Investigating a siderophore-based approach for the recovery of critical metals from waste streams and leach solutions using microorganisms
Bibliographic record
Abstract
As technological advancements progress and unsustainable consumerism of electrical devices rapidly increases, the demand for critical metals continues to rise. The limited supply of these metals, driven by the depletion of non-renewable natural resources, calls for the recycling of waste materials and the development of sustainable and cost-effective extraction methods. Conventional methods of leaching such as pyrometallurgy pose threats to the environment by creating slags and releasing toxic secondary materials. Biohydrometallurgy emerges as an environmentally friendly method to extract metals from low-grade ores and waste material. Siderophores are secondary metabolites secreted by microorganisms that have the ability to selectively chelate certain metals. By immobilizing these siderophores for subsequent bioleaching, target metals can be extracted from multielement solutions. This research focuses on optimizing the immobilization efficiency of the siderophore desferrioxamine B (DFOB) in sodium alginate using physical entrapment for the removal of gallium. Four parameters were varied to find optimal conditions: sodium alginate concentration (1 – 4 % w/v), calcium chloride concentration (1 – 10% w/v), agitation time (0.25 – 10 hours), and DFOB concentration (1 – 4 mM). Using UV-Vis spectroscopy, free siderophore concentration could be determined. After 30 runs, the highest immobilization yield of 80.05% was determined at 2.5% sodium alginate, 10% calcium chloride—the highest concentration tested—after 5.13 hours with a concentration of 2.25 mM DFOB. Design Expert-13 software was used to analyze all experimental results. 2D graphs support that DFOB immobilization depends on responsive parameters, CaCl2 demonstrating the largest impact.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".