Grasping hydrogen adsorption and dynamics in metal–organic frameworks using <sup>2</sup>H solid-state NMR
Bibliographic record
Abstract
Record greenhouse gas emissions have spurred the search for clean energy sources such as hydrogen (H2) fuel cells. Metal-organic frameworks (MOFs) are promising H2 adsorption and storage media, but knowledge of H2 dynamics and adsorption strengths in these materials is lacking. Variable-temperature (VT) (2)H solid-state NMR (SSNMR) experiments targeting (2)H2 gas (i.e., D2) shed light on D2 adsorption and dynamics within six representative MOFs: UiO-66, M-MOF-74 (M = Zn, Mg, Ni), and α-M3(COOH)6 (M = Mg, Zn). D2 binding is relatively strong in Mg-MOF-74, Ni-MOF-74, α-Mg3(COOH)6, and α-Zn3(COOH)6, giving rise to broad (2)H SSNMR powder patterns. In contrast, D2 adsorption is weaker in UiO-66 and Zn-MOF-74, as evidenced by the narrow (2)H resonances that correspond to rapid reorientation of the D2 molecules. Employing (2)H SSNMR experiments in this fashion holds great promise for the correlation of MOF structural features and functional groups/metal centers to H2 dynamics and host-guest interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".