MétaCan
Menu
Back to cohort
Record W3046199925 · doi:10.1039/d0ra03797d

Recovery of waste gold for the synthesis of gold nanoparticles supported on radially aligned nanorutile: the growth of carbon nanomaterials

2020· article· en· W3046199925 on OpenAlexaff
Farai Dziike, Paul J. Franklyn, Lerato Hlekelele, Shane H. Durbach

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsCanadian Society of Intestinal Research
FundersDepartment of Science and Technology, Republic of South AfricaNational Research Foundation
KeywordsNanorodMaterials scienceNanomaterialsCarbon fibersChemical engineeringTailingsHydrothermal circulationNanoparticleAqua regiaCatalysisNanotechnologyDeposition (geology)Colloidal goldRutileCarbon nanofiberCarbon nanotubeMetallurgyChemistryComposite materialMetalComposite number

Abstract

fetched live from OpenAlex

and that the Au nanoparticles had a face-centred cubic structure. The various catalysts were then used to synthesize carbon nanomaterials (CNMs) using the chemical vapour deposition technique. A parametric study varying the reaction temperature, duration and carbon source gas flow rate was conducted to study the effects these conditions have on the structural properties of the resulting CNMs. Here, it was found that mainly carbon nanofibers were formed and that the different reaction conditions influenced their graphicity, width, structure and thermal properties.

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 score0.004

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 head0.017
GPT teacher head0.246
Teacher spread0.230 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRSC AdvancesSame topicCatalytic Processes in Materials ScienceFrench-language works237,207