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Record W4385294658 · doi:10.1021/acs.jpclett.3c01627

Direct Observation of Higher Multiexciton Formation and Annihilation in CdSe Quantum Dots

2023· article· en· W4385294658 on OpenAlexafffund
Dallas Strandell, Arnab Ghosh, D. Zenatti, Priya Nagpal, Patanjali Kambhampati

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationSony
KeywordsQuantum dotAnnihilationPhysicsPhotoluminescenceBiexcitonAuger effectSpectroscopyFine structureScalingCondensed matter physicsAtomic physicsQuantum mechanicsOptoelectronicsAuger

Abstract

fetched live from OpenAlex

Most experiments on multiexcitons (MX) in quantum dots focused on the biexciton (XX), which is now well-understood. In contrast, there is little understanding of higher MX in quantum dots as a result of their difficulty to observe. Here, we apply time-resolved photoluminescence (t-PL) spectroscopy with 3 ps time resolution, sufficient to directly resolve previously unobserved spectral dynamics of a higher MX in CdSe quantum dots. These experiments resolve the controversy of the sequence of MX emissions, revealing that the higher channels sequentially populate the lower channels. There is a strong dependence of MX recombination kinetics upon a higher MX state, following a universal volume scaling law for Auger recombination for larger dots. Smaller dots show deviations for higher MX. In addition to triexcitons (3X), these experiments reveal MX up to the tetraexciton (4X). These experiments provide a direct observation of MX formation and annihilation in quantum dots. The impact of this observation is a step toward designing quantum dots to exploit higher MX processes.

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)0.001
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 score0.027
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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