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Record W4390975115 · doi:10.26434/chemrxiv-2024-cx9rk

Photoinduced Luminescence Activation of Hydrophilic ‘Caged’ Carbons Dots

2024· preprint· en· W4390975115 on OpenAlexafffund
Aviya S. Akari, Maria Narciso, E. D. Fagbohun, Roberto J. Botelho, Stefania Impellizzeri

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsToronto Metropolitan University
FundersOntario Ministry of Economic Development, Job Creation and TradeMinistero dello Sviluppo EconomicoNatural Sciences and Engineering Research Council of CanadaToronto Metropolitan University
KeywordsLuminescenceCovalent bondNanomaterialsCarbon fibersPhotochemistryMaterials scienceNanotechnologyUltravioletChemistryOptoelectronicsOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

As part of our efforts to develop nanomaterials with tunable optical properties, we devised a synthetic protocol to photoactivate the luminescence of hydrophilic carbon dots by ‘caging’ the nanostructures with photocleavable 2-nitrobenzyl quenchers. Photoremovable 2-nitrobenzyl groups can be attached covalently to the surface of the carbon dots via amide-bond formation. We show that 2-nitrobenzyls efficiently quench the emission intensity of the resulting nanoconstructs and that the luminescence can be activated upon ultraviolet illumination in solution. In addition, the carbon dots can be internalized by living cells and used as bioimaging agents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.277
Teacher spread0.250 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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