Synthesis of Shape and Size-Tunable Carbon Nanoparticles By 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 in the area of electronics, energy conversion/ storage, and bio-imaging. The properties of photoluminescence, photostability, and low toxicity makes them a potential candidate for various 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. The origin of photoluminescence of carbon nanoparticles is still a topic of debate but studies have shown that the carbon core is responsible for the strong absorption of light while the luminescence comes from the surface sites and the functional groups present on the surface. The uniqueness in terms of functions and properties of the CNPs gets more interesting as it changes distinctly with a change in the shape, size, and dimensionality of these nanoparticles. Despite several advantages and unique properties, the transformation from the laboratory to industrial products has been slow for carbon nanoparticles because of the difficulty in synthesizing and in controlling the size of CNPs. The control over the shape and size of nanoparticles is important as the optical properties of CNPs are shown to be varying with the variation in shape and size. The synthetic methods reported until now involves high-temperature (>100 oC) processes which often results in uncontrolled shape, size, polydisperse and chemically inert nanoparticles, increasing the difficulty to modulate their morphological, optical, and electronic properties. Thus, the development of low temperature and controlled synthesis method is desirable. Here, we describe the development of a low-temperature synthetic method for the preparation of carbon nanoparticles allowing precise control over the shape, size, and properties by dispersion polymerization of sp-carbon rich precursors. These sp-carbon rich precursors (butadiyne and acetylene) tend to become thermodynamically unstable when polymerized to long polyyne chains and decompose inside the reaction mixture to give CNPs. Hence, these polyyne intermediates provide us with the control over the size and shape of CNPs during the reaction and in turn, over their properties for further modulation and functionalization. The shape- & size-tunable nanoparticles were synthesized in a single step with dispersion polymerization by Glaser-Hay coupling. The shape and size of the resulting carbon nanoparticles are controlled by changing different reaction parameters such as temperature, monomer loading, reaction concentration, and pressure. The control over the different reaction parameters allows us to obtain monodisperse CNPs in spherical and tubular shapes with a size in the range of 25 nm to 250 nm and use of low-temperature methods (<100 oC) allows us to overcome the limitations associated with current methods. After isolation, CNPs were characterized by dynamic light scattering, scanning and transmission electron microscopy to analyze the shape and size of the CNPs. To analyze the graphitic nature and presence of sp2 -rich carbon of the resulting nanoparticles were characterized using spectroscopic techniques such as XPS spectroscopy, Auger spectroscopy, Raman spectroscopy and FTIR spectroscopy, etc. The nanoparticles were characterized to be highly fluorescence. Studying the optoelectronic behavior of CNPs helped us in establishing the structure-property relationship.
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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".