Regulating Electronic Conductivity at Cathode Interface for Low‐Temperature Halide‐Based All‐Solid‐State Batteries
Bibliographic record
Abstract
Abstract Halide solid‐state batteries (SSBs) show unparalleled application potential because of their outstanding advantages, such as high ionic conductivity and good compatibility with cathodes. However, operating halide SSBs under freezing temperatures faces big challenges, and the underlying degradation mechanisms are unclear. Herein, the impact of electronic conductivity in low‐temperature halide SSBs is investigated by designing different additives in the composite cathode. It is shown that the electrochemical stability of a halide electrolyte (Li 3 InCl 6 ) with additives is significantly affected by the degree of electronic conductivity as well as the ambient operational temperature. When the ambient temperatures are below freezing point, the moderate electronic conductivity in the composite cathode is beneficial toward improving the charge transfer kinetics without inducing the decomposition of Li 3 InCl 6 . The electrode materials (LiCoO 2 cathode and Li 3 InCl 6 electrolytes) show excellent structural and interfacial stability during electrochemical reactions, resulting in a competitive performance at low temperatures. Stable long‐term cycling performance with a capacity retention of 89.2% after 300 cycles is achieved along with a C‐rate capacity of 77.6 mAh g –1 (0.6 C) at −10 °C. This in‐depth study investigates the role of electronic conductivity, which opens the door to future research on low‐temperature SSBs.
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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 | 0.000 |
| 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".