Benchmarking selective capture of trace CO2 from C2H2 using an amine-functionalized adsorbent
Bibliographic record
Abstract
Purifying C2H2 by removing trace CO2 is critically needed yet challenged by their analogous physical properties. Herein, we report a commercial resin adsorbent HP20 (Diaion® HP-20 Resin) loaded with polyethyleneimine (PEI@HP20) which selectively captures trace CO2 and excludes C2H2. PEI@HP20 possesses a high CO2 adsorption capacity (4.35 mmol/g) at 100 kPa and 298 K and a record CO2/C2H2 uptake ratio compared with all reported CO2-selective adsorbents. The ideal adsorbed solution theory selectivity reaches 1.33×107. The pilot-scale pressure-temperature swing adsorption on 2 kg PEI@HP20 further validated that it can obtain >99.99% purity C2H2 from CO2/C2H2(1/99, v/v) mixtures with a high yield of 344.7 g per cycle. The combination of multinuclear solid-state Nuclear Magnetic Resonance, Fourier Transform infrared spectroscopy and density functional theory calculations reveal that the performance of PEI@HP20 relies on a dual chemisorption/physisorption mechanism. This work highlights a promising method to develop green, low cost, high efficiency, and readily scalable CO2-selective adsorbent. Purifying C2H2 by removing trace CO2 is needed but challenging due to their analogous physical properties. Herein, the authors report the pilot-scale production of a commercial resin adsorbent loaded with polyethyleneimine for the selective capture of CO2 traces and exclusion of C2H2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".