A priori evaluation of the printability of water-based anode dispersions in inkjet printing
Bibliographic record
Abstract
Abstract Inkjet printing represents a disruptive additive manufacturing technology that has emerged as an innovative approach to generate customized lithium-ion batteries by tailored dispersions. However, electrode dispersions cause a complex non-Newtonian behavior which hampers the processability. This paper demonstrates a novel procedure for an a priori evaluation of the printability of aqueous graphite dispersions. Therefore, dispersions with a varying active material content were prepared and the printability was examined through a characterization of the drop formation and the drop deposition behavior. While the drop formation was observed by in-situ monitoring, the drop deposition was analyzed in ex-situ test setups. The rheological properties were systematically determined to calculate nondimensional numbers that describe the dispensing behavior. Consequently, their capability to predict the stability of the drop formation was evaluated. The results revealed that a graphite dispersion with a content of 2 m% allowed for a stable drop formation. No splashing occurred on the substrate during the drop deposition and sufficient wetting can be assumed due to a contact angle of below 90 $$^\circ$$ ∘ . Conclusions were drawn to further enhance the active material content. Due to the universality of the proposed approach, it is expected to be applicable to different dispersion systems.
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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 | 0.001 | 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".