Preparation of Ca(<scp>OH</scp>)<sub>2</sub> nanoparticles by impinging stream reaction precipitation method
Bibliographic record
Abstract
Abstract In this paper, Ca(OH) 2 nanoparticles are prepared by impinging stream co‐precipitation method. The process of preparing nanoparticles by impinging stream is optimized. The influence of process variable, such as dispersant type, dispersant dosage, circulating flow rate, reactant concentration ratio of OH − and Ca 2+ , reaction temperature and circulation time, on the particle size of Ca(OH) 2 nanoparticle are investigated. The results show that the concentration ratio of reactants has a significant effect on the size of Ca(OH) 2 nanoparticles. The optimal process conditions are obtained by single factor experiment, PEG6000, 3.3% dispersant dosage, Q = 1000 L · h −1 , C = 1.88, T = 46.68°C and t = 60 min. The average particle size of Ca(OH) 2 nanoparticles prepared under these conditions is 107.67 nm. According to the microstructure analysis, the prepared Ca(OH) 2 nanoparticles samples have high purity and a good crystal structure. The powder dispersed with a marked hexagonal crystal shape and good thermal decomposition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".