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Record W1978336733 · doi:10.5539/jmsr.v2n1p34

Effects of Annealing Temperature on Raman Scattering and Electrical Properties of Te-Doped Nanostructured Black Silicon

2012· article· en· W1978336733 on OpenAlexvenueno aff
Yuanjie Su, Ting Zhang, Peng Zhang, Jing Jiang, Zhanfei Xiao, Shibin Li, Zhiming Wu, Yadong Jiang

Bibliographic record

VenueJournal of Materials Science Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersNational Key Laboratory of Electronic Thin Films and Integrated DevicesNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMaterials scienceAnnealing (glass)SiliconBlack siliconRaman scatteringDopingRaman spectroscopyDopantAmorphous solidFull width at half maximumAmorphous siliconAnalytical Chemistry (journal)OptoelectronicsComposite materialCrystalline siliconOpticsCrystallographyChemistry

Abstract

fetched live from OpenAlex

In this paper, the influence of annealing temperature on Raman scattering and transport properties of Te-doped nanostructured black silicon has been studied. We prepared the black silicon samples by wet etching, i.e. alkaline etching and metal assisted etching. The nanopores on the surface of black silicon were produced by metal assisted etching. The black silicon samples were annealed at different temperature of 600oC, 700oC, and 800oC According to the Raman scattering results, the peak intensity of Si increases with the increase of annealing temperature. However, the full width at half maximum (FWHM) is inversely proportion to the annealing temperature. Thermal annealing removes the Raman peak of amorphous Si at 480 cm-1. In term of Te dopant atoms, the peak intensities of annealed samples are much lower than that of unannealed one. Subsequent Hall Effect measurement shows that annealing treatment improves electronic transport properties of Te-doped nanostructured black silicon.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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