Density Functional Theory Analysis of Metal/Graphene Systems As a Filter Membrane to Prevent CO Poisoning in Hydrogen Fuel Cells
Bibliographic record
Abstract
Hydrogen fuel cells are a very promising potential replacement for internal combustion engines. However, their current use is limited by carbon monoxide poisoning of the platinum anode catalyst that occurs when CO enters the cell in the H 2 feed gas. A novel new solution to this problem is the addition of a metal/graphene filter membrane exterior to the cell. This membrane will remove CO from the feed gas, allowing reduced loading of the expensive Pt catalyst and increasing cell lifetime. In the current work, density functional theory (DFT) was used to analyze graphene membranes containing nickel, copper, platinum, and iridium/gold atoms. The binding energy of the metal to the graphene was measured for a lone system and in the presence of CO and H 2 to predict its durability. The binding energy of CO and H 2 to metal was also measured to estimate its efficiency. All systems were analyzed using natural bond orbitals (NBOs). It was found that copper is a poor choice for use in membranes in all respects. Nickel systems show the most promise: they have a consistent metal/graphene binding energy when feed gas molecules are introduced. In addition, although CO binding is strong to Ni, Pt, and Ir/Au, nickel systems show the weakest interaction with H 2 . NBO analysis of these systems shows that metal orbitals are the most involved in bonding.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".