Enantioselective Recognition of Ammonium Carbamates in a Chiral Metal–Organic Framework
Bibliographic record
Abstract
Chiral metal–organic frameworks have attracted interest for enantioselective separations and catalysis because of their high crystallinity and pores with tunable shapes, sizes, and chemical environments. Chiral frameworks of the type M 2 (dobpdc) (M = Mg, Mn, Fe, Co, Ni, Zn; dobpdc 4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) seem particularly promising for potential applications because of their excellent stability, high internal surface areas, and strongly polarizing open metal coordination sites within the channels, but to date these materials have been isolated only in racemic form. Here, we demonstrate that when appended with the chiral diamine trans -1,2-diaminocyclohexane (dach), Mg 2 (dobpdc) adsorbs carbon dioxide cooperatively to form ammonium carbamate chains, and the thermodynamics of CO 2 capture are strongly influenced by enantioselective interactions within the chiral pores of the framework. We further show that it is possible to access both enantiomers of Mg 2 (dobpdc) with high enantiopurity (≥90%) via framework synthesis in the presence of varying quantities of d -panthenol, an inexpensive chiral induction agent. Investigation of dach–M 2 (dobpdc) samples following CO 2 adsorption─using single-crystal and powder X-ray diffraction, solid-state nuclear magnetic resonance spectroscopy, and density functional theory calculations─revealed that the ammonium carbamate chains interact extensively with each other and with the chiral M 2 (dobpdc) pore walls. Subtle differences in the non-covalent interactions accessible in each diastereomeric phase dramatically impact the thermodynamics of CO 2 adsorption.
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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".