How the hydrogen sorption properties of palladium are modified through interaction with iridium
Bibliographic record
Abstract
Hydrogen sorption (adsorption/absorption) in metals, in the form of thin films or nanoparticles, is a key process in the fields of energy storage and heterogeneous catalysis. Atomic hydrogen dissolved in the subsurface of a metal affects its surface atomic and electronic structures, and thereby its surface reactivity and catalytic properties. In addition, alloy effects modify both catalytic and hydrogen sorption phenomena. In order to rationalize recent experimental results showing the negative impact of hydrogen absorption on catalysis, the present article proposes an insight into structure-reactivity relationships through computational simulations, using density functional theory, of hydrogen sorption in the near-surface region of palladium atomic layers interacting with an iridium substrate. A detailed analysis of the electronic structure using local projected densities of states (PDOS) and crystal orbital overlap population (COOP) curves was carried out. It is found that the Pd/Ir system, with respect to pure Pd surfaces, keeps acceptable adsorption properties for surface reactions while preventing hydrogen penetration. The results of electronic structure calculations show that the most important difference between Pd and Ir is related to the strong anti-bonding character of the 1s-H/5p-Ir interaction, leading to the non-bonding character of the sp-Ir interaction with hydrogen. Thus, increasing the Ir concentration in a Pd-based system increases the anti-bonding contribution, which strongly weakens the overall metal-hydrogen interaction.
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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".