Preparation of Microporous Carbon from <i>Sargassum horneri</i> by Hydrothermal Carbonization and KOH Activation for CO<sub>2</sub> Capture
Bibliographic record
Abstract
High-performance microporous activated carbon (AHC) for CO2 capture was prepared from an emerging marine pollutant, Sargassum horneri, via hydrothermal carbonization (HTC) and KOH activation. The as-synthesized carbon material was characterized by N2 sorption-desorption measurement, TGA, SEM, XRD, FTIR, and elemental analysis. Impressively, the activated carbon exhibited high specific surface area (1221 m2/g), narrow distributed micropores (∼0.50 nm), and a relatively high nitrogen content (3.56 wt.%), which endowed this carbon material high CO2 uptake of 101.7 mg/g at 30°C and 1 bar. Moreover, the carbon material showed highly stable CO2 adsorption capacity and easy regeneration over four adsorption-desorption cycles. Two kinetic models were employed in this work and found that the pseudo-first-order kinetic model (R2 = 0.99) provided the best description. In addition, the high CO2 uptake is mainly attributed to the presence of abundant narrow microporous. The macroporous structure of hydrochar (HC) played an important role in the production of microporous carbon with high adsorption properties. This work provides an efficient strategy for preparing microporous activated carbon from Sargassum horneri, and AHC is a promising candidate acting as an efficient CO2 adsorbent for further industrial application.
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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) | 0.000 | 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) | 0.001 | 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".