Nitrogen‐doping microporous adsorbents prepared from palm kernel with excellent CO<sub>2</sub> capture property
Bibliographic record
Abstract
In post‐combustion CO 2 capture, waste biomass is a favourable precursor to prepare porous carbons due to its low cost, renewability, and unique microstructures. This study presents a facile method for preparing N‐doping porous carbons. Palm kernel shell was selected as carbon precursor, and its inherent silica species acted as a natural template to form hierarchical pores. The obtained samples exhibit predominant characteristics with highly developed micropores and a high N content providing an important contribution to CO 2 adsorption capacity, which can reach up to 5.29 and 2.30 mmol/g under 100 kPa at 25 and 60 °C, respectively. Moreover, the resultant porous carbons also exhibit excellent cycling stability after 20 cycles. Furthermore, the activation mechanism was investigated by the quantitative thermogravimetry‐mass spectrometry (TG‐MS) method.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".