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Record W3120370986 · doi:10.1021/acsenergylett.0c02500

Control Over Ligand Exchange Reactivity in Hole Transport Layer Enables High-Efficiency Colloidal Quantum Dot Solar Cells

2021· article· en· W3120370986 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Energy Letters · 2021
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research Foundation
KeywordsQuantum dotColloidEnergy conversion efficiencyNanotechnologyReactivity (psychology)SolventMaterials scienceChemistryBand gapChemical engineeringOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Colloidal quantum dot (CQD) solar cells are solution-processed photovoltaic devices that exhibit promise in harvesting the infrared solar spectrum. Solid-state ligand exchange is the method employed to fabricate the CQD hole transport layer (HTL) in these cells: insulating oleic acid ligands are substituted with short thiol ligands (1,2-ethanedithiol) to create conductive p-type CQD solids. Thiols’ high reactivity with the CQD surface results in rapid exchange, giving rise to aggregates of dots and unpassivated sites on dots, each contributing to sub-bandgap trap states. Here we report a strategy to minimize trap states in the CQD HTL by controlling the solvent type in the exchange. By employing a less volatile solvent, we achieve a slower reaction, leading to increased order and a 2 times reduced trap density in CQD solids. These improvements enable a power conversion efficiency of 13.1 ± 0.1% in CQD solar cells compared to control devices showing 12.4 ± 0.1%.

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.

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 score0.024
Threshold uncertainty score0.977

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 head0.201
Teacher spread0.189 · 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