Selection of deep eutectic solvents for <scp> CO <sub>2</sub> </scp> separation processes by thermodynamic analysis
Bibliographic record
Abstract
Abstract To mitigate global warming, it is crucial to reduce CO 2 emissions from the combustion of fossil fuels. CO 2 separation plays a key role in this process. Deep eutectic solvents (DESs) have demonstrated significant advantages and are considered promising liquid absorbents. In this study, DESs were chosen as absorbents for CO 2 separation from various CO 2 streams, including flue gas, lime kiln gas, bio‐syngas, and biogas by thermodynamic analysis. Based on the criteria of the amount of absorbents required and energy use, several DESs including N,N‐dimethylethanolammonium chloride/urea (1:1), choline chloride/urea (2:3), choline chloride/urea (2:5) were selected for the four CO 2 streams. Meanwhile, choline chloride/lactic acid (1:5) was selected for flue gas, tetrabutylammonium bromide/lactic acid (1:3) was selected for lime kiln gas, and N,N‐dimethylethanolammonium chloride/urea (1:1) was selected for bio‐syngas and biogas. It is revealed that the selected DESs exhibit a lower amount of absorbents required and lower energy use than those of other DESs and their aqueous solutions. The relationship among the absorption pressure, the energy use, the physical properties, and the critical properties of DESs are established for the four CO 2 streams. It is shown that the absorption pressure of the DESs can be fitted with the physical properties, which are density and heat capacity, with average relative deviations lower than 5%, while the energy use can be fitted with the critical properties, which are critical temperature and critical pressure, with average relative deviations below 1%. It suggests that the selected DESs have potential for further applications.
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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) | 0.001 | 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".