Hydrogen Electrosorption into Pd−Cd Nanostructures
Bibliographic record
Abstract
Hydrogen-absorbing materials are crucial for both the purification and storage of hydrogen. Pd and Pd-based alloys have been studied extensively for their use as both hydrogen dissociation catalysts and hydrogen selective membrane materials. It is known that incorporating metal atoms of different sizes into the Pd lattice has a major impact on the hydrogen absorption process. In this paper, hydrogen electrosorption into nanostructured Pd-Cd alloys has been studied for different compositions of Cd that varied from 0 to 15 at. %. The low cost of Cd makes it an attractive material to combine with Pd for hydrogen sorption. A combination of chronoamperometry and cyclic voltammetric experiments was used to determine the ratio of the H/(Pd + Cd) and the kinetics of hydrogen sorption into these Pd-Cd alloys at different potentials. It was found that the maximum H/(Pd + Cd) value was 0.66 for pure Pd, and this decreased with increasing the amount of Cd. Also, the alpha (solid solution) to beta phase (metal hydride) hydrogen transition was determined to be the slowest step in the absorption process and was practically eliminated when an optimum amount of Cd atoms was doped (i.e., Pd-Cd(15%)). With increasing the amount of Cd, more hydrogen was absorbed into the Pd-Cd nanostructures at the higher potentials (the alpha phase region). The faster kinetics, along with the decrease in the phase transition of hydrogen sorption into the Pd-Cd nanostructures when compared to pure Pd, makes the Pd-Cd nanostructures attractive for use as a hydrogen dissociation catalytic capping layer for other metal hydrides or as a hydrogen selective membrane.
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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".