A Diaminopropane-Appended Metal–Organic Framework Enabling Efficient CO<sub>2</sub> Capture from Coal Flue Gas via a Mixed Adsorption Mechanism
Bibliographic record
Abstract
A new diamine-functionalized metal–organic framework comprised of 2,2-dimethyl-1,3-diaminopropane (dmpn) appended to the Mg 2+ sites lining the channels of Mg 2 (dobpdc) (dobpdc 4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) is characterized for the removal of CO 2 from the flue gas emissions of coal-fired power plants. Unique to members of this promising class of adsorbents, dmpn–Mg 2 (dobpdc) displays facile step-shaped adsorption of CO 2 from coal flue gas at 40 °C and near complete CO 2 desorption upon heating to 100 °C, enabling a high CO 2 working capacity (2.42 mmol/g, 9.1 wt %) with a modest 60 °C temperature swing. Evaluation of the thermodynamic parameters of adsorption for dmpn–Mg 2 (dobpdc) suggests that the narrow temperature swing of its CO 2 adsorption steps is due to the high magnitude of its differential enthalpy of adsorption (Δ h ads = −73 ± 1 kJ/mol), with a larger than expected entropic penalty for CO 2 adsorption (Δ s ads = −204 ± 4 J/mol·K) positioning the step in the optimal range for carbon capture from coal flue gas. In addition, thermogravimetric analysis and breakthrough experiments indicate that, in contrast to many adsorbents, dmpn–Mg 2 (dobpdc) captures CO 2 effectively in the presence of water and can be subjected to 1000 humid adsorption/desorption cycles with minimal degradation. Solid-state 13 C NMR spectra and single-crystal X-ray diffraction structures of the Zn analogue reveal that this material adsorbs CO 2 via formation of both ammonium carbamates and carbamic acid pairs, the latter of which are crystallographically verified for the first time in a porous material. Taken together, these properties render dmpn–Mg 2 (dobpdc) one of the most promising adsorbents for carbon capture applications.
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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".