Fe Stabilization by Intermetallic L1<sub>0</sub>-FePt and Pt Catalysis Enhancement in L1<sub>0</sub>-FePt/Pt Nanoparticles for Efficient Oxygen Reduction Reaction in Fuel Cells
Bibliographic record
Abstract
We report in this article a detailed study on how to stabilize a first-row transition metal (M) in an intermetallic L1 0 -MPt alloy nanoparticle (NP) structure and how to surround the L1 0 -MPt with an atomic layer of Pt to enhance the electrocatalysis of Pt for oxygen reduction reaction (ORR) in fuel cell operation conditions. Using 8 nm FePt NPs as an example, we demonstrate that Fe can be stabilized more efficiently in a core/shell structured L1 0 -FePt/Pt with a 5 Å Pt shell. The presence of Fe in the alloy core induces the desired compression of the thin Pt shell, especially the two atomic layers of Pt shell, further improving the ORR catalysis. This leads to much enhanced Pt catalysis for ORR in 0.1 M HClO 4 solution (at both room temperature and 60 °C) and in the membrane electrode assembly (MEA) at 80 °C. The L1 0 -FePt/Pt catalyst has a mass activity of 0.7 A/mg Pt from the half-cell ORR test and shows no obvious mass activity loss after 30 000 potential cycles between 0.6 and 0.95 V at 80 °C in the MEA, meeting the DOE 2020 target (<40% loss in mass activity). We are extending the concept and preparing other L1 0 -MPt/Pt NPs, such as L1 0 -CoPt/Pt NPs, with reduced NP size as a highly efficient ORR catalyst for automotive fuel cell applications.
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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) | 0.001 | 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".