MétaCan
Menu
Back to cohort
Record W4313621203 · doi:10.1002/adma.202209851

All‐Optical Switching Based on Sub‐Bandgap Photoactivation of Charge Trapping in Metal Halide Perovskites

2023· article· en· W4313621203 on OpenAlexaff
Sushu Wan, Ke Li, Meijun Zou, Daocheng Hong, Mingcai Xie, Hairen Tan, Ivan G. Scheblykin, Yuxi Tian

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesVetenskapsrådetNational Natural Science Foundation of ChinaState Key Laboratory of Analytical Chemistry for Life ScienceSwedish Foundation for International Cooperation in Research and Higher Education
KeywordsMaterials scienceHalideTrappingBand gapMetalOptoelectronicsCharge (physics)PhotochemistryNanotechnologyInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Controllable optical properties are crucial for the application of light‐emitting materials in optical devices. In this work, controllable photoluminescence in metal halide perovskite crystals is realized via photoactivation of their defects. It is found that under continuous excitation, the photoluminescence intensity of a CH 3 NH 3 PbBr 3 crystal can be fully controlled by sub‐bandgap energy photon illumination. Such optically controllable emission behavior is rather general as it is observed also in CsPbBr 3 and other perovskite materials. The switching mechanism is assigned to reversible light‐induced activation/deactivation of nonradiative recombination centers, the presence of which relates to an excess of Pb during perovskite synthesis. Given the success of perovskites in photovoltaics and optoelectronics, it is believed that the discovery of green luminescence controlled by red illumination will extend the application scope of perovskites toward optical devices and intelligent control.

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 score0.011
Threshold uncertainty score0.683

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced MaterialsSame topicPerovskite Materials and ApplicationsFrench-language works237,207