High‐yield synthesis of multi‐walled carbon nanotube by hydrothermal method
Bibliographic record
Abstract
Abstract The evolution of multi‐walled carbon nanotubes (MWCNTs) under hydrothermal condition was investigated, because the hydrothermal method (HTM) has been utilised for commercial production of advanced engineering materials. To synthesise MWCNTs by hydrothermal process using mixed aqueous solution diethylenetriamine, polyethyleneglycol (PEG) and NaOH were used as starting materials. We investigated the effect of hydrothermal temperature, time, and amount of formative compounds. Hydrothermal reaction temperature was in the range from 150 to 180°C. The aim of the present work is to presentation hydrothermal synthesis as a new processing method for fabrication of MWCNTs, without the addition of metal catalyst. The homogeneity of hydrothermal processes, cheapness, and availability of amorphous carbon materials, without the need of catalyst, are advantages favouring the scaling‐up of the new method. The treatment of higher volumes would only require autoclaves with increasing load‐bearing capacity. Synthesised MWCNTs were analysed with a scanning electron microscope (SEM), a high resolution transmission electron microscope (HRTEM), thermo‐gravimetric analysis (TGA), and Raman spectroscopy. This result also presented a controllable way to synthesise MWCNTs with high purity. Indeed, present work will introduce new chapter in synthesising MWCNTs for scientific and engineering.
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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 | 0.001 |
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".