Solid hold‐up measurement in a jet‐impactor assisted fluidized bed using gamma‐ray densitometry
Bibliographic record
Abstract
Abstract A jet‐impactor assisted‐fluidized bed (JIAFB) for continuous de‐agglomeration of nanopowder agglomerates was presented in previous work. Therein, a jet caused agglomerates to impinge up an impactor, where they would break. However, efficient impactor positioning will be dictated by particle momentum: the product of solid concentration and velocity must be highest. Herein, the variation of solid hold‐up was measured in a fluidized bed of Fe2O3 nanoparticles using gamma‐ray densitometry. Behaviour was compared under minimum fluidization and bubbling regimes, over a wide range of jet velocities (0–200 m s−1). A new line‐decomposition approach allowed mapping local solid distribution across seven axial and five radial positions, tangibly demonstrating how increasing the gas velocity enhanced the fluidization quality by increasing axial solid diffusivity. Conversely, increasing jet velocity locally decreased solid hold‐up in the jet‐affected zone, and brought about inhomogeneities. With this information in hand, jet‐to‐impactor distance was optimized and validated experimentally.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".