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Record W4376305451 · doi:10.24425/ame.2022.140419

Investigating the effects of alumina nanoparticles on the impact resistance of polycarbonate nano-composites

2022· article· en· W4376305451 on OpenAlex

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

VenueArchive of Mechanical Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsPolycarbonateMaterials scienceComposite materialNanoparticleNano-Impact resistanceNanotechnology

Abstract

fetched live from OpenAlex

In this project, two types of treated and untreated alumina nanoparticles with different weight percentages (wt%) of 0.5, 1 and 3% were mixed with polycarbonate matrix; then, the impact ballistic properties of the nano-composite targets made from them were investigated. Three types of projectile noses -cylindrical, hemispherical, and conical, each with the same mass of 5.88\;gr -- were used in the ballistic tests. The results highlighted that ballistic limit velocities were improved by increasing the percentage of alumina nanoparticles and the treatment process; changing the projectile's nose geometry from conical to blunt nose increases the ballistic limit velocity, and ultimately, by increasing the initial velocity of conical and hemispherical nosed projectiles, the failure mechanism of the targets changed from dishing to petalling; whereas for the cylindrical projectile, the failure mode was always plugging.

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 score0.052
Threshold uncertainty score0.182

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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