The Effect of Airborne Contaminants on Fuel Cell Performance and Durability
Bibliographic record
Abstract
The impact of contaminants on fuel cell performance was examined to document air filter specifications (prevention) and devise recovery procedures (maintenance) that are effective at the system level. Eight previously undocumented airborne contaminants were selected for detailed studies and characterization data was used to identify operating conditions that intensifying contamination effects. The use of many and complementary electrochemical, chemical and physical characterization methods and the derivation of several mathematical models supported the formulation of contamination mechanisms and the development of recovery procedures. The complexity of these contamination mechanisms suggests a shift to prevention and generic maintenance measures. Only two of the selected contaminants led to cell voltage losses after injection was interrupted. Proposed recovery procedures for calcium ions, a component of road de-icers, dessicants, fertilizers and soil conditioners, were either ineffective or partly effective, whereas for bromomethane, a fumigant, the cell voltage was recovered to its initial value before contamination by manipulating and sequencing operating conditions. However, implementation for a fuel cell stack and system remains to be demonstrated. Contamination mechanisms also led to the identification of membrane durability stressors. All 8 selected contaminants promote the formation of hydrogen peroxide, a known agent that can produce radicals that attack the ionomer and membrane molecular structure whereas the dehydrating effect of calcium ions on the ionomer and membrane increases their brittleness and favors the creation of pinholes under mechanical stresses. Data related to acetylene, acetonitrile and calcium ions are emphasized in the report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".