Enhanced Potassium Storage Performance for K-Te Batteries <i>via</i> Electrode Design and Electrolyte Salt Chemistry
Bibliographic record
Abstract
Potassium batteries are an emerging energy storage technology due to the large abundance of potassium, low cost, and potentially high energy density. However, it remains challenging to find suitable electrode materials with high energy density and good cycling stability due to the structural instability and kinetics issues resulting from large size K + . Herein, a durable and high-capacity K-Te battery was developed by rational design of a Te/C electrode and electrolyte salt chemistry. A well-confined Te/C cathode structure was prepared by using a commercially available activated carbon as the Te host via a melt-diffusion method. Compared to bulky Te, the confined Te/C electrode exhibited greatly improved cycling stability, specific capacity, and rate capability in K-Te batteries. Moreover, it was found that the electrolyte salts (KPF 6 and KFSI) had significant impacts on the electrochemical performance of K-Te batteries. The Te/C electrode in the KPF 6 -based carbonate electrolyte exhibited higher specific capacity and better rate performance than the Te/C electrode in the KFSI-based one. Mechanism studies revealed that the KPF 6 salt resulted in an organic species-rich solid-electrolyte interphase (SEI) on the Te/C electrode, allowing for fast electron transfer and K-ion diffusion and enhanced K-ion storage performance in K-Te batteries. In contrast, KFSI salt led to the formation of KF-rich SEI layers, which had much higher resistances for electron and K-ion transport and was less effective for the well-confined Te/C electrode. Our work finds that the Te electrode and electrolyte chemistry need to be simultaneously optimized and tailored toward K-ion storage in K-Te batteries. It is expected that the finding reported herein might be inspirable for the future development of K-chalcogen (S/Se/Te) batteries.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".