Research on the adsorption capacity and mechanism of carbon dioxide by ion exchange resin loaded with metal–organic cages
Bibliographic record
Abstract
Abstract Metal–organic cages (MOCs), a novel type of porous material, have shown great potential in the adsorption and separation of CO 2 gas. Zirconium‐based metal–organic cages (Zr‐MOCs) have garnered special attention because of their outstanding stability and high solubility. Nevertheless, due to its powdery nature and the tendency for easy aggregation, its application in the industrial field is limited. Here, drawing inspiration from water treatment, the inexpensive and stable ionic exchange resin D001 was employed as the supporting material for loading Zr‐MOCs, and D001‐Zr‐MOC was prepared. The adsorption capacity of D001‐Zr‐MOC for CO 2 from the mixed gas (15% CO 2 /85% N 2 ) under diverse loads of Zr‐MOCs, various temperatures, and different gas flow rates was investigated, and its cyclic adsorption performance for CO 2 was determined. The adsorption mechanism of CO 2 on D001‐Zr‐MOC was probed by means of adsorption energy, adsorption isotherm, adsorption heat, and diffusion coefficient. The results showed that the maximum CO 2 adsorption capacity of D001‐Zr‐MOC was 3.96 mmol/g. The adsorption capacity decreased by merely 4.62% after 10 adsorption/desorption cycles. The adsorption energy and adsorption heat of D001‐Zr‐MOC for CO 2 molecules are relatively high, which indicates that D001‐Zr‐MOC has good adsorption capacity and selectivity for CO 2 molecules. The adsorption of CO 2 is regarded as a typical Langmuir monolayer adsorption, and it is verified that a chemical reaction occurred between CO 2 and the adsorbent. CO 2 possesses a higher diffusion coefficient, and the excellent cyclic adsorption capacity of D001‐Zr‐MOC is verified.
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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) | 0.001 | 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".