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Record W4313320243 · doi:10.32920/21740771.v1

Optical absorption enhancement in 3D silicon oxide nano-sandwich type solar cell

2022· preprint· en· W4313320243 on OpenAlexafffund
Amirkianoosh Kiani, Krishnan Venkatakrishnan, Bo Tan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSiliconPlasmonic solar cellOptoelectronicsSolar cellSubstrate (aquarium)Quantum dot solar cellFemtosecondNanofiberLaserMonocrystalline siliconAbsorption (acoustics)Thin filmHybrid silicon laserSilicon oxideLayer (electronics)Crystalline siliconNanocrystalline siliconOpticsNanotechnologyAmorphous siliconComposite materialSilicon nitride

Abstract

fetched live from OpenAlex

<p>Recent research in the field of photovoltaic and solar cell fabrication has shown the potential to significantly enhance light absorption in thin-film solar cells by using surface texturing and nanostructure coating techniques. In this paper, for the first time, we propose a new method for nano sandwich type thin-film solar cell fabrication by combining the laser amorphization (2nd solar cell generation) and laser nanofibers generation (3rd solar cell generation) techniques. In this novel technique, the crystalline silicon is irradiated by megahertz frequency femtosecond laser pulses under ambient conditions and the multi-layer of amorphorized silicon and nano fibrous layer are generated in the single-step on top of the silicon substrate. Light spectroscopy results show significant enhancement of light absorption in the generated multi layers solar cells (Silicon Oxide nanofibers / thin-film amorphorized silicon). This method is single step and no additional materials are added and both layers of the amorphorized thinfilm silicon and three-dimensional (3D) silicon oxide nanofibrous structures are grown on top of the silicon substrate after laser irradiation. Finally, we suggest how to maximize the light trapping and optical absorption of the generated nanofibers/thin-film cells by optimizing the laser pulse duration.</p>

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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.

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

Citations2
Published2022
Admission routes2
Has abstractyes

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