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Record W2330441883 · doi:10.1021/nn505343e

Electronically Active Impurities in Colloidal Quantum Dot Solids

2014· article· en· W2330441883 on OpenAlexaff
Graham H. Carey, Illan J. Kramer, Pongsakorn Kanjanaboos, Gabriel Moreno‐Bautista, Oleksandr Voznyy, Lisa R. Rollny, Joel A. Tang, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueACS Nano · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsQuantum dotBifunctionalPhotovoltaicsLigand (biochemistry)Materials scienceColloidNanotechnologyNanoparticleImpurityPhotodetectionActive layerChemical engineeringLayer (electronics)ChemistryPhotovoltaic systemOptoelectronicsOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Colloidal quantum dot films have seen rapid progress as active materials in photodetection, light emission, and photovoltaics. Their processing from the solution phase makes them an attractive option for these applications due to the expected cost reductions associated with liquid-phase material deposition. Colloidally stable nanoparticles capped using long, insulating aliphatic ligands are used to form semiconducting, insoluble films via a solid-state ligand exchange in which the original ligands are replaced with short bifunctional ligands. Here we show that this ligand exchange can have unintended and undesired side effects: a high molecular weight complex can form, containing both lead oleate and the shorter conductive ligand, and this poorly soluble complex can end up embedded within the colloidal quantum dot (CQD) active layer. We further show that, by adding an acidic treatment during film processing, we can break up and wash away these complexes, producing a higher quality CQD solid. The improved material leads to photovoltaic devices with reduced series resistance and enhanced fill factor relative to controls employing previously reported CQD solids.

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 score0.017
Threshold uncertainty score0.492

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)0.0000.000

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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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

Citations36
Published2014
Admission routes1
Has abstractyes

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