Ionic Conductivity and Lithium Transference Number in LiClO<sub>4</sub>-Doped Carboxymethyl Cellulose
Bibliographic record
Abstract
Most new battery technologies beyond lithium-ion batteries suffer from mechanical and structural issues at the microscale within the electrode. This is due to large volume changes (e.g., lithium silicide anodes), morphological changes (e.g., sulfur cathodes), or mechanical incompatibility of materials (e.g., all-solid-state electrodes) as the battery is cycled. As such, the electrode matrix is becoming increasingly important to battery performance. New binders possessing (i) high ionic conductivity, (ii) good adhesion, and (iii) good elasticity and strength have been attracting increasing attention. Here, we examine the ionic conductivity of a highly adhesive, water-dispersible biopolymer binder – carboxymethyl cellulose (CMC). We show that the CMC binder employed in liquid electrolytes exhibits an ionic conductivity in the order of 10 –7 S cm –1 . In contrast, solvent-free CMC can dissolve up to approximately 20 wt % LiClO 4, approaching an ionic conductivity of 10 –6 S cm –1 at room temperature. Furthermore, impedance analysis and polarization experiments show high cation transference numbers, above 0.5, confirming a strong participation of lithium in ion transport. These values demonstrate sufficient ion transport with minimal resistive impact in all-solid-state batteries, proposing a new route toward mechanically stable all-solid-state electrodes.
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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 | 0.001 |
| 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 | 0.000 |
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.
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".