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Record W3192786521 · doi:10.1021/acs.jpclett.1c01856

Theoretical Understanding of Structure–Property Relationships in Luminescence of Carbon Dots

2021· review· en· W3192786521 on OpenAlexaff
Jingkun Yu, Xue Yong, Zhiyong Tang, Bai Yang, Siyu Lu

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2021
Typereview
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Henan ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsLuminescenceMaterials scienceProperty (philosophy)Quantum dotNanotechnologyPhosphorescenceCarbon fibersOptoelectronicsEngineering physicsFluorescencePhysicsOptics

Abstract

fetched live from OpenAlex

Carbon dots (CDs) have excellent luminescence characteristics, such as good light stability, high quantum yield (QY), long phosphorescence lifetime, and a wide emission wavelength range, resulting in CDs' great success in optical applications. Understanding the structure-property relationships in CDs is essential for their use in optoelectronic applications. However, because of the complex nature of CD structures and synthesis processes, understanding the luminescence mechanism and structure-property relationships of CDs is a big challenge. This Perspective reviews the theoretical efforts toward the understanding of structure-property relationships and discusses the challenges that need to be overcome in future development of CDs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.299
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations210
Published2021
Admission routes1
Has abstractyes

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