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Record W2555148678 · doi:10.1021/acsnano.6b06358

Surpassing the Exciton Diffusion Limit in Single-Walled Carbon Nanotube Sensitized Solar Cells

2016· article· en· W2555148678 on OpenAlexfundno aff
Ghada I. Koleilat, Michael Vosgueritchian, Ting Lei, Yan Zhou, Debora Lin, Franziska Lissel, Pei Lin, John W. F. To, Tian Xie, Kemar England, Yue Zhang, Zhenan Bao

Bibliographic record

VenueACS Nano · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMaterials scienceCarbon nanotubeBand gapExcitonHeterojunctionOptoelectronicsDopingPolymer solar cellNanotechnologyCharge carrierAcceptorNanotubeEnergy conversion efficiency

Abstract

fetched live from OpenAlex

Semiconducting single-walled carbon nanotube (s-SWNT) light sensitized devices, such as infrared photodetectors and solar cells, have recently been widely reported. Despite their excellent individual electrical properties, efficient carrier transport from one carbon nanotube to another remains a fundamental challenge. Specifically, photovoltaic devices with active layers made from s-SWNTs have suffered from low efficiencies caused by three main challenges: the overwhelming presence of high-bandgap polymers in the films, the weak bandgap offset between the LUMO of the s-SWNTs and the acceptor C 60, and the limited exciton diffusion length from one SWNT to another of around 5 nm that limits the carrier extraction efficiency. Herein, we employ a combination of processing and device architecture design strategies to address each of these transport challenges and fabricate photovoltaic devices with s-SWNT films well beyond the exciton diffusion limit of 5 nm. While our solution processing method minimizes the presence of undesired polymers in our active films, our interfacial designs led to a significant increase in current generation with the addition of a highly doped C 60 layer (n-doped C 60 ), resulting in increased carrier separation efficiency from the s-SWNTs films. We create a dense interconnected nanoporous mesh of s-SWNTs using solution shearing and infiltrate it with the acceptor C 60 . Thus, our final engineered bulk heterojunction allows carriers from deep within to be extracted by the C 60 registering a 10-fold improvement in performance from our preliminary structures.

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 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 score0.004
Threshold uncertainty score0.712

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 science0.0010.000
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 head0.013
GPT teacher head0.214
Teacher spread0.200 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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