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Record W2257144064 · doi:10.1038/ncomms8772

Single-step fabrication of quantum funnels via centrifugal colloidal casting of nanoparticle films

2015· article· en· W2257144064 on OpenAlexafffund
Jin Young Kim, Valerio Adinolfi, Brandon R. Sutherland, Oleksandr Voznyy, Soonho Kwon, Tae Wu Kim, Jeongho Kim, Hyotcherl Ihee, Kyle W. Kemp, Michael M. Adachi, Mingjian Yuan, Illan J. Kramer, David Zhitomirsky, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueNature Communications · 2015
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 TechnologyKorea Institute of Energy Technology Evaluation and PlanningKorea Institute of Science and TechnologyMinistry of Trade, Industry and Energy
KeywordsMaterials scienceNanoparticleFabricationQuantum dotPhotodiodeColloidBand gapOptoelectronicsQuantumNanotechnologyCastingComposite materialPhysicsChemical engineering

Abstract

fetched live from OpenAlex

Centrifugal casting of composites and ceramics has been widely employed to improve the mechanical and thermal properties of functional materials. This powerful method has yet to be deployed in the context of nanoparticles--yet size-effect tuning of quantum dots is among their most distinctive and application-relevant features. Here we report the first gradient nanoparticle films to be constructed in a single step. By creating a stable colloid of nanoparticles that are capped with electronic-conduction-compatible ligands we were able to leverage centrifugal casting for thin-films devices. This new method, termed centrifugal colloidal casting, is demonstrated to form films in a bandgap-ordered manner with efficient carrier funnelling towards the lowest energy layer. We constructed the first quantum-gradient photodiode to be formed in a single deposition step and, as a result of the gradient-enhanced electric field, experimentally measured the highest normalized detectivity of any colloidal quantum dot photodetector.

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)0.001
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 score0.012
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.071
GPT teacher head0.289
Teacher spread0.218 · 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

Citations85
Published2015
Admission routes2
Has abstractyes

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