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Fabrication and orientation control of diamond nanotips by broad ion beam etching

2010· article· en· W2014154595 on OpenAlexafffund
Yongbing Tang, Y S Li, Q. Yang, Akira Hirose

Bibliographic record

VenueJournal of Physics D Applied Physics · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
FundersNational Research Council CanadaCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDiamondMaterials scienceEtching (microfabrication)Ion beamOptoelectronicsSputteringFabricationFocused ion beamRaman spectroscopyDiamond cubicNanotechnologyIonLayer (electronics)Beam (structure)OpticsThin filmChemistryComposite material

Abstract

fetched live from OpenAlex

Well-aligned diamond nanotips are fabricated by etching as-grown diamond thin films using a Kaufman type broad ion beam source. The nanotips have nanometre-size heads, micrometre-size roots and the same apex angle. All of the nanotips consistently point in the direction against the incident ion beam. The orientation of diamond nanotips can be controlled by adjusting the incident direction of the ion beam. The Raman spectrum does not show a significant increase in graphitic peak intensity after etching, indicating that the quality of diamond is barely degraded by ion beam etching. Near-edge x-ray absorption fine structure spectra show that the diamond sp3 structure is dominant in the spectra of both as-grown and ion etched diamond. The sp2 fraction is found to increase by about 12% at the surface layer of the diamond nanotips as compared with as-grown diamond. The formation of diamond nanotips is explained by Sigmund's sputtering theory and the angle-dependent sputtering mechanism. This method to produce diamond nanotips has the advantage of excellent orientation control and the capability to produce nanotip arrays on a large area.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.007
GPT teacher head0.246
Teacher spread0.238 · 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.

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

Citations4
Published2010
Admission routes2
Has abstractyes

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