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Record W4220867486 · doi:10.1021/acs.cgd.1c01449

Conversion of Electrochemically Deposited Aragonite Crystallites to Perovskite through Ion Exchange

2022· article· en· W4220867486 on OpenAlexafffund
William D. Leal, Caroline L. Bergeron, Tyler J. Rutherford, Marek B. Majewski

Bibliographic record

VenueCrystal Growth & Design · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCentre québécois sur les matériaux fonctionnels
KeywordsPerovskite (structure)MicrostructureMaterials scienceCrystallitePhotoluminescenceChemical engineeringNanocrystalSubstrate (aquarium)OxideMineralogyNanotechnologyChemistryMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

The unique and broadly applicable optoelectronic properties of metal-halide perovskite materials are determined by structural dimensionality. Conversion of scaffold-supported carbonate salts to perovskite with microstructure retention has previously been shown to act as a gateway to unique morphologies. In the present work, calcium carbonate microstructures are electrochemically deposited on a transparent conducting oxide substrate. Through a series of ion-exchange reactions, the microstructures are decorated with a layer of surface-localized perovskite nanocrystals, indicating that this ion-exchange process occurs at the microstructure surface. Throughout the conversion process, electron microscopy confirms that the microstructures retain their overall morphology, while cubic perovskite nanocrystals exhibiting characteristic photoluminescence and photoblinking are formed at the interface. This work confirms a synthetic pathway in which perovskites can be made in shapes previously inaccessible that may lead to enhanced optoelectronic properties.

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 score0.218
Threshold uncertainty score0.917

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 head0.203
Teacher spread0.190 · 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
Published2022
Admission routes2
Has abstractyes

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