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Record W2084148131 · doi:10.1063/1.4898635

Colloidal quantum dot solar cells on curved and flexible substrates

2014· article· en· W2084148131 on OpenAlexaff
Illan J. Kramer, Gabriel Moreno‐Bautista, J.C. Minor, Damir Kopilovic, Edward H. Sargent

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsIBM (Canada)University of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsMaterials scienceQuantum dotCoatingNanotechnologySubstrate (aquarium)Solar cellQuantum dot solar cellMicrotechnologyOptoelectronicsPolymer solar cell

Abstract

fetched live from OpenAlex

Colloidal quantum dots (CQDs) are semiconductor nanocrystals synthesized with, processed in, and deposited from the solution phase, potentially enabling low-cost, facile manufacture of solar cells. Unfortunately, CQD solar cell reports, until now, have only explored batch-processing methods—such as spin-coating—that offer limited capacity for scaling. Spray-coating could offer a means of producing uniform colloidal quantum dot films that yield high-quality devices. Here, we explore the versatility of the spray-coating method by producing CQD solar cells in a variety of previously unexplored substrate arrangements. The potential transferability of the spray-coating method to a roll-to-roll manufacturing process was tested by spray-coating the CQD active layer onto six substrates mounted on a rapidly rotating drum, yielding devices with an average power conversion efficiency of 6.7%. We further tested the manufacturability of the process by endeavoring to spray onto flexible substrates, only to find that spraying while the substrate was flexed was crucial to achieving champion performance of 7.2% without compromise to open-circuit voltage. Having deposited onto a substrate with one axis of curvature, we then built our CQD solar cells onto a spherical lens substrate having two axes of curvature resulting in a 5% efficient device. These results show that CQDs deposited using our spraying method can be integrated to large-area manufacturing processes and can be used to make solar cells on unconventional shapes.

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 score0.017
Threshold uncertainty score0.740

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)0.0000.001

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 head0.019
GPT teacher head0.210
Teacher spread0.192 · 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

Citations77
Published2014
Admission routes1
Has abstractyes

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