The effect of particle size and water content on XRF measurements of phosphate slurry
Bibliographic record
Abstract
Abstract Phosphate slurries are studied using the XRF technique and the effect of the particle sizes and the water content parameters are analyzed and reported for the first time. Samples of the phosphate slurry with different particle sizes (425 µm, 300 µm, 250 µm, 200 µm, 160 µm and 106 µm) and different water contents (30%, 40%, 50%, 60%) were analyzed using an energy-dispersive X-ray spectrometer (EDXRF). The results show that the relative error of measurement varies with the particle size of the analyzed sample, the water content and the element measured. The relative error increases with the increase of the particle size for the compounds P 2 O 5 , Al 2 O 3 , K 2 O, Cr 2 O 3 , Fe 2 O 3 and Sr. The ratio between the relative errors related to the maximum and minimum grain sizes was 1.50 for P 2 O 5 , 4.01 for Al 2 O 3 , 15.58 for K 2 O, 1.22 for Cr 2 O 3 , 1.51 for Fe 2 O 3 and 1.11 for Sr. Alternatively, an opposite evolution has been observed in the case of compounds CaO and SiO 2 . The relative error increases with increasing water content for all compounds existing in the slurry. Depending on the measured compound, the relative error increases by a factor that varies between 1.39 and 2.39. In the case of P 2 O 5, the results do not show a clear correlation between the measurement error and the water content. A study will be conducted to investigate the effect of particle size and water content on XRF measurements in the case of phosphate slurry, aiming to develop an online XRF analyzer system for phosphate slurry.
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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 | 0.002 | 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 | 0.001 | 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 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".