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Record W2066241793 · doi:10.1021/la800568k

Highly Luminescent Lead Sulfide Nanocrystals in Organic Solvents and Water through Ligand Exchange with Poly(acrylic acid)

2008· article· en· W2066241793 on OpenAlexaff
Wanjuan Lin, Karolina P. Fritz, Gérald Guérin, Ghasem Rezanejade Bardajee, Sean Hinds, Vlad Sukhovatkin, Edward H. Sargent, Gregory D. Scholes, Mitchell A. Winnik

Bibliographic record

VenueLangmuir · 2008
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsChemistryLead sulfideLigand (biochemistry)Acrylic acidTetrahydrofuranQuantum yieldColloidTolueneInorganic chemistryNanocrystalPolymer chemistryQuantum dotOrganic chemistrySolventMaterials scienceCopolymerNanotechnologyPolymer

Abstract

fetched live from OpenAlex

Hydrophobic lead sulfide quantum dots (PbS/OA) synthesized in the presence of oleic acid were transferred from nonpolar organic solvents to polar solvents such as alcohols and water by a simple ligand exchange with poly(acrylic acid) (PAA). Ligand exchange took place rapidly at room temperature When a colloidal solution of PbS/OA in tetrahydrofuran (THF) was treated with excess PAA, the PbS/PAA nanocrystals that formed were insoluble in hexane and toluene but could be dissolved in methanol or water, where they formed colloidal solutions that were stable for months. Ligand exchange was accompanied by a small blue shift in the band-edge absorption, consistent with a small reduction in particle size. While there was a decrease in quantum yield associated with ligand exchange and transfer to polar solvents, as is commonly found for colloidal quantum dots, the quantum yields determined were impressively high: PbS/OA in toluene (82%) and in THF (58%); PbS/PAA in THF (42%) and in water (24%). The quantum yields for the PbS/PAA solutions decreased over time as the solutions were allowed to age in the presence of air.

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 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 score0.009
Threshold uncertainty score0.467

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)0.0000.000

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 head0.027
GPT teacher head0.219
Teacher spread0.193 · 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

Citations103
Published2008
Admission routes1
Has abstractyes

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