Influence of Zn incorporation on the structural and gas adsorption properties of CdIF-1 metal-organic frameworks
Bibliographic record
Abstract
Abstract Precise control of pore structure in metal-organic frameworks (MOF) is crucial for optimizing gas adsorption and separation performance. By introducing a second metal Zn into the CdIF-1 MOF, we confirmed the preservation of its long-range topology, indicating a single-phase structure in bimetallic Cd1−xZnxIF-1 (x = 0 to 0.5) MOF. XRD analysis reveals a unit cell contraction with increasing Zn content, which simultaneously modifies the surface area and pore structure. To investigate the influence of Zn incorporation on the local structure of the metal centers, as well as metal distribution throughout the framework, we employed X-ray absorption spectroscopy (XAS) and solid-state NMR (SSNMR) spectroscopy. Cd L3-edge and Zn K-edge XAS spectra indicate that Cd and Zn share a very similar local coordination environment in Cd1−xZnxIF-1, whereas 111Cd SSNMR results suggest a random replacement of Cd by Zn. Gas adsorption experiments for CO2, C2H2, C2H4, C2H6, and C3H8 reveal that these subtle structural modifications have an impact on the adsorption performance. Interestingly, as the kinetic diameter of the gas molecules approaches or even slightly surpasses the MOF aperture size, the adsorption capacity increases.
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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".