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Record W2028257783 · doi:10.1116/1.3605300

Monte Carlo simulation of electron scattering and secondary electron emission in individual multiwalled carbon nanotubes: A discrete-energy-loss approach

2011· article· en· W2028257783 on OpenAlexafffund
Md. Kawsar Alam, Alireza Nojeh

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2011
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsScatteringElectronMonte Carlo methodSecondary emissionAtomic physicsElectron scatteringSecondary electronsMaterials scienceCarbon nanotubeIonizationRange (aeronautics)Electron energy loss spectroscopyMolecular physicsComputational physicsPhysicsNanotechnologyOpticsIonNuclear physicsComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

Electron scattering in and secondary electron emission from multiwalled carbon nanotubes are investigated using Monte Carlo simulation. The method treats energy loss in a discrete manner, resulting from individual scattering events, rather than within a continuous-slowing-down approximation. Simulation results agree fairly well with the reported experimental data. The effect of number of nanotube walls is investigated and the energy distribution of the transmitted electrons is calculated. It is found that secondary electron yield in the low-primary-energy range is more sensitive to the number of walls and is maximized for a particular number of walls. The effect is not significant in the higher-primary-energy range. The effect of core electron ionization on secondary electron emission from nanotubes is found to be negligible because of the low scattering cross-section involved. The presented framework can also be applied to other small structures such as nanowires.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

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