Deciphering Interfacial Chemical and Electrochemical Reactions of Sulfide‐Based All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Large interfacial resistance resulting from interfacial reactions is widely acknowledged as one of the main challenges in sulfide electrolytes (SEs)‐based all‐solid‐state lithium batteries (ASSLBs). However, the root cause of the large interfacial resistance between the SEs and typical layered oxide cathodes is not fully understood yet. Here, it is shown that interfacial oxygen loss from single‐crystal LiNi0.5Mn0.3Co0.2O2 (SC‐NMC532) chemically oxidizes Li10GeP2S12, generating oxygen‐containing interfacial species. Meanwhile, the interfacial oxygen loss also induces a structural change of oxide cathodes (layered‐to‐rock salt). In addition, the high operation voltage can electrochemically oxidize SEs to form non‐oxygen species (e.g., polysulfides). These chemically and electrochemically oxidized species, together with the interfacial structural change, are responsible for the large interfacial resistance at the cathode interface. More importantly, the widely adopted interfacial coating strategy is effective in suppressing chemically oxidized oxygen‐containing species and mitigating the coincident interfacial structural change but is unable to prevent electrochemically induced non‐oxygen species. These findings provide a deeper insight into the large interfacial resistance between the typical SE and layered oxide cathodes, which may be of assistance for the rational interface design of SE‐based ASSLBs in the future.
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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) | 0.001 | 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".