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Record W3130507024 · doi:10.11159/ijtan.2021.001

Effects of Hydrogen Concentration and Cooling Speed on Fabrication of Hydrogenated Amorphous Silicon: Quantum Simulation

2021· article· en· W3130507024 on OpenAlexvenueno aff
Haili Li, Mitsuhiro Matsumoto

Bibliographic record

VenueInternational Journal of Theoretical and Applied Nanotechnology · 2021
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFabricationAmorphous siliconMaterials scienceAmorphous solidSiliconHydrogenOptoelectronicsEngineering physicsCrystalline siliconChemistryEngineeringCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

In order to investigate various properties of hydrogenated amorphous silicon (a-Si:H) for improvement of low conversion efficiency and stability of solar cells, a series of quantum simulations based on the density functional theory combined with the tight binding model were performed for a-Si:H with various hydrogen concentrations and cooling rates. The radial distribution function (RDF) for Si-Si pairs indicates that samples with higher H concentration (20% and 25%) give a structure in better agreement with experiments, but the RDF of Si-H pairs suggests that samples with lower H concentration (14%) may give more appropriate structure. The coordination number ( ) analysis indicates that more defects (dangling bonds and floating bonds) exist in 20% and 25% H concentration samples. Overall, a-Si:H with 14% H concentration gives most preferable structure. The cooling rate has also much effect on the structure. Sample with the slowest cooling rate is slightly more structured based on Si-Si pair RDF and . The electron transport of a-Si and a-Si:H were evaluated and the superiority of a-Si:H was confirmed.

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 score0.194
Threshold uncertainty score0.399

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 head0.004
GPT teacher head0.213
Teacher spread0.208 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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