Plasma-Modified Cellulose-Based Li-Ion Electrodes for Rechargeable Aqueous Li-Ion Batteries
Bibliographic record
Abstract
The composite electrodes of most aqueous electrolyte rechargeable lithium-ion batteries (ARLB) are usually fabricated using noxious fluoritinated binders (PTFE and PVDF) that are dissolved or dispersed in solvents. The use of water-soluble biobased binders reduces the environmental footprint of ARLB, thereby promoting their application. Carboxymethyl cellulose (CMC) is an efficient and environmentally friendly binder that can be used in organic electrolyte lithium batteries. However, the application of this material is limited by its dissolution in an aqueous electrolyte. In a recent study, it was shown that the efficiency of the CMC binder in the ARLB may be enhanced by depositing an organosilicon polymer thin film onto the surface of the electrode using plasma. Herein, in this work, the plasma-modified CMC-based electrode is further characterized, and the mechanism underlying its operation in a dilute aqueous electrolyte is divulged. It is found that the water repellency and porosity of the organosilicon thin film protect the topmost surface of the composite electrode against dissolution in electrolytic solution without compromising the Li + transport from the electrolyte to the electrode active material. The high electrochemical capacity and stability (over more than 100 cycles) of the cell further indicate that this plasma-deposited layer can withstand the mechanical stress associated with electrochemical cycling.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".