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Record W4311082212 · doi:10.1039/d2na00619g

Synthesis of fluorescent carbon nanoparticles by dispersion polymerization of acetylene

2022· article· en· W4311082212 on OpenAlexafffund
Vijay Kumar Jayswal, Anna M. Ritcey, Jean‐François Morin

Bibliographic record

VenueNanoscale Advances · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsDispersityMaterials sciencePolymerizationNanoparticleDispersion polymerizationFluorescenceAcetyleneDispersion (optics)Carbon fibersChemical engineeringSurface modificationNanotechnologyPolymer chemistryChemistryOrganic chemistryPolymerComposite materialOpticsComposite number

Abstract

fetched live from OpenAlex

Carbon nanoparticles (CNPs) are of interest due to their distinct optoelectronic properties for a diverse range of applications and their functions and properties can be changed by varying their shape, size and dimensionality. The current synthetic methods reported often result in uncontrolled shape, size and polydispersity. In this work, we focus on developing a low-temperature synthetic method for preparing fluorescent carbon nanoparticles and modulation of properties. Our method, based on the dispersion Glaser-Hay polymerization of acetylene followed by decomposition into a carbonaceous material, yields CNPs with sizes varying from 30 nm to 60 nm. The change in reaction parameters influences the shape and size of CNPs, yielding spherical CNPs. The residual alkynes were exploited further for post-functionalization/graphitization by UV irradiation to yield multifunctional CNPs, which were fluorescent in the blue region. The CNPs were characterized with microscopy and spectroscopy techniques after synthesis and after UV-irradiation to study the morphological, chemical, physical and optical properties. This allowed us to understand the influence of parameter variation on the properties and to attempt to establish the structure-property relationship.

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 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 score0.286

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 head0.005
GPT teacher head0.226
Teacher spread0.220 · 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.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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