Numerical Investigations on the Ultrasonic Atomization of Catalyst Inks for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
In the fabrication of a proton exchange membrane fuel cell electrode, the catalyst layers (CLs) are coated onto either a gas diffusion medium or a membrane. The deposition method of the catalyst ink plays an important role in the structure of the CL, which directly affects its electrochemical performance. Ultrasonic spraying is a method commonly employed for depositing catalyst ink onto the membrane, and the consequent droplet size is correlated to the microstructure of the CLs. In this study, a two-dimensional nozzle model that vibrates at an ultrasonic frequency was developed to simulate the spraying process of the catalyst ink. The volume of the fluid method with dynamic meshing was used. Parametric studies were carried out to gain insights into the atomization process. It was found that measures such as increasing the nozzle amplitude and frequency, and selecting the surface tension and viscosity of the catalyst ink within a proper range, are conducive to obtaining finer droplets and narrower droplet size distribution. Simulation results of non-Newtonian fluids with different viscosity ranges show that the ink fluid with higher viscosity and low shear rate improves the spray quality. This observation is consistent with the results of Newtonian fluids with different viscosities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".