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Record W4321497094 · doi:10.3390/cryst13030368

Mechanical, Electrical, and Glass Transition Behavior of Copper–PMMA Composites

2023· article· en· W4321497094 on OpenAlex
Víctor Poblete, Mariela L. Álvarez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrystals · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsSGS (Canada)
Fundersnot available
KeywordsMaterials scienceComposite materialGlass transitionPercolation thresholdFiller (materials)Electrical resistivity and conductivityPercolation (cognitive psychology)Composite numberCopperElectrical conductorEpoxyPolymerMetallurgy

Abstract

fetched live from OpenAlex

The mechanical, electrical, and glass transition behaviors (Tg) of polymethylmethacrylate (PMMA)–metal systems have been studied. Considering both the particle size and the metal filler concentration, the electrical conductivity showed a clear dependence on the sample thickness to reach percolation. An increase of up to 400% of strain-to-failure for the 2% v/v of nanometric filler composites in the mechanical test was observed. Tg analysis showed a decrease in the glass transition temperature when the increase of nanometric metallic filler reached the limit of 2% v/v. Over this concentration, the Tg values showed a tendency to reach the original value of the polymeric matrix without conductive filler. For the 20% v/v micrometric filler composites, the strain-to-failure increased up to 58%, but in the Tg analysis of this composite, no relevant changes were observed when the micrometric metallic filler was increased.

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.

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 score0.002
Threshold uncertainty score0.325

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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