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Raman Spectroscopy and Field Emission Scanning Electron Microscopic Studies on Carbon Nanomaterials

2011· article· en· W2008493107 on OpenAlexfundno aff
Aziah Buang Nor, Muhammad Zamir Othman

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersCanadian Nuclear Safety CommissionUniversiti Teknologi MalaysiaKementerian Sains, Teknologi dan Inovasi
KeywordsRaman spectroscopyNanomaterialsMaterials scienceCarbon nanotubeCarbon nanofiberCarbon fibersNanotechnologyAcetyleneField electron emissionCatalysisChemical engineeringChemical vapor depositionChemistryElectronComposite materialOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Raman analysis is one of the main methods use to investigate type of graphitized carbon nanomaterials. It provides information on the vibration of crystal lattice, electron structure and regularity of crystal structure of the graphitized carbon materials. In this work the single step chemical vapour deposition (CVD) method was introduced for the direct growth of all as-synthesized carbon nanomaterials; carbon nanotubes (CNTs), carbon nanofiber (CNF), carbon nanosphere chain (CNSC) and CNT heterojunctions (HJCNTs). Series of metal catalysts were formulated from Ni, Fe, Cr and Cu, and were used in the synthesis of the respected carbon nanomaterials (CNMs). By introducing the right combination of the reaction conditions consisting of the formulated catalyst, reaction temperature and flow rate as well as type of carbon source resulted in the formation of different types of carbon nanomaterials. Raman spectra analysis revealed that the used of acetylene and ethanol as the carbon precursors are proficient to produce different form of carbon nanomaterials as depicted by the evolution of the G-band and D-band intensities. The FESEM micrographs obtained confirmed the morphology and types of the respected carbon nanomaterials synthesized in this study.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.066
GPT teacher head0.398
Teacher spread0.332 · 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 teacher head, not a consensus.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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