Bauxite residue neutralisation precipitate stability in acidic environments
Bibliographic record
Abstract
Environmental context Although land remediation programs for bauxite residues aim at vegetation coverage, the stability of compounds in the residues with acids produced by the vegetation has not been investigated. We show that, despite the instability of caustic components in the residues (negative effects on plant development), this instability actually assists in neutralising acidic soils. These results further affirm the suitability and sustainability of current land remediation programs for bauxite residues in terms of minimising acidic soil formation. Abstract This investigation used a combination of techniques, such as X-ray diffraction, inductively coupled plasma optical emission spectroscopy and infrared spectroscopy, to determine the dissolution mechanisms of the Bayer precipitate and the associated rate of dissolution in acetic, citric and oxalic acid environments. The Bayer precipitate is a mixture of hydrotalcite, calcium carbonate and sodium chloride that forms during the seawater neutralisation of Bayer liquors (waste residue of the alumina industry). The dissolution rate of a Bayer precipitate is found to be dependent on (1) the strength of the organic acid and (2) the number of donating H+ ions. The dissolution mechanism for a Bayer precipitate consists of several steps involving: (1) the dissolution of CaCO3, (2) formation of whewellite (calcium oxalate) when oxalic acid is used and (3) multiple dissolution steps for hydrotalcite that are highly dependent on the pH of solution. The decomposition of the Al–OH hydrotalcite layers resulted in the immediate formation of Al(OH)3, which is stable until the pH decreases below 5.5. This investigation has found that the Bayer precipitate is stable across a wide pH range in the presence of common organic acids found in the rhizosphere, and that initial decomposition steps are likely to be beneficial in supporting plant growth through the release of nutrients such as Ca2+ and Mg2+.
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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".