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Record W2323227954 · doi:10.1021/nl304604y

Jointly Tuned Plasmonic–Excitonic Photovoltaics Using Nanoshells

2013· article· en· W2323227954 on OpenAlexaff
Daniel Paz‐Soldan, Anna Lee, Susanna M. Thon, Michael M. Adachi, Haopeng Dong, P. Maraghechi, Mingjian Yuan, André J. Labelle, Sjoerd Hoogland, Kun Liu, Eugenia Kumacheva, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPlasmonPhotovoltaicsNanoshellPhotocurrentMaterials scienceOptoelectronicsPlasmonic nanoparticlesAbsorption (acoustics)Plasmonic solar cellQuantum dotSolar cellAbsorption cross sectionPhotovoltaic systemNanotechnologyPhysicsPolymer solar cellCross section (physics)

Abstract

fetched live from OpenAlex

Recent advances in spectrally tuned, solution-processed plasmonic nanoparticles have provided unprecedented control over light's propagation and absorption via engineering at the nanoscale. Simultaneous parallel progress in colloidal quantum dot photovoltaics offers the potential for low-cost, large-area solar power; however, these devices suffer from poor quantum efficiency in the more weakly absorbed infrared portion of the sun's spectrum. Here, we report a plasmonic-excitonic solar cell that combines two classes of solution-processed infrared materials that we tune jointly. We show through experiment and theory that a plasmonic-excitonic design using gold nanoshells with optimized single particle scattering-to-absorption cross-section ratios leads to a strong enhancement in near-field absorption and a resultant 35% enhancement in photocurrent in the performance-limiting near-infrared spectral region.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.213
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 source (direct Gemma or distilled Codex), 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

Citations116
Published2013
Admission routes1
Has abstractyes

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