Influence of Pd nanoparticles on the structural stability and hydrogen separation of graphene oxide membranes
Bibliographic record
Abstract
Thin graphene oxide (GO) membranes decorated with palladium (Pd) nanoparticles (NPs) were fabricated and evaluated for high-temperature H₂/N₂ separation. Pd NPs were synthesized by chemical reduction using ascorbic acid and incorporated into GO nanosheets. Laminate thin film composite (TFC) membranes were prepared by vacuum filtration of a solution of GO and Pd NPs at varying loadings (0.1–5 wt%) onto polyethersulfone (PES) supports. A neat GO membrane (225 nm) exhibited an H₂ permeance of 500 GPU and an H₂/N₂ selectivity of ~31 at 220 °C, attributed to thermal reduction of GO that enhances molecular sieving. Incorporation of 0.1 wt% Pd raised permeance to 650 GPU with a moderate drop in selectivity to 28, while 0.5 wt% Pd enhanced permeance to 1400 GPU with a selectivity of 17.5. Higher Pd loadings (5 wt%) further increased permeance to 16,000 GPU but caused selectivity to drop below 5, due to nanoparticle agglomeration and the formation of nonselective voids. These results demonstrate that low Pd loadings can enhance hydrogen flux with a moderate selectivity loss, but excessive Pd disrupts GO layering. Pd NPs, which exhibit lower hydrogen embrittlement susceptibility than bulk Pd, offer a promising route to high-temperature H₂ separation with optimized Pd content.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".