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Record W2014189497 · doi:10.1111/ijac.12309

Additive Manufacturing of Graphene–Hydroxyapatite Nanocomposite Structures

2014· article· en· W2014189497 on OpenAlexafffund
Amir Azhari, Ehsan Toyserkani, Carole Villain

Bibliographic record

VenueInternational Journal of Applied Ceramic Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesVanderbilt University
KeywordsGrapheneMaterials scienceNanocompositeOxideComposite materialPorosityCore (optical fiber)Compressive strengthSaturation (graph theory)NanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

In this article, a powder‐bed class of additive manufacturing ( AM ) is incorporated into the manufacturing of graphene nanocomposite 3D structures. For AM of graphene‐based 3D structures, graphene oxide ( GO )/hydroxyapatite (Hap) nanocomposite ( GHN ) was synthesized at different GO to Hap percentage (wt.%), including 0.2% and 0.4% to develop a printable powder. The synthesized powder was utilized in a powder‐bed AM system to fabricate 3D porous structures of GHN powder. It was shown that at layer thickness of 125 μm and core binder saturation level of 400%, the compressive mechanical strength of the samples with higher content of graphene was improved significantly.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations50
Published2014
Admission routes2
Has abstractyes

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