A Quadruple‐Hydrogen‐Bonded Supramolecular Binder for High‐Performance Silicon Anodes in Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract With extremely high specific capacity, silicon has attracted enormous interest as a promising anode material for next‐generation lithium‐ion batteries. However, silicon suffers from a large volume variation during charge/discharge cycles, which leads to the pulverization of the silicon and subsequent separation from the conductive additives, eventually resulting in rapid capacity fading and poor cycle life. Here, it is shown that the utilization of a self‐healable supramolecular polymer, which is facilely synthesized by copolymerization of tert‐butyl acrylate and an ureido‐pyrimidinone monomer followed by hydrolysis, can greatly reduce the side effects caused by the volume variation of silicon particles. The obtained polymer is demonstrated to have an excellent self‐healing ability due to its quadruple‐hydrogen‐bonding dynamic interaction. An electrode using this self‐healing supramolecular polymer as binder exhibits an initial discharge capacity as high as 4194 mAh g −1 and a Coulombic efficiency of 86.4%, and maintains a high capacity of 2638 mAh g −1 after 110 cycles, revealing significant improvement of the electrochemical performance in comparison with that of Si anodes using conventional binders. The supramolecular binder can be further applicable for silicon/carbon anodes and therefore this supramolecular strategy may increase the choice of amendable binders to improve the cycle life and energy density of high‐capacity Li‐ion 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) | 0.000 | 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) | 0.002 | 0.001 |
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".