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Record W2545653655 · doi:10.1021/acsphotonics.7b00282

Four-Wave Mixing in Perovskite Photovoltaic Materials Reveals Long Dephasing Times and Weaker Many-Body Interactions than GaAs

2017· article· en· W2545653655 on OpenAlexafffund

Bibliographic record

VenueACS Photonics · 2017
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsDalhousie University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDephasingPerovskite (structure)ExcitonSemiconductorMultiple exciton generationCoulombPhononMixing (physics)Solar cell

Abstract

fetched live from OpenAlex

Perovksite semiconductors have shown promise for low-cost solar cells, lasers and photodetectors, yet their fundamental photophysical properties are not well understood. Recent observations of a low exciton binding energy and evidence of hot phonon effects in the room temperature phase suggest that perovskites are much closer to inorganic semiconductors than the absorber layers in traditional organic photovoltaics, signaling the need for experiments that shed light on the placement of perovskite materials within the spectrum of semiconductors used in optoelectronics and photovoltaics. Here we use four-wave mixing (FWM) to contrast the coherent optical response of CH 3 NH 3 PbI 3 thin films and crystalline GaAs. At carrier densities relevant for solar cell operation, our results show that carriers interact surprisingly weakly via the Coulomb interaction in perovskite, much weaker than in inorganic semiconductors. These weak many-body effects lead to a dephasing time in CH 3 NH 3 PbI 3 ∼ 3× longer than in GaAs. Our results also show that the strong enhancement of the exciton FWM signal tied to excitation-induced dephasing in GaAs and other III–V semiconductors does not occur in perovskite due to weak exciton–carrier interactions.

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 categoriesMeta-epidemiology (narrow)
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 score0.149
Threshold uncertainty score1.000

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 communication0.0010.001
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 head0.023
GPT teacher head0.256
Teacher spread0.234 · 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.

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

Citations33
Published2017
Admission routes2
Has abstractyes

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