A comprehensive review on the synthesis techniques of porous materials for gas separation and catalysis
Bibliographic record
Abstract
Abstract Intrinsic structural characteristics of porous materials have found significant applications in selective separation of gases and heterogeneous catalysis. While using porous materials for gas separation and catalysis, some of the challenging issues are the strength of catalytic sites, hydrothermal stability, crystalline order of solids, and selectivity. Researchers' ability to engineer the synthesis strategies rationally can help overcome technological challenges. In recent years, breakthroughs in porous materials are focused on developing different chemistries to control the pore architecture. This review focuses on recent advances made in synthesis strategies of porous materials and their impact on gas separation and catalysis applications. A significant part of this review is devoted to various synthesis methods, such as various types of templating methods, molecular layer deposition, sol–gel technique, and polymerization methods. Improvement in various catalytic reactions and gas separations due to different functionalization methods is also summarized. Lastly, we have discussed the applications of porous materials in the form of adsorbents and membranes in commercial processes. We hope that this review will serve as a quick reference for beginners who want to synthesize porous materials with control of pore structure.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".