Application of MOFs-membrane in post-combustion carbon capture for electricity and thermal energy plants
Bibliographic record
Abstract
Greenhouse gas emission typically carbon dioxide which result to global warming is becoming a very acute issue to human society. In order to achieve a sustainable development and society, various approaches to reduce carbon emission have been investigated and applied to industry where the majority of carbon emission happens in recent decays. Carbon capture is one of the key ways to address the emission and widely applied to coal-fired power plants. However, typically carbon capture process requires high energy input during the absorption and desorption process with lead to a low net carbon captured rate and high cost of operation. Materials with low energy penalty and high carbon affinity is desired to address this problem. In this research, a more advanced material, metal-organic frameworks (MOFs), for carbon capture under post combustion condition is introduced and evaluated from single material and cooperating with mixed-matrix-membranes to achieve membrane separation. Furthermore, MOFs are usually lack of stability and CO2/H2O selectivity. CALF-20 as the solution to these problems will be introduced later.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".