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Record W2803005186 · doi:10.1021/acsomega.8b00522

Tailored Interfaces of the Bulk Silicon Nanowire/TiO<sub>2</sub>Heterojunction Promoting Enhanced Photovoltaic Performances

2018· article· en· W2803005186 on OpenAlexafffund
Debika Banerjee, Jaime Benavides, Xiaohang Guo, Sylvain G. Cloutier

Bibliographic record

VenueACS Omega · 2018
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHeterojunctionMaterials sciencePhotovoltaic systemNanowireSilicon nanowiresOptoelectronicsSiliconNanotechnologyEngineering physicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

thin film atop vertically aligned silicon nanowire arrays obtained by facile metal-assisted wet electroless chemical etching of a bulk highly doped n-type silicon wafer. As we show here, chemical treatment of the nanowire arrays prior to depositing the sol-gel precursor has dramatic consequences on the device performance. While hydrofluoric treatment to remove the native oxide already improves significantly the device performances, hydrobromic (HBr) treatment consistently yields by far the best device performances with power conversion efficiencies ranging between 4.2 and 6.2% with fill factors up to 60% under AM 1.5G illumination. In addition to yield high-quality and easy to produce solar cell devices, these findings regarding the surface treatment of silicon nanowires with HBr suggest that HBr could contribute to the enhancement of the device performance not only for solar cells but also for other optoelectronics devices based on semiconductor nanostructures.

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 score0.018
Threshold uncertainty score0.486

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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