A priori evaluation of the printability of water-based anode dispersions in inkjet printing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 $$^\circ$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> . 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it