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Record W2749169038 · doi:10.1002/adfm.201770178

Solar Cells: Highly Stable Colloidal “Giant” Quantum Dots Sensitized Solar Cells (Adv. Funct. Mater. 30/2017)

2017· article· en· W2749169038 on OpenAlexaff
Gurpreet Singh Selopal, Haiguang Zhao, Xin Tong, Daniele Benetti, Fabiola Navarro‐Pardo, Yufeng Zhou, David Barba, François Vidal, Zhiming M. Wang, Federico Rosei

Bibliographic record

VenueAdvanced Functional Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsQuantum dotMaterials sciencePhotovoltaic systemColloidShell (structure)NanotechnologyOptoelectronicsCore (optical fiber)Quantum dot solar cellSolar cellChemical engineeringComposite materialPolymer solar cell

Abstract

fetched live from OpenAlex

The photovoltaic performance of core/shell quantum dots sensitized solar cells (QDSCs) can be significantly enhanced by optimizing the shell thickness, as demonstrated by Haiguang Zhao, Zhiming M. Wang, Federico Rosei, and co-workers in article number 1701468. By maintaining the core size of the quantum dot, the approach offers new opportunities to fabricate low cost, highly efficient, and long-term stable QDSCs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.007

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 head0.025
GPT teacher head0.236
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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