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Structural and compositional study of Erbium-doped silicon nanocrystals by HAADF, EELS and HRTEM techniques in an aberration corrected STEM

2010· article· en· W2020747770 on OpenAlexaff
Reza J. Kashtiban, U. Bangert, Iain F. Crowe, Matthew P. Halsall, B. Sherliker, A. J. Harvey, J. Eccles, Andrew P. Knights, R. Gwilliam, Mhairi Gass

Bibliographic record

VenueJournal of Physics Conference Series · 2010
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceHigh-resolution transmission electron microscopySiliconAmorphous solidDopingErbiumAnnealing (glass)Ion implantationAgglomerateMatrix (chemical analysis)Silicon oxideIonOptoelectronicsNanotechnologyTransmission electron microscopyCrystallographyComposite materialChemistrySilicon nitride

Abstract

fetched live from OpenAlex

Er-doped SiO 2 and Si nano-crystals (NCs) embedded in a SiO 2 matrix were produced by ion beam implantation of Si (100) substrates. After annealing Er ions agglomerate in different positions with different compositional properties in samples with and without Si implants. HAADF and EELS show that in the sample with Si implants the Si and Er distribution is identical and within a band of ~110nm width ~75nm below theSiO 2 surface whereas in the sample with no excess Si, Er forms on average much larger, amorphous aggregates, presumably an Er-oxide, in the SiO 2 matrix with tendency to move towards the surface of the SiO 2 layer.

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 score0.002
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.257
Teacher spread0.241 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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