Polar Benzimidazole-Containing (Sulfonated) Poly(arylene ether ketone)s as Bifunctional Binders for Lithium–Sulfur Battery Cathodes with High Sulfur Loadings
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Bibliographic record
Abstract
Rational design of advanced bifunctional binder featured with both high binding strength and desired polysulfide adsorbing/trapping performance is mandatory for the fabrication of high loading sulfur electrodes for lithium–sulfur (Li–S) batteries. In the present work, we have studied the potential of a benzimidazole-containing poly(arylene ether ketone) (PAEK-BI) and a sulfonated poly(arylene ether ketone-co-benzimidazole) (SPAEK-BI) as bifunctional binders for high sulfur loading cathodes. Our investigation demonstrates that their use significantly improves the sulfur cathode performance with improved sulfur utilization (i.e., higher discharge capacity), reduced capacity decay, longer cycling life, and enhanced rate performance, relative to the conventional poly(vinylidene fluoride) (PVDF) binder. The superior performance results from the significantly higher binding strength and the valuable polysulfide trapping ability of the polymers through the sulfonate (−SO3–Li) and benzimidazole groups on the polymers. Theoretical density functional theory (DFT) simulation has verified the significantly higher binding energies between the two groups and Li2S4 than PVDF. Ex situ adsorption experiments have also confirmed the significant adsorption of Li2S4 on both polymers. At a sulfur loading of 3.5 mg cm–2, the capacity decay rates of cathodes with PAEK-BI and SPAEK-BI are reduced to as low as 0.025% and 0.039% per cycle over 400 cycles at 0.5 C, respectively. In addition, PAEK-BI and SPAEK-BI also enable the high sulfur loading cathodes (as high as 9.1 and 7.3 mg cm–2, respectively) with high areal capacities (2.9 mAh cm–2 and 2.6 mAh cm–2) and high cycling stabilities. With their superior electrochemical performance, both polymers show strong potential for the fabrication of high sulfur loading cathodes of sound cyclic stability for practical applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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.
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