Enhancing Ionic Transport at Primary Interparticle Boundaries of Polycrystalline Lithium‐Rich Oxide in All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Polycrystalline lithium‐rich oxide (PLRO) is a promising high‐capacity cathode for next‐generation all‐solid‐state batteries (ASSBs). However, its full potential is hindered by sluggish Li + transport at primary interparticle boundaries, mainly due to the limited flowability of inorganic solid‐state electrolytes (SEs). Additionally, infiltrating conventional SEs into PLRO can lead to severe interfacial side reactions because of high melting points. Herein, we report a one‐step, low‐temperature (<200 °C) co‐sintering process that simultaneously synthesizes the SE and infiltrates it into the primary interparticle boundaries of PLRO, creating an integrated composite cathode for ASSBs. This process forms a continuous Li + transport network, enabling deep bulk activation of PLRO. Meanwhile, the co‐sintering process modulates the energy bands of the antibonding transition metal 3d‐O 2p and nonbonding O 2p at the surface, achieving greater orbital overlap to suppress oxygen release and mitigate interfacial phase transformation. As a result, the PLRO‐based ASSBs exhibit an impressive discharge capacity of 271 mAh g −1 at 0.1C, 212 mAh g −1 at 0.5C, and retain 80.0% capacity after 150 cycles. This study highlights the importance of enhancing ion transport to maximize the performance of PLRO‐based ASSBs, offering a practical solution for advancing energy storage technologies.
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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 | 0.000 |
| 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".