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Record W3034452184 · doi:10.1021/acs.nanolett.0c01614

Micron Thick Colloidal Quantum Dot Solids

2020· article· en· W3034452184 on OpenAlexafffund
James Z. Fan, Maral Vafaie, Koen Bertens, Mykhailo Sytnyk, João M. Pina, Laxmi Kishore Sagar, Olivier Ouellette, Andrew H. Proppe, Armin Sedighian Rasouli, Yajun Gao, Se‐Woong Baek, Bin Chen, Frédéric Laquai, Sjoerd Hoogland, F. Pelayo Garcı́a de Arquer, Wolfgang Heiß, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKing Abdullah University of Science and Technology
KeywordsQuantum dotMaterials scienceOptoelectronicsSemiconductorInfraredColloidNanotechnologyOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

Shortwave infrared colloidal quantum dots (SWIR-CQDs) are semiconductors capable of harvesting across the AM1.5G solar spectrum. Today’s SWIR-CQD solar cells rely on spin-coating; however, these films exhibit cracking once thickness exceeds ∼500 nm. We posited that a blade-coating strategy could enable thick QD films. We developed a ligand exchange with an additional resolvation step that enabled the dispersion of SWIR-CQDs. We then engineered a quaternary ink that combined high-viscosity solvents with short QD stabilizing ligands. This ink, blade-coated over a mild heating bed, formed micron-thick SWIR-CQD films. These SWIR-CQD solar cells achieved short-circuit current densities (Jsc) that reach 39 mA cm–2, corresponding to the harvest of 60% of total photons incident under AM1.5G illumination. External quantum efficiency measurements reveal both the first exciton peak and the closest Fabry–Perot resonance peak reaching approximately 80%—this is the highest unbiased EQE reported beyond 1400 nm in a solution-processed semiconductor.

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 categoriesInsufficient payload (model declined to judge)
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 score0.028
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)0.0010.002

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 head0.027
GPT teacher head0.225
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes2
Has abstractyes

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