Direct preparation of battery‐grade lithium carbonate via a nucleation–crystallization isolating process intensified by a micro‐liquid film reactor
Bibliographic record
Abstract
Abstract With the lithium‐ion battery industry booming, the demand for battery‐grade lithium carbonate is sharply increasing. However, it is difficult to simultaneously meet the requirements for the particle size and the purity of battery‐grade lithium carbonate. Herein, the nucleation–crystallization isolating process (NCIP) is applied to prepare battery‐grade lithium carbonate without any post‐treatment procedure. The nucleation process is intensified by a micro‐liquid film reactor (MLFR), where the feedstock solution is subject to intensive shear force and centrifugal force. The feedstock solutions are mixed rapidly and a large number of nuclei form instantly in the MLFR. After nucleation, the crystallization process is achieved in another reactor. A few new nuclei form in the crystallization process. The nucleation intensification in the MLFR is verified by computational fluid dynamics (CFD) simulations and experimental results. The particle size distribution is narrower and the impurity residue in the products is far lower than that prepared by a traditional precipitation method. The effects of nucleation and crystallization on the particle size distribution and purity were investigated. In the optimized operation parameters, the particle size distribution of the Li 2 CO 3 product is D 10 = 2.856 μm, D 50 = 5.976 μm, and D 90 = 11.197 μm, and the purity is 99.73%, both of which meet the requirements of battery‐grade Li 2 CO 3 . Moreover, the lithium recovery rate is increased to 88.21% compared to that prepared by a traditional precipitation method (79.0%). This work provides an alternative way for the preparation of high‐purity chemicals by process intensification.
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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 | 0.000 |
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".