Exploiting the Kubas Interaction in the Design of Hydrogen Storage Materials
Bibliographic record
Abstract
Abstract Hydrogen adsorption and storage using solid‐state materials is an area of much current research interest, and one of the major stumbling blocks in realizing the hydrogen economy. However, no material yet researched comes close to reaching the DOE 2015 targets of 9 wt% and 80 kg m −3 at this time. To increase the physisorption capacities of these materials, the heats of adsorption must be increased to ∼20 kJ mol −1 . This can be accomplished by optimizing the material structure, creating more active species on the surface, or improving the interaction of the surface with hydrogen. The main focus of this progress report are recent advances in physisorption materials exhibiting higher heats of adsorption and better hydrogen adsorption at room temperature based on exploiting the Kubas model for hydrogen binding: (η 2 ‐H 2 )–metal interaction. Both computational approaches and synthetic achievements will be discussed. Materials exploiting the Kubas interaction represent a median on the continuum between metal hydrides and physisorption materials, and are becoming increasingly important as researchers learn more about their applications to hydrogen storage problems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".