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Record W7130306608 · doi:10.5006/m2025_00176

Polymer Qualification Using Realistic in–situ Mechanical Testing

2025· article· W7130306608 on OpenAlexaff
David Munoz-Paniagua, Hadi Nazaripoor, Jorge Palacios Moreno, Ahmed Hammami, A. Traidia, Pierre Mertiny

Bibliographic record

Venuenot available
Typearticle
Language
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolymerUltimate tensile strengthTensile testingThermoplasticShear (geology)Thermoplastic polymerWork (physics)Polyethylene

Abstract

fetched live from OpenAlex

Abstract The qualification of polymers for use in O&G applications relies on conventional testing protocols, such as ISO 23936, which involve exposing material samples to the desired service fluid until saturation, followed by mechanical testing at ambient conditions. This practice can generate misleading results, especially for materials for which fluid ingress is rapidly reversible, most notably at elevated temperatures. A novel in–situ punch-shear device has recently been developed to enable testing of polymers while saturated (aged) in fluids at elevated temperatures and pressure conditions typically encountered in oil and gas operations. The modular and compact in–situ test device, which is an extension of the ASTM D 732 punch-shear method, has been successfully used to establish experimental correlations between the tensile properties and shear properties of polyethylene of raised temperature (PERT) under dry conditions. In a more recent work, the method was extended to a suite of commercially available thermoplastic resins spanning the commodity, engineering, and high-performance polymer grades with varying degrees of hygroscopicity and, in turn, susceptibility to hydrolysis. In the present work, we first present the basic principles behind the proposed test method and polymer qualification approach, and then we discuss some key findings from the work carried out to date. Particular focus is placed on (i) the observed correlations between shear and tensile properties of different classes of polymer in dry conditions and (ii) effect of specimen saturation and reversibility after aging in water at 95°C.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.102
GPT teacher head0.332
Teacher spread0.231 · 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
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
Published2025
Admission routes1
Has abstractyes

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