Nanostructured Carbon Xerogels by Super-Fast Carbonization
Bibliographic record
Abstract
Traditional synthesis methods of highly porous carbon xerogels impose many limitations on production in large scale such as low heat and mass transfer and long processing time. In this study, for the first time, recorsinol–formaldehyde (RF) xerogels were carbonized by fast heating rates (σ) in a fluidized bed reactor. The specific surface area (Φ) and pore volume of carbon xerogels were examined in terms of particle size (≈ 100 and 297 μm), carbonization temperature (298–1273 K), and σ (5–50 K min –1 ). The temperature above which Φ decreases by increasing temperature was shifted to lower values for larger particles. Moreover, Φ and volume of micro- and mesopores increased by increasing σ. Possible mechanisms to interpret the effects of carbonization temperature and σ on physical properties of carbon xerogels were finally furnished. Carbonization time was found to be ≈25-times faster by fluidization while maintaining the quality of xerogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".