Activated Carbon Produced from the Hydrothermal Treatment of Glucose with KOH Activation for Catalytic Absorption of CO<sub>2</sub> in a BEA-AMP Bi-Solvent Blend
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The amine-based postcombustion CO 2 capture (PCC) process involves absorption of CO 2 into a solvent and then regenerating the solvent to produce CO 2 . In this study, the effect of an activated carbon (AC) catalyst, synthesized through hydrothermal treatment and furnace activation on CO 2 absorption in a 4M BEA/AMP amine blend, was evaluated and compared with that of a KMgO/CNTs (1:4) catalyst. The physical and chemical properties of AC were investigated with a scanning electron microscope (SEM), CO 2 temperature-programmed desorption (CO 2 -TPD), Brunauer–Emmett–Teller (BET), powder X-ray diffraction (XRD), and thermogravimetric analyzer (TGA) and compared with the KMgO/CNTs (1:4) catalyst. The results showed that when compared against noncatalytic CO 2 absorption, AC enhanced the linear rate of CO 2 absorption by 33.3%, while for KMgO/CNTs, it was reported as 25.5%. The relatively higher surface area, combined with the higher number and strength of basic sites of AC relative to the KMgO/CNTs (1:4) catalyst, provided effective basic reaction sites for CO 2 absorption, thereby enhancing the rate of CO 2 absorption into the amine. AC was also relatively easier to synthesize which would provide a good replacement for the CNT-based catalyst which has carcinogenic tendencies.
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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) | 0.002 | 0.001 |
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".