H<sub>2</sub> Storage Materials (22KJ/mol) Using Organometallic Ti Fragments as σ-H<sub>2</sub> Binding Sites
Bibliographic record
Abstract
Low-coordinate Ti (III) fragments with controlled geometries designed specifically for sigma-H2 binding were grafted onto mesoporous silica using tri- and tetrabenzyl Ti precursors. The hydrogen storage capacity was tested as a function of precursor and precursor loading level. At an optimal loading level of 0.2 mol equiv tetrabenzyl Ti the total storage capacity at -196 degrees C was 21.45 wt % and 34.10 kg/m(3) at 100 atm, and 3.15 wt % and 54.49 kg/m(3) for a compressed pellet under the same conditions. The adsorption value of this material was 1.66 wt %, which equates to an average of 2.7 H2 per Ti center. The adsorption isotherms did not reach saturation at 60 atm, suggesting that the theoretical maximum of 5 H2 per Ti in this system may be reached at higher pressures. The binding enthalpies rose with surface coverage to a maximum of 22.15 kJ/mol, which is more than double that of the highest recorded previously and within the range predicted for room temperature performance. The adsorption values of 0.99 at -78 degrees C and 0.69 at 25 degrees C demonstrate retention of 2.4 H2 and 1.1 H2 per Ti at these temperatures, respectively. These findings suggest that Kubas binding of H2 may be exploited at ambient temperature to enhance the storage capacities of high-pressure cylinders currently used in hydrogen test vehicles.
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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".