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Record W2759315266 · doi:10.1039/c7nr04170e

Phenolic condensation and facilitation of fluorescent carbon dot formation: a mechanism study

2017· article· en· W2759315266 on OpenAlexaff
Kyueui Lee, Eunsook Park, Haesung A. Lee, Caroline Sugnaux, Mikyung Shin, Chan Jin Jeong, Jeehee Lee, Phillip B. Messersmith, Sung Young Park, Haeshin Lee

Bibliographic record

VenueNanoscale · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsKootenay Association for Science & Technology
FundersNational Institute of Dental and Craniofacial ResearchNational Institutes of Health
KeywordsCondensationFluorescenceFacilitationMechanism (biology)Carbon fibersMaterials scienceChemistryNanotechnologyPhotochemistryChemical engineeringComposite materialBiology

Abstract

fetched live from OpenAlex

Fluorescent carbon dots have received considerable attention as a result of their accessibility and potential applications. Although several prior studies have demonstrated that nearly any organic compound can be converted into carbon dots by chemical carbonization processes, mechanisms explaining the formation of carbon dots still remain unclear. Herein, we propose a seed-growth mechanism of carbon dot formation facilitated by ferulic acid, a widespread and naturally occurring phenolic compound in the seeds of Ocimum basilicum (basil). Ferulic acid triggers the local condensation of polysaccharide chains and forms catalytic core regions resulting in nanoscale carbonization. Our study indicates that carbon dots generated from natural sources might share the similar mechanism of phenolic compound mediated nanoscale condensation followed by core carbonization.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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 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

Citations42
Published2017
Admission routes1
Has abstractyes

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