Pulsed nanosecond discharge in heptane in contact with Ag solution: Feasibility of nanoparticles synthesis
Bibliographic record
Abstract
Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. Plasma-liquid systems have a high potential of producing numerous nanomaterials due to their easy substructure, environmentally friendly, high efficiency, and safety. In this paper, we build a novel Plasma-liquid technique in which spark discharges at 22 kV of amplitude and 500 ns of pulse width are ignited in heptane. This hydrocarbon liquid is in contact with the conductive solution containing silver nitrate solution. The main reason of producing ag nanoparticles is the reducing silver ions in the solution which is happened by the interaction between these ions and the high-pressure spark discharge which has a high density of species. After 30 minutes treatment with discharges, liquid samples from both heptane and solution side were collected to analysis by different techniques like FTIR, XRD, EDS, TEM, etc. It is determined that in heptane side, nanocomposites of silver nanoparticles in two ranges; less than 10 nm and between 10 to 45 nm are embedded in carbon matrix. And silver nanoparticles from 10 to 159 nm are synthesized in the solution sample. To study the effect of different pulse width on produced nanomaterials, the experiments repeat at shorter pulse width (100 ns). The results show the same materials in both liquids but, the size distribution of nanoparticles was smaller than large pulse width.
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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".