Magnetic Seed in Ambient Temperature Ferrite Process Applied to Acid Mine Drainage Treatment
Bibliographic record
Abstract
Due to its environmental consequences, acid mine drainage (AMD) has been recognized as a major challenge to the global mining industry. Through the innovative use of magnetic seeds, a versatile ambient temperature ferrite (ATF) process has been developed to treat AMDs containing such nonferrous heavy metal ions as Cu 2+, Zn 2+, Ni 2+, Mn 2+, and Al 3+ . These metal ions proved detrimental to ferrite formation using the existing ATF process, particulary when lime was used as neutralizer. The use of magnetic seeds in the ATF process minimized the interference. The role of the chemical environment in ferrite formation from an AMD with the addition of magnetic seeds was investigated. Compared with the conventional seed processes, controlling the solution chemistry resulted in a reduced amount of seed needed to recover an equal amount of crystalline magnetic precipitates. With a relatively short processing period (less than 2.5 h), up to 100% of the precipitates were magnetically recovered from a simulated AMD. The residual concentrations of major contaminant ions in the treated water were below the corresponding acceptable levels. The XRD pattern showed that the solid products were all of spinel ferrite crystal structure, in contrast to the presence of a substantial amount of noncrystalline phase in the product formed using a conventional seed process.
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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".