Three-Dimensional Printable Sodium Carbonate Composite Sorbents for Efficient Biogas Upgrading
Bibliographic record
Abstract
Abstract We have developed a new class of sodium carbonate/silicone composite sorbents that selectively capture carbon dioxide (CO2) and can purify biogas to natural gas pipeline-quality biomethane. These nontoxic composites can be three-dimensionally printed or extruded at low costs, can have high specific CO2 sorption rates (in excess of 5 μmol s–1 g–1 bar–1) and high selectivity due to their chemical mechanism, and can be regenerated with low-energy air stripping. Therefore, these composite sorbents combine the high selectivity of liquid sorbents with the high specific sorption rates and low regeneration energies found in many solid sorbents. We characterized these composite sorbents with X-ray computed tomography, scanning electron microscopy (SEM), and X-ray diffraction (XRD). Furthermore, we measured composite sorption capacities of up to 0.62 mol CO2 kg–1 and recorded breakthrough curves in a flow-through, fixed-bed reactor using both simulated biogas and locally sourced industrial biogas. Additional tests of the composite sorbent were carried out with pure CO2 in a sealed pressure drop apparatus. This experimental data was used to validate a numerical model of the setup and to simulate an industrial-scale biogas upgrading process. Finally, we performed a preliminary technoeconomic analysis for this upgrading process and found that this composite sorbent can upgrade biogas at a lower cost (∼$0.97 per GJ) than other currently implemented techniques.
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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) | 0.002 | 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".