MétaCan
Menu
Back to cohort
Record W4402625683 · doi:10.1177/07316844241285231

Influence of accelerated aging on mechanical properties and aging behavior of glass fiber reinforced pipe

2024· article· en· W4402625683 on OpenAlexaff
Jingjie Dou, Yan Jing, Tan Gu, Zhiming Yu, Dandan Liao, Hu Min, Fei Zhao, Jie Liu, Siwei Chen, Jun Wang

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsPetro-Canada
FundersChina National Petroleum Corporation
KeywordsMaterials scienceComposite materialGlass fiberAccelerated agingFiber

Abstract

fetched live from OpenAlex

In this work, the accelerated aging test of GFRP in two thermal environments was conducted. The accelerated aging test was conducted at a constant temperature of 95°C for periods of 3000h to analyze and study the aging behaviors and mechanism under two environments. Resin defects caused by aging were found in the SEM images, and degradation of the fiber/resin interface was observed. Both the hoop tensile test and uniaxial compression test showed brittle fracture characteristics, and the strength of hydrothermal aging specimens decreased more significantly. Due to matrix degradation, uniaxial compression fracture after hydrothermal aging was observed that the resin exhibited poor adhesion. The decrease in the hardness of the outer resin layer was attributed to the development of the degree of molecular chain breakage. ATR-FTIR results showed that the accelerated aging process is accompanied by changes in the concentration of functional groups. The change in C-H intensity was attributed to the post-curing phenomenon. Accelerated aging resulted in the formation of carboxylic acids or esters and the introduction of hydroxyl groups. Thermogravimetric tests showed that accelerated aging did not change the thermal decomposition temperature of epoxy resins, but caused a decrease in resin content.

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 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 score0.005
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Reinforced Plastics and CompositesSame topicMechanical Behavior of CompositesFrench-language works237,207