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Record W4414250158 · doi:10.1021/acsami.5c06966

Ferroelastic Domain-Induced Electronic Modulation in Halide Perovskites

2025· article· en· W4414250158 on OpenAlexafffund
G. Narasimha, Maryam Bari, Benjamin J. Lawrie, Ilia N. Ivanov, Martí Checa, Sumner B. Harris, Zuo‐Guang Ye, Rama K. Vasudevan, Yongtao Liu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsSimon Fraser University
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsCathodoluminescenceHalidePerovskite (structure)Coupling (piping)PhononSpectroscopyPhase transitionModulation (music)Phase (matter)

Abstract

fetched live from OpenAlex

Lead halide perovskites have emerged as promising materials for optoelectronic applications due to their exceptional properties. In the all-inorganic CsPbBr 3 perovskites, ferroelastic domains formed during phase transitions enhance bulk transport and emissive efficiency. However, the microscopic mechanisms governing carrier dynamics remain poorly understood. In this study, we employ cathodoluminescence (CL) and micro-Raman spectroscopy to image and investigate the electronic properties of the ferroelastic domain walls in CsPbBr 3 single crystals. CL measurements reveal a reduced emissive yield and a slight redshift in emission at the domain walls. Further, micro-Raman studies provide spatially resolved mapping of vibrational modes, exhibiting second-order phonon modes localized at the domain boundaries. Our findings suggest that electron–phonon coupling at the twin domain walls plays a critical role in facilitating efficient charge separation, thereby improving the optoelectronic performance of the CsPbBr 3 perovskites.

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 score0.003

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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Same venueACS Applied Materials & InterfacesSame topicPerovskite Materials and ApplicationsFrench-language works237,207