High-Performance Pd-Based Hydrogen Spillover Catalysts for Hydrogen Storage
Bibliographic record
Abstract
High surface area carbon materials are promising for low-temperature storage of hydrogen by physisorption. To achieve acceptable hydrogen capacities at ambient temperature, chemisorption must come into play. The dispersion of transition metal catalysts to carbon materials can enhance the ambient temperature adsorption capacity of the carbon materials via the hydrogen spillover mechanism. In this study, three different hydrogen dissociation catalysts (Pd, PdAg, and PdCd nanoparticles) were dispersed onto surfaces of activated carbon. The surface composition of these metal-dispersed carbon materials was analyzed using X-ray photoelectron spectroscopy (XPS) and the specific surface areas, and pore sizes were measured using N 2 adsorption/desorption. The effect of the dispersed catalysts on the hydrogen adsorption properties of the activated carbon was systemically investigated at 77 K and room temperature (295 K) using a volumetric gas adsorption technique. At 77 K, the catalysts have no effect, and the hydrogen capacity of the materials is strictly related to the specific surface area. At room temperature, hydrogen spillover was observed from the catalysts to the carbon material. The hydrogen capacity is related to the adsorption strength of hydrogen atoms to the catalyst particle surface atoms, which was verified with DFT calculations. In addition, this study reveals that the PdCd nanoparticle possesses much higher hydrogen spillover enhancement (108%) than the pure Pd and PdAg nanoparticles, promising for hydrogen storage.
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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".