Zeolite A-Carbon Membranes: Possibilities in H2 Purification and how to Overcome their Shortcomings
Bibliographic record
Abstract
This work describes the modification of zeolite Na-LTA membranes supported on macroporous carbon materials, prepared by a combination of secondary hydrothermal treatment followed by different alternative post-synthesis procedures, which aim at improving the permeance properties of the as-synthesized Na-LTA membranes with a simulated reformer mixture (H2, CO, CO2 and H2O) towards their use in a hydrogen purification device.These post-synthetic treatments include the deposition of a thin layer of amorphous silica formed by the hydrolysis of a silicon alcoxide, the coating with a thin metallic film by electroless plating, and the deposition of noble metal nanoparticles. Our results indicate that some of these treatments, which may be performed very quickly compared to other treatments which are generally used in order to improve the quality of the membranes, result in membranes which may effectively separate H2 from CO under simulated reformer conditions. Considering the simple approach employed in some of the cases described in this study, the potential benefits should be considered highly interesting in fields such as membrane recovery and membrane selectivity control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".