Improving flow and fluidization quality of fine and ultrafine particles via nanoparticle modulation
Bibliographic record
Abstract
Fine and ultrafine particles possess great potential for industrial applications ascribed from their huge specific surface area and ability to provide good gas–solid contact. However, these powders are inherently cohesive, making it challenging to achieve smooth flow and fluidization. This challenge can be well-resolved by nanoparticle modulation (nano-modulation), where a small amount of nanoparticles is uniformly mixed with the cohesive fine/ultrafine powders. Through nano-modulation, the fluidization system of cohesive powders exhibits distinguishable minimum fluidization velocity, enlarged bed expansion ratio (particularly the dense phase expansion), and scarcer, smaller, and slower moving bubbles, indicating improved flow and fluidization quality. The purpose of the current work is to systematically summarize the state-of-the-art progress in the fluidization and utilization of fine and ultrafine particles via the nanoparticle modulation method. Accordingly, a broader audience can be enlightened regarding this promising fine/ultrafine particle fluidization technology, so as to provoke their attention and encourage interdisciplinary integration and industry-academia collaborative research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".