Development and Evaluation of Zeolites and Metal–Organic Frameworks for Carbon Dioxide Separation and Capture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With increasing carbon dioxide (CO 2 ) emissions from the combustion of fossil‐based fuels, the concentration of CO 2 in the atmosphere is growing at 407.54 parts per million, as released in May 2016. Accordingly, the reduction of CO 2 emissions is an essential issue for global climate changes. Tremendous efforts have been directed towards the goal of CO 2 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 CO 2 . Hence, this comprehensive review focuses on studying and analyzing newly developed methods to reduce greenhouse gas emissions and to sequester CO 2 released from anthropogenic activities. It compares and analyzes, in terms of storage capacity and adsorption selectivity, the innovative technologies that capture CO 2 . Also described are the key advancements in CO 2 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 CO 2 adsorption, storage, and separation. Current progress in MOF materials for CO 2 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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it