Synthesis of Aminopolyolefins with Tunable Hydrophilicity and Chain Flexibility for Application as Binders in Aqueous Rechargeable Batteries
Bibliographic record
Abstract
Rechargeable Zn-ion batteries (ZIBs) are an attractive alternative to Li-ion batteries (LIBs) for delivering sustainable energy storage systems for stationary applications due to their high energy density, use of earth-abundant materials, lower cost, and improved safety. Although ZIBs have theoretically high energy densities, maintaining the long-term performance of high-mass-load ZIBs remains a challenge. While polymeric binders are known to maintain mechanical integrity during cycling and prolong battery life, diverse binder materials have not been widely explored for the development of new ZIB technologies. As binders, recently disclosed aminopolyolefins (APOs) have the potential to offer good charge and mass transport properties to improve the electrochemical performance of the cell. Here, different types of APO-based binders with variable mechanical properties and hydrophilicities have been synthesized. The chain flexibility and hydrophilicity of the APO binders were found to have a profound influence on the ZIB cycling performance. APOs with self-healing and hydrophilic properties were preferred for assembling aqueous ZIBs that are not limited to low active material loading but show that a high active material loading is possible. The tunability of APOs was exploited to assemble near-neutral aqueous Zn/MnO 2 batteries, with up to 25 mg cm –2 of MnO 2, to deliver modified performance and cyclability of up to ≈100 mAh g –1 MnO 2 at the 100th cycle. These results show that novel APOs are a promising platform for the development of materials with tunable hydrophilicity and chain flexibility to push toward the enhanced long-term performance of ZIBs.
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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 | 0.001 |
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".