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Record W1995191869 · doi:10.1021/nn203728t

Efficient Exciton Funneling in Cascaded PbS Quantum Dot Superstructures

2011· article· en· W1995191869 on OpenAlexaff
Fan Xu, Xin Ma, Chelsea R. Haughn, Jamie Benavides, Matthew F. Doty, Sylvain G. Cloutier

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsÉcole de Technologie Supérieure
FundersAir Force Office of Scientific Research
KeywordsQuantum dotExcitonPhotocurrentNanocrystalline materialPhotoluminescenceMaterials scienceAcceptorOptoelectronicsMultiple exciton generationBiexcitonBand gapNanocrystalNanotechnologyCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

Benzenedithiol (BDT) and ethanedithiol (EDT) ligand-exchange treatments can be used to cross-link colloidal PbS quantum dots into nanocrystalline film structures with distinct optoelectronic properties. Such structures can provide a unique platform to study the energy transfer between layers of quantum dots with different sizes. In this report, efficient exciton funneling and recycling of surface state-bound excitons is observed in cascaded PbS quantum dot-based multilayered superstructures, where the excitons transfer from the larger band gap or donor layers to the smallest band gap or acceptor layers. In this system, both the BDT- and EDT-treated cascaded structures exhibit dramatically enhanced photoluminescence from the acceptor layers. As we show, the energy transfer mechanisms involved and their efficiencies are significantly different depending on the ligand-exchange treatment. In the future, we believe these efficient exciton recycling and funneling mechanisms could be used to improve significantly the photocurrent, charge-transport, and conversion efficiencies in low-cost nanocrystalline and hybrid solar cells and the emission efficiencies in hybrid light-emitting devices.

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 categoriesInsufficient payload (model declined to judge)
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 score0.004
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 communication0.0000.000
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 head0.043
GPT teacher head0.232
Teacher spread0.189 · 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

Citations81
Published2011
Admission routes1
Has abstractyes

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