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Record W1193490646 · doi:10.1021/acs.jpclett.5b00765

Modulating Exciton Dynamics in Composite Nanocrystals for Excitonic Solar Cells

2015· article· en· W1193490646 on OpenAlexaff
Isabella Concina, Cristian Manzoni, Giulia Grancini, Mert Çelikin, Afsoon Soudi, Federico Rosei, M. Zavelani–Rossi, Giulio Cerullo, Alberto Vomiero

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceQuantum dotChalcogenideOptoelectronicsPhotovoltaic systemNanocrystalExcitonNanoparticleHeterojunctionNanotechnologyCharge carrierPhotoinduced charge separationComposite numberCadmium selenideHybrid solar cellPolymer solar cellEnergy conversion efficiencyPhotocatalysisChemistryArtificial photosynthesisPhysics

Abstract

fetched live from OpenAlex

Quantum dots (QDs) represent one of the most promising materials for third-generation solar cells due to their potential to boost the photoconversion efficiency beyond the Shockley-Queisser limit. Composite nanocrystals can challenge the current scenario by combining broad spectral response and tailored energy levels to favor charge extraction and reduce energy and charge recombination. We synthesized PbS/CdS QDs with different compositions at the surface of TiO2 nanoparticles assembled in a mesoporous film. The ultrafast photoinduced dynamics and the charge injection processes were investigated by pump-probe spectroscopy. We demonstrated good injection of photogenerated electrons from QDs to TiO2 in the PbS/CdS blend and used the QDs to fabricate solar cells. The fine-tuning of chemical composition and size of lead and cadmium chalcogenide QDs led to highly efficient PV devices (3% maximum photoconversion efficiency). This combined study paves the way to the full exploitation of QDs in next-generation photovoltaic (PV) devices.

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 score0.026
Threshold uncertainty score0.337

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)0.0000.000

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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
Published2015
Admission routes1
Has abstractyes

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