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Record W2416902902 · doi:10.1038/srep12420

Eco-friendly carbon-nanodot-based fluorescent paints for advanced photocatalytic systems

2015· article· en· W2416902902 on OpenAlexaff
So‐Young Park, Hyun Uk Lee, Young‐Chul Lee, Saehae Choi, Dae Hyun Cho, Hee Sik Kim, Sunghee Bang, Soonjoo Seo, Soon Chang Lee, Jonghan Won, Byoungchul Son, Mino Yang, Jouhahn Lee

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Waterloo
FundersKorea Basic Science Institute
KeywordsFluorescencePhotocatalysisBiocompatibilityNanomaterialsNanodotMaterials sciencePolyethylene glycolNanotechnologyPhotoluminescenceCarbon fibersPhotobleachingNanoparticlePhotochemistryChemical engineeringChemistryOptoelectronicsCatalysisOrganic chemistryComposite numberOptics

Abstract

fetched live from OpenAlex

Fluorescent carbon nanomaterials, especially zero-dimensional (0D) carbon nanodots (CDs), are widely used in broad biological and optoelectronic applications. CDs have unique characteristics such as strong fluorescence, biocompatibility, sun-light response, and capability of mass-production. Beyond the previous green CD obtained from harmful natural substances, we report a new type of fluid-based fluorescent CD paints (C-paints) derived from polyethylene glycol (PEG; via simple ultrasound irradiation at room temperatures) and produced in quantum yields of up to ~14%. Additionally, C-paints possess a strong, UV- and visible-light-responsive photoluminescent (PL) property. Most especially, C-paints, by incorporation into a photocatalytic system, show additional roles in the emission of fluorescent light for activation of TiO2 nanoparticles (NPs) and the resultant detoxification of most organic dyes, thus further enabling embarkation in advanced water purification.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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