MétaCan
Menu
Back to cohort
Record W6922125849 · doi:10.11938/cjmr20223007

Insights into the Phase Structure and Dynamics of Polyurethane Rubber Using T1-T2* Relaxation Correlation

2023· article· en· W6922125849 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsInterphaseShore durometerPhase (matter)PolymerRelaxation (psychology)Component (thermodynamics)Polyurethane

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance (NMR) is a non-destructive technique that can reveal the phase structure and dynamics of polymers at the molecular level. It is sensitive to polymer chain mobility and requires minimal special sample preparation. We investigated the phase structure and molecular dynamics of polyurethane rubber (PUR) based on the T1-T2* relaxation correlation spectra, and analyzed the T1-T2* data by multi modal decay. The T1-T2* spectra showed three types of signals: rigid 1H with the shortest T2* value, interphase 1H with an intermediate T2* value, and mobile-amorphous 1H with the longest T2* value. The three 1H components exhibit the similar T1 values in PUR, which decreased with increasing hardness or decreasing temperature. The integrals of these signals depend on the durometer hardness and temperature for PUR. They increase for the rigid phase but reduce for mobile-amorphous phase and interphase with an increase of PUR durometer hardness. The rigid 1H component decreased and the mobile-amorphous 1H component increased with increasing temperature, while the interphase 1H component remained constant. In addition, the hard/soft ratio decreased with increasing temperature for PUR systems. These results indicated that T1-T2* spectra can be used to characterize phase structure and dynamics of PUR.

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 score0.371
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.132
GPT teacher head0.497
Teacher spread0.365 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicPolymer composites and self-healingFrench-language works237,207