Doped Ceria Nanoparticles with Reduced Solubility and Improved Peroxide Decomposition Activity for PEM Fuel Cells
Bibliographic record
Abstract
Ceria nanoparticles (NPs) have unique catalytic properties which make them suited to scavenge degrading radical species and their precursor peroxides during PEM fuel cell operation. However, in the acidic environment of the fuel cell, ceria dissolves and the resulting cations migrate within the MEA, causing performance and durability losses. In this work, ex situ testing was used to evaluate the peroxide decomposition, selectivity towards radical generation, and solubility of Gd, Pr, and Zr-doped ceria NPs over a range of crystallite sizes and dopant levels. These doped materials exhibit better peroxide scavenging activity and dissolution resistance than undoped ceria. In these materials, activity is largely governed by increased surface area due to high internal porosity at smaller crystallite sizes compared to undoped ceria. Of the compounds tested, ceria NPs doped with 15 at% Zr (10 nm) and 5 at% Pr (17 nm) exhibited greater dissolution resistance than undoped ceria. Stabilization of the former doped NPs is attributed to crystallite agglomeration, while the increased stability of the latter is proposed to be due to its internally-porous, mesoscale structure suggested by its sorption isotherm. Both materials are more dissolution-resistant and active peroxide decomposers compared to undoped ceria but exhibit increased byproduct radical generation.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".