Selective CO<sub>2</sub> Capture by Activated Carbons: Evaluation of the Effects of Precursors and Pyrolysis Process
Bibliographic record
Abstract
Activated carbons are produced from different Canadian waste biomasses including agricultural waste (wheat straw and flax straw), forest residue (sawdust and willow ring), and animal manure (poultry litter). The precursors are carbonized through the fast and slow pyrolysis processes and then activated with potassium hydroxide. A fixed-bed reactor is used for temperature swing adsorption of CO 2 in a gas mixture of N 2, O 2, and CO 2 to study the cyclic CO 2 adsorption capacity and selectivity of the produced activated carbons. The breakthrough adsorption capacity of the produced activated carbon is measured under a flue gas condition of 15 mol % of CO 2, 5 mol % of O 2, and 80% of N 2 at 25 °C and atmospheric pressure. Slow pyrolysis based activated carbon has a lower surface area and total pore volume but higher adsorption capacity in the presence of N 2 . Sawdust based activated carbon synthesized using the slow pyrolysis process creates the highest ultra-micropore volume of 0.36 cm 3 /g, and the highest adsorption capacity in N 2 (78.1 mg/g) but low selectivity (2.8) over O 2 because of the oxygen functional groups on the surface. Ultra-micropores and surface chemistry of adsorbents are far more important than particle size, total pore volume, and internal surface area of the adsorbents. All the samples fully recovered their initial adsorption capacity in each cycle (for up to 10 cycles). This work also demonstrates that adsorption capacity and selectivity of activated carbon can be controlled and optimized through the choice of starting material and carbonization conditions.
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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 | 0.001 |
| 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 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".