Constructing multifunctional solid electrolyte interface via in-situ polymerization for dendrite-free and low N/P ratio lithium metal batteries
Bibliographic record
Abstract
Abstract Stable solid electrolyte interface (SEI) is highly sought after for lithium metal batteries (LMB) owing to its efficient electrolyte consumption suppression and Li dendrite growth inhibition. However, current design strategies can hardly endow a multifunctional SEI formation due to the non-uniform, low flexible film formation and limited capability to alter Li nucleation/growth orientation, which results in unconstrained dendrite growth and short cycling stability. Herein, we present a novel strategy to employ electrolyte additives containing catechol and acrylic groups to construct a stable multifunctional SEI by in-situ anionic polymerization. This self-smoothing and robust SEI offers multiple sites for Li adsorption and steric repulsion to constrain nucleation/growth process, leading to homogenized Li nanosphere formation. This isotropic nanosphere offers non-preferred Li growth orientation, rendering uniform Li deposition to achieve a dendrite-free anode. Attributed to these superiorities, a remarkable cycling performance can be obtained, i.e., high current density up to 10 mA cm −2 , ultra-long cycle life over 8500 hrs operation, high cumulative capacity over 4.25 Ah cm −2 and stable cycling under 60 °C. A prolonged lifespan can also be achieved in Li-S and Li-LiFePO 4 cells under lean electrolyte content, low N/P ratio or high temperature conditions. This facile strategy also promotes the practical application of LMB and enlightens the SEI design in related fields.
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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".