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Record W2898147482 · doi:10.1021/acs.nanolett.8b03020

Picosecond Charge Transfer and Long Carrier Diffusion Lengths in Colloidal Quantum Dot Solids

2018· article· en· W2898147482 on OpenAlexafffund
Andrew H. Proppe, Jixian Xu, Randy P. Sabatini, James Z. Fan, Bin Sun, Sjoerd Hoogland, Shana O. Kelley, Oleksandr Voznyy, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2018
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum dotPhotoexcitationPicosecondUltrafast laser spectroscopyExcitonMaterials scienceBand gapCharge carrierPhotoluminescenceDiffusionAcceptorNanoclustersOptoelectronicsMolecular physicsSpectroscopyChemistryNanotechnologyAtomic physicsExcited stateOpticsCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

Quantum dots (QDs) are promising candidates for solution-processed thin-film optoelectronic devices. Both the diffusion length and the mobility of photoexcited charge carriers in QD solids are critical determinants of solar cell performance; yet various techniques offer diverse values of these key parameters even in notionally similar films. Here we report diffusion lengths and interdot charge transfer rates using a 3D donor/acceptor technique that directly monitors the rate at which photoexcitations reach small-bandgap dot inclusions having a known spacing within a larger-bandgap QD matrix. Instead of relying on photoluminescence (which can be weak in strongly coupled QD solids), we use ultrafast transient absorption spectroscopy, a method where sensitivity is undiminished by exciton dissociation. We measure record diffusion lengths of ∼300 nm in metal halide exchanged PbS QD solids that have led to power conversion efficiencies of 12%, and determine 8 ps interdot hopping of carriers following photoexcitation, among the fastest rates reported for PbS QD solids. We also find that QD solids composed of smaller QDs ( d = ∼3.2 nm) exhibit 5 times faster interdot charge transfer rates and 10 times lower trap state densities compared to larger ( d = ∼5.5 nm) QDs.

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 score0.014
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations69
Published2018
Admission routes2
Has abstractyes

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