Preparation of an adsorbent derived from canola hull by slow pyrolysis for effective carbon dioxide adsorption
Bibliographic record
Abstract
Canola hull was proposed as a valuable precursor for activated carbon production, due to its abundance, low cost, and economic viability. This study aimed to develop a waste-based biosorbent from canola hulls by slow pyrolysis for effective CO2 capture. The biochar was synthesized from the canola hull using a fixed-bed tubular reactor through slow pyrolysis at various temperatures. The optimum biochar was activated using KOH as a chemical activating agent at different impregnation ratios. The biochar and activated carbon were characterized by elemental analysis, thermogravimetric analysis, surface textural analysis and FT-IR analysis. All the activated carbon samples with impregnation ratios of 0, 0.2, and 0.4 were screened at a total inlet mass flow rate of 100 mL/min (15% CO2 + 85% N2). Activated carbon prepared with an impregnation ratio of 0.4 (0.4AC) with a specific surface area of 1112 m2/g exhibited the highest adsorption capacity of CO2 (2.9 mmol/g) at 25 °C under ambient pressure when the feed gas was 15% CO2). 0.4AC was chosen for further study at different feed compositions. The breakthrough curves were analyzed for the compositions 5%, 15%, and 25% CO2 in feed, and the effects of adsorption parameters were discussed. The maximum CO2 uptake of the canola hull-based activated carbon (0.4AC) is 13.0 mmol/g at ambient pressure and 25 °C, with 100% CO2 inlet. Adsorption isotherm and kinetic were studied on the 0.4AC adsorbent. The canola hull-based adsorbent with desirable physiochemical and surface textural properties can work as an effective adsorbent for CO2 capture.
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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 | 0.001 | 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".