Preparation of Porous Anhydrous MgCl2 Particles by Spray Drying Process
Bibliographic record
Abstract
Polyethylene (PE) is indispensable materials in daily lives.To produce PE, the catalyst was needed in the reaction.Ziegler-Natta catalysts were mostly used which consisted of TiCl4 on the MgCl2 supports.Polyethylene particle was reported to replicate the shape of the catalyst particles or catalyst support particles.Therefore, the MgCl2 supports need to satisfy various requirements regarding particle morphology such as shape, particle size with uniform size distribution as well as the porosity.In this research, the preparation of MgCl2 particles from irregular shape of anhydrous MgCl2 by spray drying method was studied.The moisture was reported as the poison of the catalyst, so the unusual close loop spray drying under N2 conditions was used in this study.The different types of alcohol, ethanol, n-propanol and n-butanol as solvent which was used to dissolve MgCl2 before feeding through the spray drying on the particle properties were investigated.The amount of residual alcohol (alcoholic hydroxyl group content), morphology, specific surface area, porosity and crystallinity were determined by GC method, scanning electron microscope (SEM), N2 sorption analyzer and X-ray diffraction (XRD), respectively.The results revealed that spray drying process can produce the porous anhydrous MgCl 2 particles which have rough surface, higher porosity and lower crystallinity than original anhydrous MgCl2.
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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".