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Record W2541778707 · doi:10.1063/1.4966217

Single-step colloidal quantum dot films for infrared solar harvesting

2016· article· en· W2541778707 on OpenAlexaff
Amirreza Kiani, Brandon R. Sutherland, Younghoon Kim, Olivier Ouellette, Larissa Levina, Grant Walters, Cao‐Thang Dinh, Mengxia Liu, Oleksandr Voznyy, Xinzheng Lan, André J. Labelle, Alexander H. Ip, Andrew H. Proppe, Ghada H. Ahmed, Omar F. Mohammed, Sjoerd Hoogland, Edward H. Sargent

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceInfraredOptoelectronicsSolar cellQuantum dot solar cellSiliconQuantum dotBand gapSemiconductorHybrid solar cellEnergy conversion efficiencyGermaniumNanotechnologyPolymer solar cellOpticsPhysics

Abstract

fetched live from OpenAlex

Semiconductors with bandgaps in the near- to mid-infrared can harvest solar light that is otherwise wasted by conventional single-junction solar cell architectures. In particular, colloidal quantum dots (CQDs) are promising materials since they are cost-effective, processed from solution, and have a bandgap that can be tuned into the infrared (IR) via the quantum size effect. These characteristics enable them to harvest the infrared portion of the solar spectrum to which silicon is transparent. To date, IR CQD solar cells have been made using a wasteful and complex sequential layer-by-layer process. Here, we demonstrate ∼1 eV bandgap solar-harvesting CQD films deposited in a single step. By engineering a fast-drying solvent mixture for metal iodide-capped CQDs, we deposited active layers greater than 200 nm in thickness having a mean roughness less than 1 nm. We integrated these films into infrared solar cells that are stable in air and exhibit power conversion efficiencies of 3.5% under illumination by the full solar spectrum, and 0.4% through a simulated silicon solar cell filter.

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 score0.025
Threshold uncertainty score0.700

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)0.0000.000

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 head0.031
GPT teacher head0.221
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

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

Citations62
Published2016
Admission routes1
Has abstractyes

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