Sustainable recovery of critical metals from spent lithium-ion batteries using chitosan as biosorbent in citrate-sulfate media: A comprehensive isotherm, kinetic, and thermodynamic analysis
Bibliographic record
Abstract
This study showcases the ability of chitosan powder to selectively adsorb Co(II), Mn(II), and Ni(II) from spent lithium-ion batteries within a sulfate-citrate medium while effectively maintaining Li in solution. Characterization via BET, FT-IR, and TGA analyses revealed that amine and hydroxyl groups are key players in adsorption. Considerably high adsorption rates for Co(II) and Ni(II), surpassing 80%, underscore chitosan's potential for robust metal ions retrieval. Notably, chitosan exhibits negligible adsorption capacity for Li, keeping it predominantly in solution, thus providing a selective separation and recovery method. Optimum parameters were determined as a dilution factor of 40, pH 4, and an adsorbent dosage of 10 g/L. While pH variations minimally affected Co(II) and Ni(II) recovery, Mn(II) removal rates increased with pH. Using the Langmuir isotherm model, adsorption capacities for Co(II) and Ni(II) were estimated at 1.86 mmol/g (or 110 mg/g) and 0.37 mmol/g (or 20 mg/g), respectively. Mn followed the Freundlich isotherm model, and its adsorption uptake was 0.02 mmol/g (1.10 mg/g) at an equilibrium concentration of 0.47 mmol/L. Kinetic studies highlighted rapid Co(II) and Ni(II) adsorption within 5 min, confirming surface reaction as the rate-limiting step, while thermodynamic analysis revealed favorable Co(II) and Ni(II) adsorption compared to unfavorable Mn(II) adsorption. Optimal desorption was achieved with 0.01 M sulfuric acid, achieving complete desorption for Co(II) and Ni(II) and over 82% for Mn(II). These findings underscore chitosan powder's potential as a sustainable and efficient green adsorbent for selective metal ions recovery in battery recycling, addressing crucial environmental and resource conservation concerns.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".