Microporous and mesoporous materials for gas storage and separation: a review
Bibliographic record
Abstract
ABSTRACT Various technologies and materials have been applied for the storage of clean gas energy, which also could control the combustion process to reduce CO 2 emissions. Over the years, microporous and mesoporous materials have been developed as useful technology for gas storage and separation. However, no materials can adequately meet the needs of all applications. The present paper gives an overview of the various materials for gas storage and separation. The results showed that carbonaceous adsorbents were the most potential for meeting the US DoE system targets for gas (methane and hydrogen) storage. Meanwhile, the properties of metal organic frameworks and covalent organic frameworks (thermodynamics and kinetics) had significant improvements with the development of material science. Further, zeolites performed well in CO 2 separation, but necessitated a high temperature for recovery. It was concluded that the utility of any new material will ultimately be determined not only by its material performance but also by its system performance, and much work is still required to be performed in the application of microporous and mesoporous materials for gas storage and separation in the future. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".