A New Approach to Magnesium Removal in Cobalt Precipitation
Bibliographic record
Abstract
Sodium dodecyl sulfate (SDS) was introduced to reduce the magnesium content in cobalt hydroxide precipitates (MHPs) derived from copper-cobalt ore leaching solutions. The impact of the key parameters – SDS dosage, MgO/Co mass ratio, reaction time, temperature, stirring speed, and MgO concentration – was systematically evaluated to identify the optimal conditions for producing MHP with minimal magnesium impurity. The effect of SDS on MHP crystal morphology, surface area, pore size, reaction kinetics, and electrostatic potential was also assessed. Under optimal conditions, with SDS, a magnesium grade of 0.96% and a cobalt grade of 53.69% were achieved, alongside a cobalt recovery of 98.69%. Moreover, SDS was found to enhance the chemical control reaction, reducing the activation energy from 74.96 kJ/mol to 67.86 kJ/mol. This study offers valuable insights into the magnesium removal mechanism facilitated by SDS, addressing the challenge of high magnesium impurities in MHP products and providing guidance for impurity control in other precipitation processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".