Mitigating the Interfacial Degradation in Cathodes for High-Performance Oxide-Based Solid-State Lithium Batteries
Bibliographic record
Abstract
Solid-state lithium batteries (SSLBs) are the promising next-generation energy storage systems because of their attractive advantages in terms of energy density and safety. However, the interfacial engineering and battery building are of huge challenges, especially for stiff oxide-based electrolytes. Herein, we construct SSLBs by a cosintering method using Li 3 BO 3 as a sintering agent to bind the cathode materials LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC) and solid-state electrolytes Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 . Small NMC primary particles are compared with large secondary particles to study the effects on interfacial adhesion, mechanical retention, internal resistance evolution, and electrochemical performance. Our results reveal that the interfacial resistance decreases during charging and increases during discharging, resulting in an overall increase in the interfacial resistance after one cycle. The main reason is attributed to the microcracks induced by the volumetric changes of NMC during the electrochemical process. The mechanical degradations at the interfaces accumulated upon cycling can cause capacity decay and low Coulombic efficiency. The SSLB constructed from small NMC primary particles shows regulation of particle distribution, mitigation in local volumetric change, and alleviation in mechanical degradation at the interfaces, leading to smaller resistance change and better electrochemical performance. The findings shed lights on designing SSLBs with good mechanical retention and electrochemical performance.
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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 | 0.001 |
| 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".