Selective Complexation and Leaching of Cobalt Using Histidine in an Alkaline Medium
Bibliographic record
Abstract
Considering the issues of significant ammonia volatilization loss and toxic gas emission associated with the conventional ammonia leaching method used in the resource utilization of cobalt-rich alloy slag, a novel approach involving selective complexation leaching of cobalt in an alkaline histidine solution has been proposed. Under conditions of 35 °C temperature, a molar ratio of histidine to cobalt of 1.5, pH of 8, a leaching period of 12 h, and a stirring speed of 300 rpm, the cobalt leaching rate from cobalt-rich alloy slag exceeds 95%. In contrast, the leaching rates for impurity metals such as iron, lead, and copper remain below 3%, demonstrating outstanding leaching selectivity. Leaching kinetics calculations indicate that the rate-controlling step is chemical reaction control, with an apparent activation energy of 64.32 kJ/mol. Through the use of FTIR and XPS characterization techniques, it has been confirmed that histidine molecules form a stable complex with cobalt ions via the dual coordination of the carboxyl (COO−) and amino (-NH2) groups. This distinctive bifunctional synergistic coordination mechanism markedly enhances leaching selectivity and reaction efficiency.
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 | 0.000 | 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 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".