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Record W2229503627 · doi:10.1002/adfm.201503338

Electro‐Optics of Colloidal Quantum Dot Solids for Thin‐Film Solar Cells

2016· article· en· W2229503627 on OpenAlexfundno aff
Xiaoliang Zhang, Carl Hägglund, Erik M. J. Johansson

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersEnergimyndighetenVetenskapsrådetUniversity of TorontoSvenska Forskningsrådet FormasGöran Gustafssons Stiftelser
KeywordsQuantum dotMaterials sciencePhotocurrentQuantum dot solar cellOptoelectronicsSolar cellPhotovoltaic systemMultiple exciton generationExcitonHeterojunctionQuantum opticsThin filmPlasmonic solar cellEnergy conversion efficiencySolar cell efficiencyPolymer solar cellOpticsNanotechnologyCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

The electro‐optics of thin‐film stacks within photovoltaic devices plays a critical role for the exciton and charge generation and therefore the photovoltaic performance. The complex refractive indexes of each layer in heterojunction colloidal quantum dot (CQD) solar cells are measured and the optical electric field is simulated using the transfer matrix formalism. The exciton generation rate and the photocurrent density as a function of the quantum dot solid thickness are calculated and the results from the simulations are found to agree well with the experimentally determined results. It can therefore be concluded that a quantum dot solid may be modeled with this approach, which is of general interest for this type of materials. Optimization of the CQD solar cell is performed by using the optical simulations and a maximum solar energy conversion efficiency of 6.5% is reached for a CQD solid thickness of 300 nm.

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 categoriesInsufficient 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 score0.008
Threshold uncertainty score0.999

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 science0.0000.000
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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

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