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Record W2888124329 · doi:10.1038/s41598-018-31184-0

Solvent-Antisolvent Ambient Processed Large Grain Size Perovskite Thin Films for High-Performance Solar Cells

2018· article· en· W2888124329 on OpenAlexafffund
Dawit Gedamu, Ivy M. Asuo, Daniele Benetti, Matteo Basti, Ibrahima Ka, Sylvain G. Cloutier, Federico Rosei, Riad Nechache

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsÉcole de Technologie SupérieureInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Jiangxi Province
KeywordsMaterials sciencePerovskite (structure)Grain sizeChlorobenzeneEnergy conversion efficiencyThin filmMicrostructureFabricationNanotechnologyGranularityPhotovoltaic systemOptoelectronicsChemical engineeringComputer scienceComposite materialChemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract In recent years, hybrid organic-inorganic halide perovskites have been widely studied for the low-cost fabrication of a wide range of optoelectronic devices, including impressive perovskite-based solar cells. Amongst the key factors influencing the performance of these devices, recent efforts have focused on tailoring the granularity and microstructure of the perovskite films. Albeit, a cost-effective technique allowing to carefully control their microstructure in ambient environmental conditions has not been realized. We report on a solvent-antisolvent ambient processed CH 3 NH 3 PbI 3− x Cl x based thin films using a simple and robust solvent engineering technique to achieve large grains (>5 µm) having excellent crystalline quality and surface coverage with very low pinhole density. Using optimized treatment (75% chlorobenzene and 25% ethanol), we achieve highly-compact perovskite films with 99.97% surface coverage to produce solar cells with power conversion efficiencies (PCEs) up-to 14.0%. In these planar solar cells, we find that the density and size of the pinholes are the dominant factors that affect their overall performances. This work provides a promising solvent treatment technique in ambient conditions and paves the way for further optimization of large area thin films and high performance perovskite solar cells.

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)0.001
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 score0.080
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations139
Published2018
Admission routes2
Has abstractyes

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