Permeability of ultra thin porous materials by poro-elastic response
Why this work is in the frame
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Bibliographic record
Abstract
Thin porous materials frequently make up important components in electrochemical systems. For example, fuel cell catalyst layer electrodes are thin (≈ 10 μm) porous layers that have high surface area to volume ratios, efficient mass transport, and lower material requirements. Accurate measurements of material properties are essential for understanding current and designing new electrochemical systems. However, these catalyst layers are often delicate and awkward to handle making it difficult to characterize them using conventional experimental methods. Therefore, it is important to develop new experimental techniques specific for thin porous materials.This work explored nanoindentation as a method to estimate the properties of thin porous materials. By fitting poroelastic finite element models to experimental stress relaxation curves, permeabilities of thin (300 – 2000 μm) agar gels of varying concentrations were determined and found to agree with reported literature values. However, similar measurements for fuel cell catalyst layers did not produce reliable permeability estimates. Stress relaxations were not present in saturated catalyst layer indentation measurements, indicating the experimental setup used in this project was unable to capture the dynamics of fluid movement in the layers. Poroelastic finite element models also showed the duration of stress relaxations decreased as indentation depths became large with respect to the total thickness of the sample. The reasons nanoindentation didn't successfully characterize the poroelastic behavior of catalyst layers are discussed and suggestions for future experimental designs are provided.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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