Adsorption mechanism of the simulated red mud from diaspore with high levels of silicon and iron
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Bibliographic record
Abstract
Abstract The flocculation effect of Hx‐600 on the simulated red mud of the Bayer process from diaspore with high levels of silicon and iron was investigated, and a flocculant with high content of hydroxamate groups (HCPAM) and sodium polyacrylate (PAAS) was chosen as the model compound of Hx‐600. The typical silicon‐containing and iron‐containing monominerals in red mud, hydration grossular and hematite, were correspondingly synthesized, and the adsorption mechanism with HCPAM or PAAS was investigated using FTIR and XPS, respectively. The results reveal physical adsorption of HCPAM or PAAS on the surfaces of hydration grossular and chemisorption of HCPAM or PAAS on the surfaces of hematite. The atomic Mulliken populations of the model compounds of HCPAM and PAAS were calculated by quantum chemical calculation. The results show that a five‐membered ring may be formed on the surfaces of HCPAM‐treated hematite and a four‐membered ring may be formed on the surfaces of PAAS‐treated hematite. A stronger adsorption of the flocculant with hydroxamate groups on the hematite surfaces can be estimated, compared to carboxyl groups. Therefore, the improvement of settling performance achieved by Hx‐600 could be attributed to the high content of hydroxamate groups and high molecular weight (mass) of Hx‐600. A chemisorption with a bridging flocculation between the iron‐containing component in red mud and Hx‐600, and a physical adsorption with a bridging flocculation between the silicon‐containing component in red mud and Hx‐600 can enhance the settling performance of the simulated red mud of the Bayer process from diaspore with high levels of silicon and iron.
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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 | 0.000 | 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) | 0.000 | 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 it