Cumulative leaching behavior in sustainable vanadium extraction from V-slag using Ba-Slag
Bibliographic record
Abstract
This study explores a sustainable “waste-to-value” approach that employs barium slag (Ba-slag), a byproduct of BaCO 3 production, as a roasting additive for vanadium extraction from V-slag. Key challenges addressed include isolating a pure V product from a multi-component leachate, explaining the leaching characteristics, and managing the tailings. In this work, a roasted mixture of Ba-slag and V-slag was leached under optimized conditions, yielding a leachate from which V 2 O 5 with 99.50 % purity was recovered via precipitation and calcination. Tailings were repurposed into foamed ceramics, achieving a maximum compressive strength of 4.05 MPa as a potential structural component. Optimal leaching parameters, 30 wt% H 2 SO 4 , 80 °C, and a holding time of 90 min, resulted in a peak V leaching efficiency of 90.67 %. The leaching efficiency increased initially but declined under excessive leaching conditions due to cumulative effects. Elemental migration, crystal phase evolution, and morphology and elemental distribution analyzes revealed that Ba 3 V 2 O 8 acts as the intermediate for V concentration, with newly formed small BaSO 4 particles on the tailings surface confirmed cumulative effects. Further, a novel method for calculating kinetic parameters was proposed to elucidate the cumulative characteristics. The cumulative rate constant ( k d ) ranged from 0.0009 to 0.006, while the activation energy ( E a) ranged from 22.4 to 32.53kJ/mol, aligning with reported values and validating the reliability of the proposed calculation method.
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".