Highly porous carbons with superior performance for CO2 capture through hydrogen-bonding interactions
Bibliographic record
Abstract
Highly porous carbons were prepared by using polyaniline (PANI) as a carbon precursor and KOH as an activating agent via a one-step chemical activation process. The effects of the activation parameters such as activation temperature, KOH–PANI weight ratio and pre-heating temperature were fully investigated, through which the pore structure and the materials chemistry of the activated porous carbons were optimized. When studied as an adsorbent for CO2 capture, the optimized porous carbon exhibited a high CO2 capture capacity of 4.50 mmol g−1, high multi-cycle sorption/desorption stability and highly selective adsorption of CO2 over N2 (0.27 mmol g−1) at 25 °C. This superior performance for CO2 capture was found to be closely related to C–H groups on the carbon surface through hydrogen bonding interactions.
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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 | 0.000 |
| 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 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".