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Record W2315717541 · doi:10.1021/jz502227h

Quantum-Dot-Based Solar Cells: Recent Advances, Strategies, and Challenges

2014· article· en· W2315717541 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum dotPhotovoltaic systemMultiple exciton generationNanotechnologySolar cellComputer scienceEngineering physicsMaterials sciencePhysicsOptoelectronicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Among next-generation photovoltaic systems requiring low cost and high efficiency, quantum dot (QD)-based solar cells stand out as a very promising candidate because of the unique and versatile characteristics of QDs. The past decade has already seen rapid conceptual and technological advances on various aspects of QD solar cells, and diverse opportunities, which QDs can offer, predict that there is still ample room for further development and breakthroughs. In this Perspective, we first review the attractive advantages of QDs, such as size-tunable band gaps and multiple exciton generation (MEG), beneficial to solar cell applications. We then analyze major strategies, which have been extensively explored and have largely contributed to the most recent and significant achievements in QD solar cells. Finally, their high potential and challenges are discussed. In particular, QD solar cells are considered to hold immense potential to overcome the theoretical efficiency limit of 31% for single-junction cells.

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.

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 score0.029
Threshold uncertainty score0.357

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 head0.024
GPT teacher head0.229
Teacher spread0.205 · 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