Elucidating CO2 Chemisorption in Diamine-Appended Metal–Organic Frameworks
Bibliographic record
Abstract
The widespread deployment of carbon capture and sequestration as a climate change mitigation strategy could be facilitated by the development of more energy-efficient adsorbents. Diamine-appended metal–organic frameworks of the type diamine–M2(dobpdc) (M = Mg, Mn, Fe, Co, Ni, Zn; dobpdc4− = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) have shown promise for carbon capture applications, although questions remain regarding the molecular mechanisms of CO2 uptake in these materials. Here, we leverage the crystallinity and tunability of this class of frameworks to perform a comprehensive study of CO2 chemisorption. Using multinuclear nuclear magnetic resonance (NMR) spectroscopy experiments and van der Waals-corrected density functional theory (DFT) calculations for thirteen diamine–M2(dobpdc) variants, we demonstrate that the canonical CO2 chemisorption products—ammonium carbamate chains and carbamic acid pairs—can be readily distinguished, and that ammonium carbamate chain formation dominates for diamine–Mg2(dobpdc) materials. In addition, we elucidate a new chemisorption mechanism in the material dmpn Mg2(dobpdc) (dmpn = 2,2-dimethyl-1,3-diaminopropane), which involves formation of a 1:1 mixture of ammonium carbamate and carbamic acid and accounts for the unusual adsorption properties of this material. Finally, we show that the presence of water plays an important role in directing the mechanisms for CO2 uptake in diamine–M2(dobpdc) materials. Overall, our combined NMR and DFT approach enables a thorough depiction and understanding of CO2 adsorption within diamine–M2(dobpdc) compounds, which may aid similar studies in other amine-functionalized adsorbents in the future.
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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".