Polyethyleneimine-impregnated activated carbon nanofiber composited graphene-derived rice husk char for efficient post-combustion CO <sub>2</sub> capture
Bibliographic record
Abstract
Abstract This study presents the fabrication of polyethyleneimine (PEI)–graphene-derived rice husk char (GRHC)/activated carbon nanofiber (ACNF) composites via electrospinning and physical activation processes and its adsorption performance toward CO 2 . This study was performed by varying several parameters, including the loading of graphene, impregnated and nonimpregnated with amine, and tested on different adsorption pressures and temperatures. The resultant ACNF composite with 1% of GRHC shows smaller average fiber diameter (238 ± 79.97 nm) with specific surface area ( S BET ) of 597 m 2 /g, and V micro of 0.2606 cm 3 /g, superior to pristine ACNFs (202 m 2 /g and 0.0976 cm 3 /g, respectively). ACNF/GRHC0.01 exhibited CO 2 uptakes of 142 cm 3 /g at atmospheric pressure and 25°C, significantly higher than that of pristine ACNF’s 69 cm 3 /g. The GRHC/ACNF0.01 was then impregnated with PEI and further achieved impressive increment in CO 2 uptake to 191 cm 3 /g. Notably, the adsorption performance of CO 2 is directly proportional to the pressure increment; however, it is inversely proportional with the increased temperature. Interestingly, both amine-impregnated and nonimpregnated GRHC/ACNFs fitted the pseudo first-order kinetic model (physisorption) at 1 bar; however, best fitted the pseudo second-order kinetic model (chemisorption) at 15 bar. Both GRHC/ACNF and PEI-GRHC/ACNF samples obeyed the Langmuir adsorption isotherm model, which indicates monolayer adsorption. At the end of this study, PEI-GRHC/ACNFs with excellent CO 2 adsorption performance were successfully fabricated.
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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".