Stochastic Generation of Sintered Titanium Powder-Based Porous Transport Layers in Polymer Electrolyte Membrane Electrolyzers and Investigation of Structural Properties
Bibliographic record
Abstract
The stochastic modeling of sintered titanium powder-based porous transport layers in polymer electrolyte membrane (PEM) electrolyzers using information gathered from microscale computed tomography (μ-CT) is proposed. The stochastic reconstructions were compared to the μ-CT reconstruction in terms of surface morphology and structural properties. Seeding parameter and filling radius were found to be the key parameters in this stochastic model. Parametric studies on the stochastic parameters were conducted, comparing pore and throat size distributions, mean pore and throat sizes, and numbers of pores and throats, with the μ-CT reconstruction. Increasing the seeding parameter led to increases in the number of pores and throats while decreasing mean pore and throat sizes. Increasing the filling radius led to decreases in number of pores and throats but increases in the mean pore and throat sizes. With appropriate seeding and filling parameters, the structural properties of the stochastic reconstruction closely matched the μ-CT reconstruction.
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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) | 0.000 | 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 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".