Inter-aggregate mixing in hetero-aggregates formulated in opposed jets fluidized bed
Bibliographic record
Abstract
The mixing quality in nanoparticulate systems plays a fundamental role in the functional enhancement of advanced materials. In this study, we evaluate the effectiveness of mixing TiO 2 (rutile) and ZrO 2 (monoclinic) nanopowders using Raman mapping and energy-dispersive X-ray (EDX) spectroscopy within a scanning electron microscope (SEM). Eighteen experiments were carried out in an opposed jet fluidized bed, varying process time, mass ratio, and Laval nozzle back pressure. Raman mapping enabled spatially resolved identification of phases, while SEM/EDX provided high-resolution elemental composition. Both techniques indicated good overall inter-aggregate mixing efficiency, especially at a mass ratio of 1:1, with average Ti atomic fractions close to 0.607. Quantitative comparison showed that Raman micro spectroscopy yielded lower relative deviations from the expected values and required simpler sample preparation, making it a practical choice for assessing mixing homogeneity. Deviations from the expected compositions were more pronounced at other mass ratios (especially 1:2), likely owing to differences in particle size, density, and aggregation tendencies. Finally, in contrast to previous intra-aggregate mixing studies, the current results suggest that inter-aggregate composition tends to stabilize near equimolar proportions regardless of the initial mass ratio, highlighting self-regulating behavior at the macro scale. • Inter-aggregate mixing was successfully evaluated using Raman microspectroscopy and EDX. • Compositional self-stabilization behavior was observed under all operating conditions. • The processing time had a limited influence, suggesting rapid mixing stabilization. • The final system composition is strongly dependent on the mass ratio and pressure.
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 | 0.000 |
| 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) | 0.000 | 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 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".