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Record W2808619522 · doi:10.1088/1361-6641/aacbe4

<i>In situ</i> study of porous silicon thin films thermal oxidation by pulsed laser photoacoustics

2018· article· en· W2808619522 on OpenAlexfundno aff
Atzin David Ruíz Pérez, M.B. de la Mora, J.L. Benítez, R. Castañeda-Guzmán, J. A. Reyes‐Esqueda, M. Villagrán-Munı́z

Bibliographic record

VenueSemiconductor Science and Technology · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoCanadian Institute for Theoretical Astrophysics
KeywordsIn situPorous siliconMaterials scienceThin filmSiliconLaserThermal oxidationPorosityThermalChemical engineeringOptoelectronicsNanotechnologyChemistryOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract Passivation is of remarkable importance for porous silicon (PSi) applications to guarantee the chemical stability of its high surface area through time. Thermal oxidation is one of the most common methods to passivate the surface of PSi. In order to better understand the oxidation process, here we performed an in situ study by using pulsed laser photoacoustics. During the thermal oxidation, the photoacoustical signal was measured each 1 °C from 70 °C to 900 °C. The measurements were analyzed by standard correlation, and the significant signal changes were related to the presence of different surface species. We have found temperatures, where the photoacoustical signal changes drastically, that can be related to the absorption and desorption of chemical species in the surface. Species identification where made through Fourier-transform infrared spectroscopy-attenuated total reflection analysis of the PSi samples, at temperatures around where these notorious modifications on the surface were observed.

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 score0.004

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 head0.008
GPT teacher head0.244
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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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