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Record W4283023759 · doi:10.1002/adom.202100439

Phonon Analysis of 2D Organic‐Halide Perovskites in the Low‐ and Mid‐IR Region

2022· article· en· W4283023759 on OpenAlexafffund
Yanfang Chen, Arup Mahata, Aura D. Lubio, Marco Cinquino, Annalisa Coriolano, Lilian Skokan, Young‐Gyun Jeong, Luca Razzari, Luisa De Marco, Andréas Ruediger, Filippo De Angelis, Silvia Colella, Emanuele Orgiu

Bibliographic record

VenueAdvanced Optical Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesUniversità degli Studi di PerugiaMinistero dell’Istruzione, dell’Università e della RicercaNatural Sciences and Engineering Research Council of CanadaConsiglio Nazionale delle RicercheEuropean CommissionDipartimenti di EccellenzaRegione PugliaUniversité du Québec à Trois-Rivières
KeywordsHalideRaman spectroscopyMaterials scienceIodidePhononDensity functional theoryPerovskite (structure)Work (physics)Chemical physicsMolecular vibrationComputational chemistryCondensed matter physicsInorganic chemistryCrystallographyOpticsThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Combining the characteristics of hybrid perovskites and layered materials, 2D Ruddlesden–Popper perovskites exhibit unique properties, some of which still require to be deeply understood. In this study, the vibrational signatures of such materials are analyzed by collecting experimental Raman spectra of four distinct compounds. Supported by density functional theory simulations, the role of the phenyl spacer single fluorination on the phonon modes of two similar yet different compounds, i.e., phenethylammonium lead iodide (PEAI) and 4‐fluorophenethylammonium lead iodide (PEAI‐F), is explained. In addition, this work analyzes some so‐far unreported experimental Raman peaks in the 600–1100 cm−1 range and discusses their origin in this class of 2D compounds. This work paves the way for a better design of novel compounds as well as for their exploitation in (opto)electronic applications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.218
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 source (direct Gemma or distilled Codex), 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

Citations13
Published2022
Admission routes2
Has abstractyes

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