Enhanced CO<sub>2</sub> Adsorption Using MgO-Impregnated Activated Carbon: Impact of Preparation Techniques
Bibliographic record
Abstract
The development of a facile and sustainable approach to produce magnesium oxide (MgO) activated carbons impregnated through a single-step activation of biochar is reported. In a single-step activation process, biochar is impregnated with 3 and 10 wt % of magnesium salt solutions followed by steam activation. In a two-step method, activated carbon, the product of steam activation of biochar, is impregnated with magnesium salt using the incipient wetness and excess solution impregnation process and calcined. The impacts of activation method, impregnation method, and metal content are evaluated, and the product qualities are compared in terms of porosity and surface chemistry. The sorbents are then used for CO 2 capture in low partial pressure of CO 2 at 25 and 100 °C from a feed containing 15% CO 2 in N 2 in a fixed-bed reactor. The incipient wetness of activated carbons results in the highest CO 2 uptake (49 mg/g) at 25 °C, while single-step impregnation of biochar with rinsing step yields the largest surface area (760 m 2 /g) and the second highest CO 2 uptake (47 mg/g). The increase in Mg content from 3 to 10 wt % results in the smaller surface area and higher CO 2 uptake suggesting that the metal content has a greater impact than porosity and surface area. Rinsing the Mg impregnated activated carbon with water results in the larger surface area and higher CO 2 uptake in all samples. Moreover, the CO 2 adsorption runs at 100 °C shows a 65% increase using MgO impregnated activated carbon as compared to steam activated carbon indicating that MgO impregnation of activated carbon can overcome the limitation of using nontreated activated carbon at moderate operating temperature of 100 °C and low partial pressure of CO 2 of 15 mol %.
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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) | 0.001 | 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".