Water-Soluble Linear Poly(ethylenimine) as a Superior Bifunctional Binder for Lithium–Sulfur Batteries of Improved Cell Performance
Bibliographic record
Abstract
To realize the practical application of lithium–sulfur (Li–S) batteries, bifunctional binders featured with the capability of trapping soluble polysulfide species besides the strong binding property are highly desired. Herein, we demonstrate the strong potential of a commercially available, environmentally friendly, water-soluble linear polyethylenimine (PEI) as a superior bifunctional binder for high-sulfur-loading cathodes in Li–S batteries. Our investigation shows the significantly improved cathode performance with enhanced sulfur utilization (i.e., higher capacity), reduced capacity decay, and longer cycling life upon the use of PEI as the binder, relative to the traditional polyvinylidene fluoride (PVDF) binder. This arises from the significantly stronger binding strength and valuable polysulfide trapping ability of the linear PEI binder. In particular, its superior polysulfide adsorption capability has been evidenced experimentally with both ex situ and in situ studies, as well as through theoretical density functional theory (DFT) calculations. At a sulfur loading of 2.4 mg cm –2, the capacity decay rate of the cathode with the linear PEI binder is reduced to as low as 0.042% per cycle over 500 cycles at 2 C. In addition, it also enables the fabrication of high-sulfur-loading cathodes (as high as 6.5 mg cm –2 ) with high areal capacity ( ca . 4.5 mAh cm –2 ) and high cycling stability. With its superior performance, linear PEI is promising for fabricating high-sulfur-loading cathodes of significantly lowered capacity decay for practical applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".