Improvement of CO<sub>2</sub> absorption using AL<sub>2</sub>O<sub>3</sub> nanofluids in a stirred thermostatic reactor
Bibliographic record
Abstract
The improvement of CO2 absorption by Al2O3 nanofluids with deionized water as the base fluid was studied experimentally. The reactor used was a stirred thermostatic reactor, operated batchwise. Pure CO2 was employed in all the experiments. The content of Al2O3 in nanofluids ranged from 0 to 0.2 % (wt). Sodium dodecyl benzene sulfonate (SDBS) was employed to improve the stability of the nanofluids, and the combined effect of nanoparticles and surfactants on the absorption enhancement was studied. The parameters such as the concentration of Al2O3 nanoparticles and surfactants, the stirring speed, and the ultrasonic time were varied. The results show that the nanoparticle mass fraction and the ultrasonic time have an optimum value for the CO2 absorption enhancement. The combination of surfactants and nanoparticles improves the enhancement performance of Al2O3 nanofluids more effectively than that without surfactants. With the increase of the stirring speed, the effective absorption ratio in stable nanofluids declines, while in poorly dispersed Al2O3 suspensions, it is increased first and then reduced. The mechanism of the Al2O3 nanofluid enhancing CO2 absorption is discussed accordingly. The absorption enhancement by the Al2O3 nanofluid is mainly attributable to convective motion induced by the Brownian motion.
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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".