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Record W2808679948 · doi:10.1021/acs.nanolett.8b01470

Acid-Assisted Ligand Exchange Enhances Coupling in Colloidal Quantum Dot Solids

2018· article· en· W2808679948 on OpenAlexafffund
Jea Woong Jo, Jongmin Choi, F. Pelayo Garcı́a de Arquer, Ali Seifitokaldani, Bin Sun, Younghoon Kim, Hyungju Ahn, James Z. Fan, Rafael Quintero‐Bermudez, Junghwan Kim, Min‐Jae Choi, Se‐Woong Baek, Andrew H. Proppe, Grant Walters, Dae‐Hyun Nam, Shana O. Kelley, Sjoerd Hoogland, 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 CanadaOntario Ministry of Research, Innovation and Science
KeywordsQuantum dotDispersityLigand (biochemistry)PassivationColloidNanocrystalNanotechnologyNitric acidChemistryPhase (matter)Materials scienceReactivity (psychology)Chemical engineeringInorganic chemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Colloidal quantum dots (CQDs) are promising solution-processed infrared-absorbing materials for optoelectronics. In these applications, it is crucial to replace the electrically insulating ligands used in synthesis to form strongly coupled quantum dot solids. Recently, solution-phase ligand-exchange strategies have been reported that minimize the density of defects and the polydispersity of CQDs; however, we find herein that the new ligands exhibit insufficient chemical reactivity to remove original oleic acid ligands completely. This leads to low CQD packing and correspondingly low electronic performance. Here we report an acid-assisted solution-phase ligand-exchange strategy that, by enabling efficient removal of the original ligands, enables the synthesis of densified CQD arrays. Our use of hydroiodic acid simultaneously facilitates high CQD packing via proton donation and CQD passivation through iodine. We demonstrate highly packed CQD films with a 2.5 times increased carrier mobility compared with prior exchanges. The resulting devices achieve the highest infrared photon-to-electron conversion efficiencies (>50%) reported in the spectral range of 0.8 to 1.1 eV.

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 score0.006
Threshold uncertainty score0.724

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)0.0010.001

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 head0.029
GPT teacher head0.257
Teacher spread0.228 · 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

Citations76
Published2018
Admission routes2
Has abstractyes

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