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Record W2739550862 · doi:10.1021/acs.jpclett.7b01621

Biexciton Resonances Reveal Exciton Localization in Stacked Perovskite Quantum Wells

2017· article· en· W2739550862 on OpenAlexafffund
Madeline H. Elkins, Ryan D. Pensack, Andrew H. Proppe, Oleksandr Voznyy, Li Na Quan, Shana O. Kelley, Edward H. Sargent, Gregory D. Scholes

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersFonds de recherche du Québec – Nature et technologiesUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced ResearchPrinceton University
KeywordsBiexcitonExcitonPerovskite (structure)Quantum wellCondensed matter physicsPhysicsMaterials scienceChemistryQuantum mechanicsCrystallographyLaser

Abstract

fetched live from OpenAlex

, are quantum confined materials with an ever-developing range of optoelectronic device applications. Like other semiconductors, the correlated motion of electrons and holes dominates the material's response to optical excitation influencing its electrical and optical properties such as charge formation and mobility. However, the effects of many-particle correlation have been relatively unexplored in perovskite because of the difficultly of probing these states directly. Here, we use double quantum coherence spectroscopy to explore the formation and localization of multiexciton states in these materials. Between the most confined domains, we demonstrate the presence of an interwell, two-exciton excited state. This demonstrates that the four-body Coulomb interaction electronically couples neighboring wells despite weak electron/hole hybridization in these materials. Additionally, in contrast with inorganic semiconductor quantum wells, we demonstrate a rapid decrease in the dephasing time as wells become thicker, indicating that exciton delocalization is not limited by structural inhomogeneity in low-dimensional perovskite.

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 score0.037
Threshold uncertainty score0.349

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 head0.009
GPT teacher head0.231
Teacher spread0.223 · 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

Citations44
Published2017
Admission routes2
Has abstractyes

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