(Digital Presentation) Synthesis of Fluorescent Carbon Nanoparticles (CNPs) By Microwave-Assisted Polymerization of Sp-Carbon Rich Precursors
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. These unique properties arise from the network of hybridized sp2 carbon atoms as it allows delocalization of the electrons over the entire surface of the molecule. These surface groups help CNPs emit strong fluorescence in visible region. The significant fluorescence of CNPs combined with low toxicity, and photostability makes them suitable for various applications in biosensing, bio-imaging, and OLEDs. Despite several advantages and unique properties, the transformation from laboratory to industrial products has been slow for carbon nanoparticles. The synthetic methods reported until now chemically inert nanoparticles, increasing the difficulty to modulate their morphological, optical, and electronic properties. The organic synthesis methods for synthesizing CNPs often involve several synthesis steps, long reaction time, low yield, non-scalable and inefficient purification methods. Most of these synthesis methods involve several catalysts and removal of catalysts is always a challenge. Resulting nanoparticles also need functionalization using another synthesis step to give them properties such as fluorescence and making them soluble. Therefore, a synthesis method with minimum steps, minimum catalysts which could result in soluble and fluorescence nanoparticles is desired. This work investigates the use of highly reactive sp-carbon rich precursors for polymerization with microwave heating with minimum use of catalysts resulting in fluorescent CNPs. The sp-carbon rich precursors such as TMS-Benzene, butadiyne and acetylene were used as starting material. These alkynes are highly reactive and reacts to other alkynes present in the system in the presence of heat and form polyyne intermediates which are thermodynamically unstable in the form of long chains and decompose to provide us with CNPs. The plan is to use the high reactivity with microwave heating for polymerization without catalysts and further investigate the use of oxidants or minimum catalysts to increase the rate of polymerization and conversion into CNPs. The resulting nanoparticles required minimum purification in centrifuge and were isolated easily as there were no catalysts involved. The resulting nanoparticles were characterized with various techniques to study the morphology, structure, composition, and optical properties of CNPs. The resulting nanoparticles had blue fluorescence under the UV lamp. The single step synthesis of blue fluorescence CNPs was performed with microwave assisted polymerization of sp-carbon rich precursors without any catalysts. Further investigation of this method will provide a single step synthesis method for fluorescent CNPs which would be suitable for application in sensing and OLEDs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.170 | 0.039 |
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".