An improved model for determining fractal structure of nano‐agglomerates
Bibliographic record
Abstract
Abstract Effects of operating conditions on fractal structures of three nanoparticle agglomerates (SiO2, TiO2, and ZnO) in a vibro‐fluidized bed were investigated. An improved model is proposed by combining the fractal relation, Richardson‐Zaki equation, and mass balance. This model was used to predict substantial properties of nanoparticle agglomerates, such as fractal dimension and size of the agglomerate particulate fluidization. It was shown that with increasing vibration intensity, the fractal dimension of agglomerates decreases slightly, while the number of primary particles in the agglomerate decreases significantly. It was found that the fractal dimension of nanoparticle agglomerates is in the range of 2.63–2.78, and the number of primary particles in the agglomerate is in the order of 1010. Agglomerate size reaches a constant value at high vibration frequency. Calculation results indicated that the vibration frequency has a more important role than its amplitude in reducing agglomerate size. Minimum fluidization velocity was calculated by applying estimated agglomerate sizes, and it was found that the results were in close agreement with the experimental data.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".