Chemical and Morphologic Characterization of Sylvite (KCl) Mineral from Different Deposits Used in the Production of Fertilizers
Bibliographic record
Abstract
In the present work, four (4) samples of sylvite (KCl) from different deposits in Canada, Belarus and Germany were analyzed in order to determine the differences between them in terms of chemical composition, morphology and impurities present. Different non-instrumental and instrumental techniques were used. A scanning electron microscope analysis showed that the particles are completely amorphous. Bueno mineral has particles with the largest area (with 91.4% particles larger than 0.50 mm), and the BPC mineral samples have those with the smallest area (with 49.9% particles smaller than 0.50 mm). The Bueno mineral has the highest angle of repose. Bueno mineral has the highest surface area and pore volume when compared to the other KCl minerals. On the other hand, the BPC KCl mineral presented a lower surface area and pore volume. The problem mineral has the highest percentage composition of K. Blanco mineral did not present Al, and the element Mo was not detected in the Blanco and Bueno mineral. The IR bands of O-H, C-N, N-H and C=O bonds were observed. These results are useful for the fertilizer industry. These differences can produce adverse effects in a subsequent application in fertilizer production processes. Such differences can be attributed to the origin of this mineral, the way it is obtained and exploited, or to the transport and storage process through the use of anticompacting additives due to the high hygroscopicity of the mineral.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".