Nearly all-active-material cathodes free of nickel and cobalt for Li-ion batteries
Bibliographic record
Abstract
The global transition to electric vehicles and large-scale energy storage systems requires cost-effective and abundant alternatives to commercial Co/Ni-based cathodes (e.g., LiNi0.6Mn0.2Co0.2O2) for Li-ion batteries (LIBs). Manganese-based disordered rock-salts (Mn-DRXs) can outperform conventional cathodes at lower cost, achieving >900 Wh/kg-AM (per active material, AM), but such performance has been demonstrated exclusively in cell constructions far removed from commercial viability, namely with diluted electrode films (~70 wt%-AM) containing excessive carbon and binder. Herein, our work involves a comprehensive study to attain AM-concentrated Mn-DRX cathodes (>95 wt%-AM), covering from inherent material properties to the microstructure of electrodes, to address the formidable challenges in Mn-DRX research. We reveal that Mn-DRXs’ failures in AM-concentrated electrodes originate from their extremely low electrical conductivity (10^-10-10^-8 S/cm) and the collapse of the electrical network with volume change over cycling. These failure modes are resolved through electrical percolation engineering and enhancement of electrode mechanical properties, allowing our demonstration of nearly all-AM Mn-DRX cathodes (~96 wt%-AM) and the highest application-level energy density (~1050 Wh/kg-cathode) reported to date. This work further unveils the trade-off role of Mn-content on Mn-DRXs’ electrical conductivity and volume change, providing guidelines for material design to 33 advance Co/Ni-free LIB's technology readiness.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".