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Record W1973294801 · doi:10.1142/s0218625x00000439

HIGH RESOLUTION ELECTRON ENERGY LOSS SPECTROSCOPY APPLIED TO A GRAPHITE SURFACE MODIFIED BY ION BOMBARDMENT

2000· article· en· W1973294801 on OpenAlexaff
Denis Roy, Marc Portail, Jean-Marc Layet

Bibliographic record

VenueSurface Review and Letters · 2000
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHigh resolution electron energy loss spectroscopyElectron energy loss spectroscopyGraphiteAtomic physicsPlasmonElectronInelastic mean free pathElectron spectroscopyIonMaterials scienceSpectroscopyInelastic scatteringExcitationSurface phononElectron excitationX-ray photoelectron spectroscopyPhononScatteringChemistryPhysicsCondensed matter physicsOpticsOptoelectronicsNuclear magnetic resonanceNuclear physics

Abstract

fetched live from OpenAlex

This work presents the application of the technique known as high resolution electron energy loss spectroscopy (HREELS) to the study of a modern technological surface. First the physics of the interaction of low energy electrons with surfaces is briefly reviewed. The dielectric theory of inelastic electron scattering is outlined, with its application to surfaces and the excitation of phonons, polaritons and plasmons. Then a study of the modification of a graphite surface by Ar and H ion bombardment is presented, in relation with graphite surfaces modified by plasma wall interactions in fusion reactors. The observations of phonons and low energy plasmons are reported, with the C–H stretching vibrations as well following the H + bombardment. These observations are related to structural and chemical modifications induced by the ion bombardment.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.240
Teacher spread0.234 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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