Porous Structure of β-Cyclodextrin for CO2 Capture: Structural Remodeling by Thermal Activation
Bibliographic record
Abstract
With a purpose of extending the application of β-cyclodextrin (β-CD) for gas adsorption, this paper aims to reveal the pore formation mechanism of a promising adsorbent for CO2 capture which was derived from the structural remodeling of β-CD by thermal activation. The pore structure and performance of the adsorbent were characterized by means of SEM, BET and CO2 adsorption. Then, the thermochemical characteristics during pore formation were systematically investigated by means of TG-DSC, in situ TG-FTIR/FTIR, in situ TG-MS/MS, EDS, XPS and DFT. The results show that the derived adsorbent exhibits an excellent porous structure for CO2 capture accompanied by an adsorption capacity of 4.2 mmol/g at 0 °C and 100 kPa. The porous structure is obtained by the structural remodeling such as dehydration polymerization with the prior locations such as hydroxyl bonded to C6 and ring-opening polymerization with the main locations (C4, C1, C5), accompanied by the release of those small molecules such as H2O, CO2 and C3H4. A large amount of new fine pores is formed at the third and fourth stage of the four-stage activation process. Particularly, more micropores are created at the fourth stage. This revealed that pore formation mechanism is beneficial to structural design of further thermal-treated graft/functionalization polymer derived from β-CD, potentially applicable for gas adsorption such as CO2 capture.
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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".