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Record W2330785013 · doi:10.1039/c6nh00010j

Using carbon nanodots as inexpensive and environmentally friendly sensitizers in mesoscopic solar cells

2016· article· en· W2330785013 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.

Bibliographic record

VenueNanoscale Horizons · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsNanodotEnvironmentally friendlyMesoscopic physicsNanotechnologyCarbon fibersMaterials scienceOptoelectronicsPhysicsComposite materialBiology

Abstract

fetched live from OpenAlex

We discuss the use of carbon nanodots (CNDs) as sensitizers in mesoscopic solar cells. The CNDs are synthesized using a one-step, bottom-up microwave approach with citric acid, urea, and formic acid as precursors in aqueous media. Their light-harvesting capabilities can be tuned by adjusting the synthetic parameters. Comprehensive spectroscopic and theoretical studies allow us to rationalize the nature of their absorption features. Promising power conversion efficiencies (η) of 0.24% can be achieved from these cheap and eco-friendly sensitizers by optimizing the solar-cell assembly process. Interestingly, we found that extending the light absorption towards longer wavelengths does not necessarily improve the performance of the solar cells, since the longer-wavelength absorption features hardly contribute to the cells' photo-action spectra, so that the overall power conversion efficiency is actually worse. The origin of the lower performance is corroborated in transient absorption spectroscopy and photovoltage decay measurements. Our work points, on one hand, to the limits of as-synthesized CNDs as photosensitizers and, on the other hand, to possible improvements.

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)0.000
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 score0.004
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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