Experimental study on the absorption enhancement of <scp> CO <sub>2</sub> </scp> by <scp>MDEA‐MEA</scp> based nanofluids
Bibliographic record
Abstract
Abstract Nanofluids promote the application of nanotechnology in the mass transfer process, which can effectively reduce mass transfer resistance and increase the gas–liquid mass transfer rate. A bubbling absorption system was used to explore the CO 2 absorption performance of methyldiethanolamine (MDEA) and monoethanolamine (MEA) blended nanofluids. TiO 2 nanoparticles and sodium polyacrylate (ASAP) surfactant were added to MDEA‐MEA aqueous solutions to prepare nanofluids. The effects of MDEA concentration, MEA concentration, blended amine ratio, and nanoparticle solid content on CO 2 capture performance were investigated, respectively. The study found that, compared with single‐component MEA/MDEA solutions, nanofluids have a more significant enhancement in CO 2 absorption of blended amines. The increase of MEA concentration in blended amine has a negative effect on the enhanced CO 2 absorption of TiO 2 nanoparticles. The enhancement factor of MDEA‐MEA nanofluids at each solid content is higher than that of two single‐component nanofluids. The best solid content of TiO 2 nanoparticles in the MDEA‐MEA system is 0.6 g/L, while the enhancement factor E in 25% MDEA + 5% MEA nanofluids can reach up to 1.36. Finally, a new empirical formula was proposed to predict the enhancement factor and optimum solid content of TiO 2 ‐MEA/MDEA nanofluids.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".