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Record W3182658772 · doi:10.1038/s41598-021-93212-w

Perception on aggregation induced multicolor emission and emission centers in carbon nanodots using successive dilution, anion exchange chromatography, and multi-way statistics

2021· article· en· W3182658772 on OpenAlexaff
Mohsen Kompany‐Zareh, Saeed Bagheri

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNanodotDilutionIon chromatographyIonMaterials scienceCarbon fibersAnalytical Chemistry (journal)ChemistryNanotechnologyPhysicsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Exploration in the way of understanding the optical behavior and structure of carbon nanodots has been increased due to their vast application. Their emission dependency on excitation wavelengths is the more prevalent and controversial subject. In this report we considered the optical structure of hydrothermally synthesized carbon nanodots using citric acid and 2,3-diaminopyridine as precursors. The presence of different emission centers experimented through anion exchange chromatography which resulted in fractions with more unique optical structures. The quantum confinement effect and energy exchange between different types of carbon nanodots, due to aggregation in higher concentration levels, was studied applying a stepwise dilution experiment. Analysis of the experimental data was done through the parallel factor analysis and the trajectory pattern recognition which resolved more about optical interactions and the presence of different emission centers in different particles. Results from infrared spectroscopy confirmed the dominating density of carboxyl functional groups on the nanodots with negative surface charges and higher influence of amine groups on dots with positive surface charges.

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)0.001
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 score0.173
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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