 175.8°C and prolong of crystallization half‐time from 0.27 to 0.37 min, when the amount of PDP was increased to 30 wt%. Consequently, the toughness of the PA6,12/PDP composites containing 30 wt% PDP was improved and their notched impact strength reached to 17.5 kJ m−2, about three times higher than that of the neat PA6,12. It was deduced that both plasticizing effect and intermolecular H‐bonding interaction played critical roles in PA6,12 resultant composites toughness, which opens a new perspective of PA6,12 toughening for a wide range of applications.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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