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Record W2985727531 · doi:10.1039/c9nr07854a

Synergistic ligand exchange and UV curing of PbS quantum dots for effective surface passivation

2019· article· en· W2985727531 on OpenAlexfundno aff
Hadi Tavakoli Dastjerdi, Daniel Prochowicz, Pankaj Yadav, Mohammad Mahdi Tavakoli

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFundacja na rzecz Nauki Polskiej
KeywordsPassivationQuantum dotMaterials scienceUltravioletBand gapOptoelectronicsNanocrystalLead sulfideLigand (biochemistry)PhotochemistryNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Lead sulfide (PbS) quantum dots (QDs) are promising materials in solution-processed photovoltaic (PV) devices due to their tunable bandgap and low-cost processing. Replacing the long oleic acid ligands of the as-synthesized QDs with shorter ligands is a key step for making functional QD PVs with correctly tuned band energies and reduced non-radiative recombination centers. In this work, we study the effect of ultraviolet (UV) treatment of PbS QD layers on the QD surface states during ligand exchange. We demonstrate that this straightforward approach effectively reduces the surface trap states and passivates the surface of QDs. We find that UV treatment reduces the density of hydroxyl groups attached to the QD surface and improves the bonding of short ligands to the QD surface. Multiple analyses show the reduction of nonradiative recombination centers for the UV-treated sample. The power conversion efficiency (PCE) of our optimized PbS QD device reached 10.7% (vs. 9% for the control device) and was maintained above 10% after 230 h of constant illumination.

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 score0.006
Threshold uncertainty score0.327

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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