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Record W4405192216 · doi:10.3390/nano14231975

Controlling the Optical and Electrical Properties of Perovskite Films and Enhancing Solar Cell Performance Using the Photonic Curing Process

2024· article· en· W4405192216 on OpenAlexafffund
Moulay Ahmed Slimani, Arjun Wadhwa, Luis Felipe Gerlein, Jaime Benavides, Mohamad Hassan Taherian, Ricardo Izquierdo, Sylvain G. Cloutier

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversité du Québec à Trois-RivièresÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials sciencePhotonicsOptoelectronicsSolar cellCuring (chemistry)Perovskite solar cellProcess (computing)Perovskite (structure)Engineering physicsNanotechnologyComposite materialChemical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The most common method of processing metal oxide and perovskite thin films in the laboratory is thermal annealing (TA), which is a constraint for the commercialization of large-scale perovskite solar cells. Here, we present a photonic curing (PC) process to produce fully photonically annealed perovskite cells-a fast process with well-controlled, short light pulses-to develop perovskite photovoltaic devices with high efficiency. We also demonstrate how to use the parameters of the photonic annealing system to control the optical, electrical, morphological, and structural properties of perovskite layers for photovoltaic device applications. The effect of PC treatment on the microstructure, granularity, and electronic properties was studied by scanning electron microscopy (SEM), photoluminescence (PL), and transient photocurrent (TPC). The degree of conversion of the perovskite precursor and its influence on the electronic structure have been identified. SnO2 and perovskite films were treated with a single pulse and produced PCE comparable to control samples treated by TA.

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 score0.134
Threshold uncertainty score0.313

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 head0.010
GPT teacher head0.212
Teacher spread0.201 · 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
Published2024
Admission routes2
Has abstractyes

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