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Record W2765502567 · doi:10.1021/acsami.7b11449

Halide Re-Shelled Quantum Dot Inks for Infrared Photovoltaics

2017· article· en· W2765502567 on OpenAlexafffund
James Z. Fan, Mengxia Liu, Oleksandr Voznyy, Bin Sun, Larissa Levina, Rafael Quintero‐Bermudez, Min Liu, Olivier Ouellette, F. Pelayo Garcı́a de Arquer, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePhotovoltaicsHalideQuantum dotPhotoluminescenceAbsorbanceEnergy conversion efficiencyPerovskite (structure)SiliconChemical engineeringPhotochemistryOptoelectronicsInorganic chemistryPhotovoltaic systemChemistry

Abstract

fetched live from OpenAlex

Colloidal quantum dots are promising materials for tandem solar cells that complement silicon and perovskites. These devices are fabricated from solution phase; however, existing methods for making infrared-bandgap CQD inks suffer agglomeration and fusion during solution exchange. Here we develop a ligand exchange that provides robust surface protection and thereby avoids aggregation. First, we exchanged long oleic acid ligands to a mixed system comprising medium-chain ammonium and anionic chloride ligands; we then reshelled the surface using short halides and pseudohalide ligands that enabled transfer to a polar solvent. Absorbance and photoluminescence measurements reveal the retention of exciton sharpness, whereas X-ray photoelectron spectroscopy indicates halide capping. The best power conversion efficiency of these devices is 0.76 power points after filtering through silicon, which is 1.9× higher than previous single-step solution-processed IR-CQD solar cells.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.282
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

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

Citations42
Published2017
Admission routes2
Has abstractyes

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