MétaCan
Menu
Back to cohort
Record W2095831961 · doi:10.1002/chem.201502158

An Efficient Templating Approach for the Synthesis of Redispersible Size‐Controllable Carbon Quantum Dots from Graphitic Polymeric Micelles

2015· article· en· W2095831961 on OpenAlexafffund
Jianming Zhang, Farshad Abbasi, Jérôme P. Claverie

Bibliographic record

VenueChemistry - A European Journal · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoreactorMicelleRaftMaterials sciencePolymerizationCopolymerQuantum yieldChain transferNanoparticleAcrylic acidChemical engineeringQuantum dotNanotechnologyPolymerRadical polymerizationOrganic chemistryChemistryAqueous solution

Abstract

fetched live from OpenAlex

Access to high-quality, easily dispersible carbon quantum dots (CQDs) is essential in order to fully exploit their desirable properties. Copolymers based on N-acryloyl-D-glucosamine and acrylic acid prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization are self-assembled into micelle-like nanoreactors. After a facile graphitization process (170 °C, atmospheric pressure), each micellar template is transformed into a CQD through a 1:1 copy process. These high-quality CQDs (quantum yield=22 %) with tunable sizes (2-5 nm) are decorated by carboxylic acid moieties and can be spontaneously redispersed in water and polar organic solvents. This preparation method renders the mass production of multifunctional CQDs possible. To demonstrate the versatility of this approach, CQDs hybridized TiO2 nanoparticles with enhanced photocatalytic activity under visible-light have been prepared.

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 score0.002

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 head0.240
Teacher spread0.215 · 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

Citations32
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueChemistry - A European JournalSame topicCarbon and Quantum Dots ApplicationsFrench-language works237,207