Effect of Porous Carbons’ Intrinsic Parameters on the Pressure Swing Adsorption of CO<sub>2</sub> from Biogas
Bibliographic record
Abstract
The current study investigates the use of six commercially available activated carbons and one carbon aerogel for the removal of CO 2 from biogas during biogas upgrading using pressure swing adsorption. Pure component CH 4 isotherms were determined gravimetrically at temperatures of 10, 30, 50, 70, and 90 °C and pressures up to 6.5 atm. This data was used to investigate the effect of intrinsic parameters of the carbons such as pore size, surface area, %oxidation, and ash content. In the high-pressure region, pore size in the range of <1 nm, <2 nm, and <3 nm significantly correlates to the CH 4 adsorption capacities. In the low-pressure region, the heterogeneous nature of the carbons dominates adsorption. Comparing CO 2 data to CH 4 data, carbons having a smaller pore size distribution, a higher %oxidation, and a higher %ash content are all beneficial for the separation of CO 2 from biogas. In this study, NZ-AC, M-30, and ACC have these favorable intrinsic properties along with large estimated working capacities, indicating them as prospective porous carbons for separating CO 2 from biogas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".