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Record W6939335747 · doi:10.60692/3pczd-fk771

Perovskite seeding growth of formamidinium-lead-iodide-based perovskites for efficient and stable solar cells

2018· article· en· W6939335747 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2018
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerovskite (structure)SeedingCaesiumPhotovoltaicsCrystallizationFabricationBand gapEnergy conversion efficiency

Abstract

fetched live from OpenAlex

Abstract Formamidinium-lead-iodide (FAPbI 3 )-based perovskites with bandgap below 1.55 eV are of interest for photovoltaics in view of their close-to-ideal bandgap. Record-performance FAPbI 3 -based solar cells have relied on fabrication via the sequential-deposition method; however, these devices exhibit unstable output under illumination due to the difficulty of incorporating cesium cations (stabilizer) in sequentially deposited films. Here we devise a perovskite seeding method that efficiently incorporates cesium and beneficially modulates perovskite crystallization. First, perovskite seed crystals are embedded in the PbI 2 film. The perovskite seeds serve as cesium sources and act as nuclei to facilitate crystallization during the formation of perovskite. Perovskite films with perovskite seeding growth exhibit a lowered trap density, and the resulting planar solar cells achieve stabilized efficiency of 21.5% with a high open-circuit voltage of 1.13 V and a fill factor that exceeds 80%. The Cs-containing FAPbI 3 -based devices show a striking improvement in operational stability and retain 60% of their initial efficiency after 140 h operation under one sun illumination.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.735

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 head0.017
GPT teacher head0.185
Teacher spread0.168 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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