Bioleaching and Kinetic Investigation of WPCBs by A. Ferrooxidans, A. Thiooxidans and their Mixtures
Bibliographic record
Abstract
Bioleaching was used to mobilize Cu, Zn and Ni from waste printed circuit boards (WPCBs) and eliminate hazardous metal species from these wastes. Pulp density (PD) and medium culture are two effective factors which have been optimized in this paper. The bacteria Acidithiobacillus ferrooxidans (A. ferrooxidans) and Acidithiobacillus thiooxidans (A. thiooxidans) and their mixture were grown and adapted in the presence of WPCBs and then used as bioleaching bacteria to solubilize metals from PCBs. The experimental results demonstrated that 15 g/L WPCB is the best solid concentration which can be tolerated by the bacteria. Comparing different inoculation ratios, Cu (86%), Zn (100%) and Ni (100%) were recovered after 25 days of bioleaching, which suggests that the rate of metal recovery is significantly influenced by PD. Kinetics of bioleaching reactions was investigated in this work and the shrinking core model (SCM) was used to describe the kinetics of the process of no pretreated WPCBs. A constrained multi-linear regression analysis using the least square technique was employed to determine the rate controlling mechanism in each operating condition. Based on the results, diffusion through solid product layer was the major controlling mechanism.
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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) | 0.001 | 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".