Structural Study of Sulfur-Added Carbon Nanohorns
Bibliographic record
Abstract
In the past few decades, nanostructured carbons (NCs) have been investigated for their interesting properties, which are attractive for a wide range of applications in electronic devices, energy systems, sensors, and support materials. One approach to improving the properties of NCs is to dope them with various heteroatoms. This work describes the synthesis and study of sulfur-added carbon nanohorns (S-CNH). Synthesis of S-CNH was carried out by modified chemical vapor deposition (m-CVD) using toluene and thiophene as carbon and sulfur sources, respectively. Some parameters such as the temperature of synthesis and carrier gas flow rates were modified to determine their effect on the properties of S-CNH. High-resolution scanning and transmission electron microscopy analysis showed the presence of hollow horn-type carbon nanostructures with lengths between 1 to 3 µm and, diameters that are in the range of 50 to 200 nm. Two types of carbon layers were observed, with rough outer layers and smooth inner layers. The surface textural properties are attributed to the defects induced by the sulfur intercalated into the lattice or bonded with the carbon. The XRD patterns and X-ray microanalysis studies show that iron serves as the seed for carbon nanohorn growth and iron sulfide is formed during synthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".