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Record W4407139691 · doi:10.1504/ijmatei.2024.144232

Characterisation and evaluation of Al-8011 metal matrix composites reinforced with B<SUB align="right">4C + carbon nano tubes particulate

2024· article· en· W4407139691 on OpenAlex
S. Shivaprakash, H. K. Shivanand, M.K. Srinath, Din Bandhu

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

VenueInternational Journal of Materials Engineering Innovation · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsMaterials scienceComposite materialMatrix (chemical analysis)Carbon nanotubeMetalCarbon fibersParticulatesNano-Metal matrix compositeComposite numberMetallurgy

Abstract

fetched live from OpenAlex

In this study, a novel metal matrix composite (MMC) was created by reinforcing an Al-8011 alloy with carbon nanotube (CNT) and boron carbide (B4C) hybrid particles. The composites' density (ρ), Poisson's ratio (v), and Young's modulus (E) were calculated using the rule of mixture. Hardness, tensile, and compression strengths were measured on relevant specimens according to ASTM standards. The hybrid reinforced composite (5% B4C + 1.5% CNT) reached 123.6 HRA in the hardness test. 5% B4C + 1.5% CNT reinforcement improved tensile strength to 310.2 MPa. Compression strength, on the other hand, reduced as B4C and CNT percentages increased. For applications demanding the greatest hardness and tensile stresses, the MMC of Al-8011 alloys with 5% B4C and 1.5% CNT was optimal. For the most compressive applications, it was determined that Al-8011 doped with 1% B4C and 1.5% CNT would be the optimal choice. The hybrid reinforcements of B4C and CNT improved the composite, making it suitable for structural applications.

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)0.001
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 score0.008
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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