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Record W2943950444 · doi:10.1039/c9nr02708d

Ligand cleavage enables formation of 1,2-ethanedithiol capped colloidal quantum dot solids

2019· article· en· W2943950444 on OpenAlexafffund
James Z. Fan, Andrew D. La Croix, Zhenyu Yang, Emma Howard, Rafael Quintero‐Bermudez, Larissa Levina, Nicole M. Jenkinson, Nathan J. Spear, Yiying Li, Olivier Ouellette, Zheng‐Hong Lu, Edward H. Sargent, Janet E. Macdonald

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum dotCleavage (geology)ColloidLigand (biochemistry)Materials scienceNanotechnologyChemistryChemical engineeringOrganic chemistryReceptorBiochemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Colloidal quantum dots have garnered significant interest in optoelectronics, particularly in quantum dot solar cells (QDSCs). Here we report QDSCs fabricated using a ligand that is modified, following film formation, such that it becomes an efficient hole transport layer. The ligand, O-((9H-fluoren-9-yl)methyl) S-(2-mercaptoethyl) carbonothioate (FMT), contains the surface ligand 1,2-ethanedithiol (EDT) protected at one end using fluorenylmethyloxycarbonyl (Fmoc). The strategy enables deprotection following colloidal deposition, producing films containing quantum dots whose surfaces are more thoroughly covered with the remaining EDT molecules. To compare fabrication methods, we deposited CQDs onto the active layer: in one case, the traditional EDT-PbS/EDT-PbS is used, while in the other EDT-PbS/FMT-PbS is used. The devices based on the new EDT/FMT match the PCE values of EDT/EDT controls, and maintain a higher PCE over an 18 day storage interval, a finding we attribute to an increased thiol coverage using the FMT protocol.

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 categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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.

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

Citations22
Published2019
Admission routes2
Has abstractyes

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