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Record W2171950035 · doi:10.1139/cjp-2013-0582

Effect of hydrogen concentration on structure and photoelectric properties of a-Si:H films modified by femtosecond laser pulses

2014· article· en· W2171950035 on OpenAlexvenueno aff
Mark Khenkin, A. V. Emelyanov, А.Г. Казанский, П. А. Форш, Martynas Beresna, Mindaugas Gecevičius, Peter G. Kazansky

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFemtosecondCrystallizationLaserMaterials scienceHydrogenSiliconAmorphous siliconPhotoelectric effectPhotoconductivityCrystalline siliconAmorphous solidOptoelectronicsChemical engineeringAnalytical Chemistry (journal)OpticsCrystallographyPhysicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The crystallization of hydrogenated amorphous silicon films with femtosecond laser pulses is one of the promising ways to produce nanocrystalline silicon for photovoltaics. The structure of laser treated films and their hydrogen content are the most important factors for determining the photoelectric properties of materials. In this work we investigated the effect of femtosecond laser irradiation of a-Si:H films with different fluences on crystalline volume fraction, hydrogen concentration, and photoelectric properties of this material. Our results point out that hydrogen out-diffusion accompanies the crystallization process. The increase of hydrogen concentration in the initial a-Si:H films structure affects the kinetics of the crystallization process, but does not lead to a substantial increase of hydrogen concentration remaining in the film after the treatment. The deficiency of hydrogen in the films’ structure results in degradation of their photoconductivity, thus other strategies of a-Si:H laser crystallization or postprocessing should be found to obtain device-quality films using ultrafast laser processing.

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)0.000
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 score0.031
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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