Theoretical investigation of the use of doped graphene as a membrane support for effective CO removal in hydrogen fuel cells
Bibliographic record
Abstract
Carbon monoxide poisoning of the anode catalyst is currently a big problem facing the use of hydrogen fuel cells. This study uses density functional theory to model the interaction between a filter membrane and carbon monoxide to optimise the removal of CO from the H2 feed gas. The membranes studied are graphene/metal surfaces of nickel, platinum and iridium/gold over undoped or boron-, nitrogen- or oxygen-doped graphene. It was found that graphene doping improved the efficiency of the filter membrane in hydrogen fuel cells because addition of a dopant increases metal/graphene binding and causes metal/H2 binding to become negligible while only decreasing metal/CO binding slightly. Platinum and iridium/gold systems show slightly stronger binding to graphene and CO than nickel systems. However, nickel is a non-precious metal, so membranes produced with this active centre could lead to a reduction in the cost of fuel cell production by increasing the lifetime of the platinum anode catalyst.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".