Development and Evaluation of Zeolites and Metal–Organic Frameworks for Carbon Dioxide Separation and Capture
Bibliographic record
Abstract
Abstract With increasing carbon dioxide (CO2) emissions from the combustion of fossil‐based fuels, the concentration of CO2 in the atmosphere is growing at 407.54 parts per million, as released in May 2016. Accordingly, the reduction of CO2 emissions is an essential issue for global climate changes. Tremendous efforts have been directed towards the goal of CO2 separation and capture. These have led to the development of novel classes of porous materials that possess unique potential applications in the capture and sequestration of CO2. Hence, this comprehensive review focuses on studying and analyzing newly developed methods to reduce greenhouse gas emissions and to sequester CO2 released from anthropogenic activities. It compares and analyzes, in terms of storage capacity and adsorption selectivity, the innovative technologies that capture CO2. Also described are the key advancements in CO2 capture from chemical absorption post‐ and precombustion industrial units and its subsequent physical adsorption by using various zeolites and metal–organic framework (MOF) materials for CO2 adsorption, storage, and separation. Current progress in MOF materials for CO2 capture is considered, and the potentials and limitations of new discoveries in the area are addressed, as it is a rapidly growing area. Furthermore, trends in the design of various kinds of porous structures with tailored macro‐ and microstructures and target surface properties are examined.
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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 | 0.000 |
| 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 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".