Thermal Treatment Effects on Pd<sub>5</sub>Cu<sub>4</sub>Pt Electrocatalyst for the Oxygen Reduction Reaction in a PEM Fuel Cell
Bibliographic record
Abstract
The research is aimed to study the thermal treatment effects in the electrochemical activity of Pd5Cu4Pt electrocatalyst for the oxygen reduction reaction (ORR) in acid medium as well as on its performance as cathode electrode in a single Proton Exchange Membrane Fuel Cell (PEMFC). The electrocatalyst is synthesized by chemical reduction of PdCl2, CuCl2 and H2PtCl6 with NaBH4 in THF, and is characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Cyclic voltammetry (CV) and rotating disc electrode (RDE) are performed for electrochemical characterization in a 0.5 M H2SO4 at 25 ºC. Results of thermal treatment at 200 and 300 ºC in H2 atmosphere show a growth of nanocrystallyte particles and an enhancement of the crystallinity of the electrocatalyst. Shifts towards positive 2 θ XRD values are associated to the incorporation of elements inside the crystalline structure of the sample. Electrochemical results show a decrease in the electrocatalytic activity as the temperature of the thermal treatment increases. The maximum power density, Wmax of 350 mW cm-2 is achieved using Pd5Cu4Pt without thermal treatment with 0.8 mg cm-2 cathode electrocatalyst loading of the PEMFC. This result is attributed to the formation of new inactive-ORR phases on the electrocatalyst with the thermal treatment.
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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".