DFT Study of the CO Poisoning Effects on Pd<sub>x</sub>Cu<sub>1-x</sub> (110) Surface
Bibliographic record
Abstract
CO contaminants play a significant role in modifying the performance of proton exchange membrane fuel cells (PEMFC). Pt is probably the most common catalyst being used today to absorb CO in the PEMFC, yet recent studies have shown that the use of Pd alloys such as Pd-Cu can increase the fuel cell efficiency versus a pure Pt catalyst. In this work, we examine the adsorption of CO onto PdxCu1-x (110) surfaces, with different values of x, in order to improve the CO tolerance. Understanding how molecules interact with such surfaces is the first step in understanding catalytic reactions. The study here presented was done using CASTEP, a computational code based on the plane-wave pseudopotential method of functional density theory. The surface structure of PdxCu1-x (110) was optimized and then the state density-functional, the repulsion energies and the chemisorption for CO on PdxCu1-x(110) were calculated. The results indicate that chemisorption energies of CO on PdxCu1-x are highly dependent on the concentration x of the alloy. In addition, density of states analysis indicate that the poisoning effect is partially due to the loss of Pd-Cu(d) electrons upon CO adsorption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".