A hydrometallurgical process for the separation of lead and bismuth from lead bismuthite
Bibliographic record
Abstract
Abstract A hydrometallurgical process to efficiently separate and recover bismuth and lead from lead bismuthite was proposed. It included the dissolution of lead bismuthite in a solution containing 4.2 mol/L HNO 3 and 5.0 wt.% of carbamide at a temperature of 70–90°C followed by bismuth precipitation by adding lead carbonate to precipitate basic bismuth nitrate [Bi 6 O 4 (OH) 4 ](NO 3 ) 6 ∙ H 2 O of a technical grade. The lead‐containing solution was further evaporated to produce lead nitrate Pb(NO 3 ) 2 of a chemically pure grade, while the basic bismuth nitrate was further purified from metal impurities. Basic bismuth nitrate was dissolved in a ~6.0 mol/L HNO 3 solution at a temperature of 60–70°C, diluted with distilled water two times, and mixed with an aqueous ammonium carbonate solution to adjust the pH to 0.9 at a temperature of 55°C and to precipitate bismuth. After washing and drying of the precipitate at a temperature of 90–100°C, the high purity basic bismuth nitrate [Bi 6 O 5 (OH) 3 ](NO 3 ) 5 ∙ 3H 2 O was obtained. The large‐scale laboratory trials confirmed the relevance of the proposed technology for hydrometallurgical processing of lead‐ and bismuth‐containing raw materials. It is an easy and economical way to produce high purity bismuth and lead compounds.
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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".