Formulation of hetero-aggregates in opposed jet fluidized beds
Bibliographic record
Abstract
Nanoparticle powders produced by mixing have applications in catalysis, coatings, and advanced materials, yet they remain challenging owing to the strong cohesive behavior of the constituents. Hetero-aggregates form through hetero-contacts at interfaces between chemically distinct materials, but achieving a uniform distribution of components requires an efficient mixing process. Opposed jet fluidized beds offer a promising approach for overcoming these limitations. In this study, the formulation of titania-zirconia hetero-aggregates was investigated by analyzing process parameters and their impact on the inter- and intra-aggregate mixing quality. The effects of feed composition mass ratios (1:1, 2:1, and 1:2), back pressures of the Laval nozzles (0.5, 2.5, and 5.0 bar), and processing times (1 and 5 min) were evaluated by mapping the Ti atomic fraction. The composition and shape of the hetero-aggregates were investigated. The hetero-aggregates exhibited compositions closely aligned with the expected values, based on the initial masses of the components. Shape analysis revealed star-like, elongated, and irregular structures, with circularity and roundness values of approximately 0.4 and 0.5, respectively. Most hetero-aggregates exhibited porosities between 0.971 and 0.991, indicating highly porous structures with significant void spaces. These findings demonstrate that opposed jet fluidized beds enable control over the composition and morphology of hetero-aggregates, leading to efficient nanoparticle mixing.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".