Maximization of Carbon Nanohorns Production via the Arc Discharge Method for Hydrotreating Application
Bibliographic record
Abstract
The submerged arc in liquid nitrogen method was used to produce carbon nanohorns (CNH) for hydrotreating application. In this paper the effects of current, time and system design modification were investigated to maximize CNH production. A current setting of 90 A was found to be the best condition for CNH production. Additionally, CNH production was impacted by processing time and design setup used in synthesizing the samples. For each batch, 0.12 g of CNH was obtained for 30 mins of processing time. The properties of CNH were evaluated using Brunauer–Emmett–Teller (BET) method, transmission electron microscopy (TEM), Fourier transform infrared (FTIR), Raman Spectroscopy and X-ray diffraction (XRD). BET results revealed mesoporous pore diameters for all pristine CNH samples under the different current (50–100 A) settings. Dahlia-like and budlike structures with aggregate diameters ranging from ~50–110 nm were observed in the TEM images. Although current and processing times were found to be two crucial parameters affecting the yield of CNH production, the entire equipment design was a major key factor in improving the yield by being capable of retaining more CNH particles during production.
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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".