Improved pre‐treatment of porous stainless steel substrate for preparation of Pd‐based composite membrane
Bibliographic record
Abstract
Abstract The integrity and robustness of Pd‐based composite membranes depend heavily on the quality of the metallic substrate. This paper presents an improved method for pre‐treating the substrate. Detailed pre‐treatment steps of these porous stainless steel substrates included washing, polishing, etching, coating the first alumina layer, calcining the first alumina layer, coating the second alumina layer, calcining the second alumina layer and repair. The repair cycle was the most helpful step, greatly improving the membrane reproducibility and stability. Repeat coating with dilute suspensions of alumina particles of 0.3 µm mean diameter can repair defects on the top alumina layer. The hydrogen permeation flux was 268 × 10−4 N m3/(m2 h Pa) after one repair cycle, decreasing by 14% after an additional repair cycle. The hydrogen permeation flux of the pre‐treated porous stainless steel substrate was thus maintained at a level comparable with previous work. Surface topography revealed that the average roughness was reduced from 1.15 to 0.47 µm, and the surface Maximum Peak to Valley decreased from 17.5 to 6.0 µm/mm2 as a result of the pre‐treatment.
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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 | 0.000 |
| 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 | 0.001 |
| 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".