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Record W2083205938 · doi:10.1063/1.4883919

Measuring inside damage of individual multi-walled carbon nanotubes using scanning transmission X-ray microscopy

2014· article· en· W2083205938 on OpenAlexafffund
Jinyin Liu, Lili Bai, Jian Wang, Guanqi Zhao, Jun Zhong

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeTransmission electron microscopyMaterials scienceScanning electron microscopeNanomaterialsNanotechnologyMicroscopyScanning transmission electron microscopyCarbon fibersComposite materialOpticsComposite numberPhysics

Abstract

fetched live from OpenAlex

The electronic structure of individual multi-walled carbon nanotubes (MWCNTs) has been probed using scanning transmission X-ray microscopy (STXM). Although transmission electron microscope (TEM) images show that the exterior of the MWCNTs are clean and straight; the inside structure of some of the MWCNTs is much less well ordered, as revealed by STXM. The amorphization of the interior tubes can be introduced in the growth or modification processes. Moreover, TEM measurement with high dose may also lead to the inside damage. Our results reveal that the structure of individual MWCNTs can be complex and suggest that electronic structure measurements are an important tool for characterizing carbon nanomaterials.

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.251
Teacher spread0.221 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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