Hexabutylcyclohexane‐1,2,3,4,5,6‐hexaimine Additive‐Assisted Commercial Ester Electrolyte for 4.7 V Highly‐Stable Li‐Metal Batteries
Bibliographic record
Abstract
Abstract The development of high‐energy density batteries is of utmost importance for various applications. However, the utilization of numerous high‐capacity materials is impeded by the inadequate stability of electrolytes beyond 4.5 V. This research proposes a straightforward yet highly efficient strategy to overcome this limitation and enhance the stability of interfaces in high‐voltage Li metal batteries when employing commercial ester electrolytes. A series of electrolyte additives is designed with the aim of gradually extending the length of the terminal alkyl groups of cyclohexane‐1,2,3,4,5,6‐hexaimine (CHHI). Among these additives, hexabutylcyclohexane‐1,2,3,4,5,6‐hexaimine (HBCHHI) demonstrate exceptional performance due to the synergistic complementarity of n‐butyl and imino groups. This synergistic effect leads to the formation of a thinner, denser, and LixN‐rich solid/cathode‐electrolyte interface. Accordingly, the electrolyte‐electrode interactions are suppressed, enabling Li||LiNi0.8Co0.1Mn0.1O2 (NCM811) battery operation at a high‐voltage of 4.7 V with alleviated structural degradation of cathode and even Li deposition at the anode. The capacity retention of high‐voltage Li||NCM811 cells is improved by ≈250% after 500 cycles at a rate of 5C. The lifespan of full cells is also prolonged at quasi‐practical conditions of high cathode loading of ≈2.5 mAh cm−2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".