An Investigation of the Adverse Effect of TiO<sub>2</sub> on Pt-Catalyst for the Oxygen Reduction Reaction
Bibliographic record
Abstract
Northeastern University Center for Renewable Energy Technology (NUCRET), 317 Egan Center, Northeastern University, 360 Huntington Avenue, Boston, MA 02115 Metal oxides have long been used to protect carbon supports from corrosion on the anode side of a fuel cell during fuel starvation, when the metal oxides serve as a catalyst for water hydrolysis it protects the carbon supports from corrosion. Titanium dioxide (TiO2) is commonly used on the anode, and known to be effective at protecting the carbon from corrosion. However, we recently found that mechanically mixing Pt/C with some TiO2 as the anode catalyst of a PEMFC leads to a significant drop of the cell performance at a very early stage of operation. Subsequent efforts to understand the phenomenon led to the finding that the even a small amount of TiO2 seriously poison the Pt/C catalyst for the oxygen reduction reaction (ORR) by reducing the half wave potential by more than 150 mV (Figure 1), whereas the HOR rate was not significantly affected. By combing ex situ and in situ characterizations and detailed electrochemical testing, we provide solid experimental evidence for the unexpected TiOx–induced poisoning effect, which largely accounts for the activity loss. The new findings of the interactions between the Pt/C and the metal oxide help to pursue metal oxides that are better at protecting the carbon from corrosion while minimizing the poisoning effects. Acknowledgement: The authors gratefully acknowledge the financial support of Automotive Fuel Cell Corporation (AFCC), Canada and Ford Motor Co., Detroit, MI (under a URIP program). The authors also acknowledge instrumental support from Thermo Fisher Corp., and access to synchrotron based facility for x-ray absorption spectroscopy at National Synchrotron Light Source-II (NSLS-II) situated in Brookhaven National Laboratory (BNL), Upton, NY, under grant # DE-SC0012704 Figure 1
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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 | 0.001 | 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".