Black mass impurities effect on re-synthesized NMC811 by carbonate coprecipitation
Bibliographic record
Abstract
The rapid expansion of electromobility and renewable energy storage has increased lithium-ion battery production, emphasizing the need for efficient end-of-life management and critical material recovery. Mechanical pretreatment of spent batteries yields a black mass containing valuable oxides and metallic impurities, primarily Al, Cu, and Fe. This study investigated the impact of Al 3+ , Cu 2+ and Fe 2+ impurities on the carbonate coprecipitation synthesis of LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) precursors to simplify cathode resynthesis from acid leachate. Electrochemical testing showed improved performance for NMC811 materials doped with 1–3 at% Al 3+ , 2 at% Fe 2+ , or co-doped with Al–Fe at total concentrations of 2–4 at%, compared to undoped NMC811. Rietveld refinement of XRD patterns revealed reduced Li + /Ni 2+ cation mixing in these same concentrations, confirming structural stabilization. In contrast, Cu 2+ doping beyond 1 at%, whether alone or in combination, did not yield additional benefits and instead led to increased disorder. These findings suggest that leachates containing up to these impurity levels could be used directly in resynthesis without further purification, as the resulting NMC811 retained equal or improved performance. This supports a more sustainable and resource-efficient recycling process by reducing water, reagent, and energy consumption. • Impurity-tolerant recycling saves reagents and energy for greener NMC811 resynthesis. • Controlled Al 3+ and Fe 2+ content can enhance structural & electrochemical stability. • Cu impurities above 1 at% impair capacity and structural ordering in NMC811. • Al–Fe co-doping improves NMC811 rate capability and cycling stability.
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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) | 0.000 | 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 | 0.001 |
| 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".