A novel closed-loop biotechnology for recovery of cobalt from a lithium-ion battery active cathode material
Bibliographic record
Abstract
In recent years, the demand for lithium-ion batteries (LIBs) has been increasing rapidly. Conventional recycling strategies (based on pyro- and hydrometallurgy) are damaging for the environment and more sustainable methods need to be developed. Bioleaching is a promising environmentally friendly approach that uses microorganisms to solubilize metals. However, a bioleaching-based technology has not yet been applied to recover valuable metals from waste LIBs on an industrial scale. A series of experiments was performed to improve metal recovery rates from an active cathode material (LiCoO2; LCO). (i) Direct bioleaching of ≤0.5 % LCO with two prokaryotic acidophilic consortia achieved >80 % Co and 90 % Li extraction. Significantly lower metal recovery rates were obtained at 30 °C than at 45 °C. (ii) In contrast, during direct bioleaching of 3 % LCO with consortia adapted to elevated LCO levels, the 30 °C consortium performed significantly better than the 45 °C consortium, solubilizing 73 and 93 % of the Co and Li, respectively, during one-step bioleaching, and 83 and 99 % of the Co and Li, respectively, during a two-step process. (iii) The adapted 30°C consortium was used for indirect leaching in a low-waste closed-loop system (with 10 % LCO). The process involved generation of sulfuric acid in an acid-generating bioreactor (AGB), 2–3 week leaching of LCO with the biogenic acid (pH 0.9), selective precipitation of Co as hydroxide, and recirculation of the metal-free liquor back into the AGB. In total, 58.2 % Co and 100 % Li were solubilized in seven phases, and >99.9 % of the dissolved Co was recovered after each phase as a high-purity Co hydroxide. Additionally, Co nanoparticles were generated from the obtained Co-rich leachates, using Desulfovibrio alaskensis, and Co electrowinning was optimized as an alternative recovery technique, yielding high recovery rates (91.1 and 73.6% on carbon felt and roughened steel, respectively) from bioleachates that contained significantly lower Co concentrations than industrial hydrometallurgical liquors. The closed-loop system was highly dominated by the mixotrophic archaeon Ferroplasma and sulfur-oxidizing bacteria Acidithiobacillus caldus and Acidithiobacillus thiooxidans. The developed system achieved high metal recovery rates and provided high-purity solid products suitable for a battery supply chain, while minimizing waste production and the inhibitory effects of elevated concentrations of dissolved metals on the leaching prokaryotes. The system is suitable for scale-up applications and has the potential to be adapted to different battery chemistries.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".