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Record W3012399519 · doi:10.1002/adma.201906199

A Chemically Orthogonal Hole Transport Layer for Efficient Colloidal Quantum Dot Solar Cells

2020· article· en· W3012399519 on OpenAlexafffund
Margherita Biondi, Min‐Jae Choi, Olivier Ouellette, Se‐Woong Baek, Petar Todorović́, Bin Sun, Seungjin Lee, Mingyang Wei, Peicheng Li, Ahmad R. Kirmani, Laxmi Kishore Sagar, Lee J. Richter, Sjoerd Hoogland, Zheng‐Hong Lu, F. Pelayo Garcı́a de Arquer, Edward H. Sargent

Bibliographic record

VenueAdvanced Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaBrookhaven National LaboratoryOffice of ScienceU.S. Department of Energy
KeywordsMaterials scienceQuantum dotActive layerEnergy conversion efficiencyNanotechnologyOptoelectronicsLayer (electronics)ColloidChemical engineering

Abstract

fetched live from OpenAlex

Colloidal quantum dots (CQDs) are of interest in light of their solution-processing and bandgap tuning. Advances in the performance of CQD optoelectronic devices require fine control over the properties of each layer in the device materials stack. This is particularly challenging in the present best CQD solar cells, since these employ a p-type hole-transport layer (HTL) implemented using 1,2-ethanedithiol (EDT) ligand exchange on top of the CQD active layer. It is established that the high reactivity of EDT causes a severe chemical modification to the active layer that deteriorates charge extraction. By combining elemental mapping with the spatial charge collection efficiency in CQD solar cells, the key materials interface dominating the subpar performance of prior CQD PV devices is demonstrated. This motivates to develop a chemically orthogonal HTL that consists of malonic-acid-crosslinked CQDs. The new crosslinking strategy preserves the surface chemistry of the active layer beneath, and at the same time provides the needed efficient charge extraction. The new HTL enables a 1.4× increase in charge carrier diffusion length in the active layer; and as a result leads to an improvement in power conversion efficiency to 13.0% compared to EDT standard cells (12.2%).

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), Insufficient 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 score0.028
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.243
Teacher spread0.217 · 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

Citations102
Published2020
Admission routes2
Has abstractyes

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