MétaCan
Menu
Back to cohort
Record W4402616098 · doi:10.3390/nano14181508

Enhancing the Performance of Nanocrystalline SnO2 for Solar Cells through Photonic Curing Using Impedance Spectroscopy Analysis

2024· article· en· W4402616098 on OpenAlexafffund
Moulay Ahmed Slimani, Jaime Benavides, Sylvain G. Cloutier, Ricardo Izquierdo

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNanocrystalline materialDielectric spectroscopyMaterials scienceSpectroscopyPhotonicsOptoelectronicsCuring (chemistry)Electrical impedanceComposite materialNanotechnologyElectrical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Wide-bandgap tin oxide (SnO2) thin-films are frequently used as an electron-transporting layers in perovskite solar cells due to their superior thermal and environmental stabilities. However, its crystallization by conventional thermal methods typically requires high temperatures and long periods of time. These post-processing conditions severely limit the choice of substrates and reduce the large-scale manufacturing capabilities. This work describes the intense-pulsed-light-induced crystallization of SnO2 thin-films using only 500 μs of exposure time. The thin-films' properties are investigated using both impedance spectroscopy and photoconductivity characteristic measurements. A Nyquist plot analysis establishes that the process parameters have a significant impact on the electronic and ionic behaviors of the SnO2 films. Most importantly, we demonstrate that light-induced crystallization yields improved topography and excellent electrical properties through enhanced charge transfer, improved interfacial morphology, and better ohmic contact compared to thermally annealed (TA) SnO2 films.

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 score0.061
Threshold uncertainty score0.594

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 head0.012
GPT teacher head0.260
Teacher spread0.247 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNanomaterialsSame topicPerovskite Materials and ApplicationsFrench-language works237,207