The Role of Binders for Water-Based Anode Dispersions in Inkjet Printing
Bibliographic record
Abstract
Binders play a pivotal role in the production and the operation of lithium-ion batteries. They influence a number of key dispersion characteristics and battery parameters. In the light of growing interest in additive manufacturing technologies, binders were found to decisively govern the processability due to the induced complex non-Newtonian behavior. This paper examines the relevance of various binder derivatives for aqueous graphite dispersions that can be employed in inkjet printing. Two different carboxymethyl cellulose (CMC) derivatives with strongly deviating molecular weights were employed. The impact of the inherent polymer characteristics on the processability and the electrode characteristics were explored. Therefore, miscellaneous studies were carried out at the dispersion, the electrode, and the cell levels. The results revealed that the CMC with the lower molecular weight affected most of the studied characteristics more favorably than the counterpart with a higher molecular weight. In particular, the processability, encompassing drop formation and drop deposition, the cohesion behavior, and the electrochemical characteristics, were positively impacted by the low-molecular-weight CMC. The adhesion behavior was enhanced using the high-molecular-weight CMC. This demonstrates that the selection of a suitable binder derivative merits close attention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".