Hydrogen Adsorption in Ultramicroporous Metal-organic Frameworks Possessing Silent Open Metal Sites
Bibliographic record
Abstract
Herein, we report the use of an ultramicroporous (pore size <0.7 nm) metal-organic framework (MOF), [Ni3(pzdc)2(ade)2(H2O)4]2.18H2O (H3pzdc: pyrazole-3,5-dicarboxylic acid, ade: adenine), for hydrogen (H2) adsorption. Upon activation, [Ni3(pzdc)2(ade)2] was generated, and in situ carbon monoxide loading transmission infrared spectroscopy revealed that open Ni(II) sites could be generated. The MOF displayed a Brunauer-Emmett-Teller (BET) surface area of 160 m2/g. Hydrogen adsorption collected on this MOF at 77 K revealed a steep uptake at low pressure, and H2 uptake saturation was achieved at 0.15 bar. The affinity of this MOF for H2 is 9.7 1.0 kJ/mol. An interplay of in situ H2 loading experiments and computations confirmed that H2 does not bind to the open Ni(II) sites of the MOF, and the observed high affinity of the MOF for H2 is mainly attributed to its narrow pore size. To shed light on the impact of ultramicropores on H2 uptake, we experimentally compared the H2 uptake per surface area unit as a function of the pore size of other ultramicroporous, microporous, and mesoporous MOFs. Our results showcase that ultramicropores contribute the most to H2 uptake, and the size, shape, and functionality of our MOF are ideal and can be used as guiding principles for the design and synthesis of novel adsorbents for efficient H2 storage and delivery.
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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) | 0.000 | 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 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".