Large‐scale oxidation of multi‐walled carbon nanotubes in fluidized bed from ozone‐containing gas mixtures
Bibliographic record
Abstract
Abstract 100 g of multi‐walled carbon nanotubes (MWCNTs) tangled in balls of 388 μm in Sauter diameter were treated per run in a pre‐industrial scale fluidized bed reactor, using ozone‐based gaseous mixtures at ambient temperature. The influence of ozone concentration and of the addition of water vapour was studied, for treatment durations between 1 h and 20 h. The process behaviour was analyzed in terms of fluidized bed pressure drop and temperature profile. The nature and amount of the grafted oxygen based functions were analyzed, as the structural modifications created. An oxidation mechanism in two steps was evidenced, showing the grafting of hydroxyl, phenol, and ether functions in a first step and then of lactone, quinone, carbonyl, and carboxylic groups. A moderate etching of the MWCNT outer walls was observed. The amount of grafted functions and of structural defects increased with treatment duration and was highly exalted by the presence of water vapour. All the results obtained showed that the oxidation was uniform on the whole powder of the bed and from the outer part to the centre of the balls, probably thanks to the high fluidization quality maintained all along the ozone treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".