Influence of atomization conditions on spray drying lithium iron phosphate nanoparticle suspensions
Bibliographic record
Abstract
Abstract Removing solvents from nanoparticle solid suspensions requires the same diligence as drying pharmaceutical ingredients. nanoparticles suspension in water oxidize, sinter, and segregates on the surface when they dry in a furnace. Spray drying preserves the material properties because contact times are on the orders of seconds; furthermore, the atomized droplets ensure particles are small (5 m to 20 m) and dispersed. A Yamato GA‐32 (120 mm inner diameter) spray dried in co‐current flow a nanoparticle suspension of in water, with a solid content up to 60 %. Atomization gas velocities of 140 m s to 350 m s agglomerated the nanomaterial into spherical particles that ranged from 3 m to 10 m. The particle diameters ranged from 10 m to 20 m at atomization velocities of 50 m s to 140 m s . At this condition, yield was lower because the semi‐dried particles adhere on the wall. At 150 C to 200 C the surface area reached 26 m g while from 50 C to 100 C it varied from 14 m g to 20 m g . The trend for mesoporosity versus spray drying temperature is the same as for surface area: pore volumes are higher (0.18 cm g ) above 200 C and 20 % lower below 200 C. Drying temperature modifies drying speed; low temperatures compact the powders more than high temperature which results in lower surface area and porosity.
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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) | 0.001 | 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) | 0.002 | 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".