Prelithiation, a key strategy for next‐generation lithium‐ion batteries
Bibliographic record
Abstract
Abstract Lithium‐ion batteries (LIBs) are highly efficient devices for secondary energy and conversion. Prelithiation is emerging as a promising strategy for developing next‐generation high‐performance LIBs, with rapid advancements achieved through various innovative methods. This review summarizes prelithiation strategies from a new original perspective, which is based on the utilization of lithium metal, lithium‐containing compounds, and introduced prelithiation methods without any additional lithium sources firstly. Furthermore, the industrialization progress of various prelithiation methods is presented. Based on key industrialization criteria, the merits and limitations of various prelithiation strategies have been comprehensively assessed, along with a discussion of their future challenges and perspectives facing its industrialization. Key internal mechanisms of prelithiation are described, including electron pathway density in contact prelithiation and the intrinsic influence of electrode damage in mechanical prelithiation. Additionally, evaluation methods and theoretical models for prelithiation are presented. The comprehensive effects of prelithiation on electrochemical performances are analyzed, offering valuable insights into its benefits and limitations. Finally, the extended applications of prelithiation, including its potential in battery recycling processes, solutions to critical challenges in lithium‐sulfur batteries (LSBs) and lithium‐oxygen (Li‐O 2 ) batteries, and its inspired adaptations for sodium‐ion (SIBs) and potassium‐ion batteries (PIBs), are systematically highlighted in this review.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".