Revealing Carbon Capture Chemistry with 17-Oxygen NMR Spectroscopy
Bibliographic record
Abstract
Carbon dioxide capture is an essential greenhouse mitigation technology to achieve netzero emissions. A key hurdle to the design of improved carbon capture materials is the lack of adequate tools to characterise how CO2 adsorbs. Solid-state nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising probe of CO2 capture, but it remains challenging to distinguish different adsorption products. Here we perform a comprehensive computational investigation of 22 amine-functionalised metal-organic frameworks and discover that 17O NMR is a powerful probe of CO2 capture chemistry that provides excellent differentiation of ammonium carbamate and carbamic acid species. The computational findings are supported by 17O NMR experiments on a series of CO2-loaded frameworks that clearly identify ammonium carbamate chain formation and provide new evidence for a mixed carbamic acid – ammonium carbamate adsorption mode. The fine sensitivity of 17O NMR to local chemistry also shows that hydrogen bonding schemes proposed in previous ammonium carbamate chain structures may be inaccurate and new structures are proposed. We further discover a new mixed CO2 adsorption mechanism and show that carbamic acid formation is more prevalent in this materials class than previously believed. Finally, we show that our methods are readily applicable to other adsorbents, and find support for ammonium carbamate formation in amine-grafted silicas. Overall our work paves the way for new investigations of carbon capture chemistry that can enable the design of improved materials.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".