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Record W4289109775 · doi:10.3390/cryst12081073

Improved Perovskite Structural Stability by Halogen Bond from Excessive Lead Iodide via Numerical Simulation

2022· article· en· W4289109775 on OpenAlexafffund
Taoran Wang, Fan Xu, Qi Wang, Lu Tai, Gu Xu

Bibliographic record

VenueCrystals · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPerovskite (structure)PassivationIodideMaterials scienceSolar cellDecompositionDiffusionHalogenKineticsLead (geology)Perovskite solar cellChemical physicsStability (learning theory)Structural stabilityThermodynamicsChemistryNanotechnologyInorganic chemistryCrystallographyOptoelectronicsAlkylComputer sciencePhysicsLayer (electronics)

Abstract

fetched live from OpenAlex

The short lifetime of perovskite solar cell devices limits the application of the technique, which is yet to be resolved, despite many attempts. An important step is made here by the numerical modelling method, which reveals the decomposition kinetics under the protection of halogen bonds from excess PbI2. Irregular diffusion behaviour of water molecules is observed when excessive PbI2 is introduced, possibly due to the passivation and hindrance from the halogen bond, resulting in a lifetime enhancement of at least five times. The detailed kinetics are also obtained by analyzing the decomposition rate curve, offering a possible path towards high-stability PCE perovskite solar devices, by increasing the PbI2 concentration to above the threshold, which opens an unprecedented route in perovskite solar cell research, and is, hopefully, of intrinsic interest to the broad materials research community as well.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2022
Admission routes2
Has abstractyes

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