Hydride-Induced Amplification of Performance and Binding Enthalpies in Chromium Hydrazide Gels for Kubas-Type Hydrogen Storage
Bibliographic record
Abstract
Hydrogen is the ideal fuel because it contains the most energy per gram of any chemical substance and forms water as the only byproduct of consumption. However, storage still remains a formidable challenge because of the thermodynamic and kinetic issues encountered when binding hydrogen to a carrier. In this study, we demonstrate how the principal binding sites in a new class of hydrogen storage materials based on the Kubas interaction can be tuned by variation of the coordination sphere about the metal to dramatically increase the binding enthalpies and performance, while also avoiding the shortcomings of hydrides and physisorpion materials, which have dominated most research to date. This was accomplished through hydrogenation of chromium alkyl hydrazide gels, synthesized from bis(trimethylsilylmethyl) chromium and hydrazine, to form materials with low-coordinate Cr hydride centers as the principal H(2) binding sites, thus exploiting the fact that metal hydrides form stronger Kubas interactions than the corresponding metal alkyls. This led to up to a 6-fold increase in storage capacity at room temperature. The material with the highest capacity has an excess reversible storage of 3.23 wt % at 298 K and 170 bar without saturation, corresponding to 40.8 kg H(2)/m(3), comparable to the 2015 DOE system goal for volumetric density (40 kg/m(3)) at a safe operating pressure. These materials possess linear isotherms and enthalpies that rise on coverage, retain up to 100% of their adsorption capacities on warming from 77 to 298 K, and have no kinetic barrier to adsorption or desorption. In a practical system, these materials would use pressure instead of temperature as a toggle and can thus be used in compressed gas tanks, currently employed in the majority of hydrogen test vehicles, to dramatically increase the amount of hydrogen stored, and therefore range of any vehicle.
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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".