Synthesis of core–shell copper–graphite submicronic particles and carbon nano-onions by spark discharges in liquid hydrocarbons
Bibliographic record
Abstract
Spark discharge in hydrocarbon liquids is considered a promising method for the synthesis of various nanomaterials, including nanocomposites. In this study, copper-carbon particles were synthesized by generating spark discharges between two Cu electrodes immersed in heptane, cyclohexane, or toluene. The synthesized particles were characterized using scanning electron microscopy, high-resolution transmission electron microscopy, and selected area electron diffraction. Overall, two families of particles were observed: Cu particles (diameter < 10 nm) embedded in a carbon matrix and submicrometric Cu particles encapsulated in a carbon shell. The obtained results indicate that the size distribution of the Cu nanoparticles and the degree of graphitization of the carbon matrix depend on the liquid. Indeed, discharges in heptane lead to Cu particles with diameters of 2-6 nm embedded in a carbon matrix of low graphitization degree, while discharges in toluene result in particles with diameters of 2-14 nm embedded in carbon matrix of high graphitization degree. Based on the obtained experimental results, it is proposed that the Cu nanoparticles are produced in the plasma core where Cu (evaporated from the electrode surface) and carbonaceous species (decomposition of the liquid) are present. When the plasma hits the electrode surface, hot (thousands of Kelvin) Cu particles are ejected from the electrode, and they propagate in the liquid. The propagation of the hot particles in the liquid results in the local evaporation of this liquid, which leads to the formation of a C-shell around each Cu particle. In few cases where the shape of the Cu particle is not spherical, carbon nanoonions are detected between the C-shell and the Cu core. These nanoonions are supposedly formed under the effect of the fluid vortices generated close to the particle surfaces when these latter are ejected into the liquid.
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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) | 0.000 | 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".