Synthesis of Fluorescent Carbon Nanoparticles by Dispersion Polymerizationof Acetylene
Bibliographic record
Abstract
Carbon nanoparticles (CNPs) have emerged as one of the most promising nanomaterials due to their distinct optoelectronic properties for a diverse range of applications in the area of electronics, energy conversion and storage, and bio-imaging. Their functions and properties can be changed by varying their shape, size and dimensionality. The synthetic methods reported until now involve high-temperature (>100 °C) processes, which often result in uncontrolled shape, size and polydispersity. In this work, we focus on the development of a low-temperature synthetic method for the preparation of 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 shape and size of the resulting carbon nanoparticles are influenced by changing different reaction parameters such as temperature, reaction time and pressure. The control over the different reaction parameters allows us to obtain monodisperse CNPs in spherical shapes. After isolation, CNPs were characterized by microscopy and spectroscopy techniques. The residual alkynes in the CNPs’ structure were exploited for further post-functionalization/ graphitization to yield multifunctional CNPs, which were fluorescent in the blue region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".