Microscopic Origins of Enhanced Gas Adsorption and Selectivity in Mixed-Linker Metal–Organic Frameworks
Bibliographic record
Abstract
We use molecular simulations to study the gas adsorption properties of metal–organic framework (MOF) materials composed of mixtures of linker groups, focusing on the prototypical MTV-MOF-5 systems. While MOF functionalization is well-known to influence gas uptake, we show that the absolute gas uptake is frequently not merely a sum of linear contributions from its constituent functionalities but rather there exists a synergistic enhancement that arises due to cooperative adsorbate–linker interactions involving multiple functionalities. In certain mixed-linker MOFs, such cooperativity yields increased gas uptake over any possible corresponding pure “parent” compound. Considering a model system based on ZIF-8, we are able to clearly demonstrate the microscopic origin of this synergy, arising from the strong, simultaneous interactions of multiple linker groups with a single adsorbate. We also provide a concrete example of a mixed-linker MOF that exhibits gas adsorption superior to that of any of its pure parent compounds. We conclude that such cooperativity should be a fairly general phenomenon and suggest some design guidelines that can be exploited to synthesize synergistically enhanced mixed MOFs.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".