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 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) | 0.000 | 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 | 0.001 |
| 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".