Pt-SnO<sub>2</sub>−Pd/C Electrocatalyst with Enhanced Activity and Durability for the Oxygen Reduction Reaction at Low Pt Loading: The Effect of Carbon Support Type and Activation
Bibliographic record
Abstract
A simple-to-use method of carbon support surface activation using a PdCl 2 −SnCl 2 solution (referred to as the Shipley solution) was developed, for improving the activity and durability of low Pt loading (0.1 mg cm −2 ) catalysts toward the oxygen reduction reaction (ORR). Three types of carbon supports were investigated: Denka, Graphitized Carbon (GC) and Vulcan XC-72R. Pt nanoparticles were synthesized by a modified polyol process and adsorbed on Shipley solution treated carbon supports. The resulting catalyst composition was Pt-SnO 2 −Pd/C with beneficial atomic ratios of Pt/Sn ≥ 12 and Sn/Pd ≥ 2. Employing extensive potential cycling as accelerated degradation testing, we found that the Pt-SnO 2 −Pd/C catalysts maintained excellent electrocatalytic activity for ORR with respect to control samples without carbon support treatment (both commercial and in-house prepared). The Pt area-specific oxygen reduction current density at 0.9 V RHE and 25 °C was most improved in the case of the GC support. The activity of commercial Pt/GC after stability testing decreased by 21.5% to 0.055 A m −2 Pt while for Pt-SnO 2 −Pd/GC, 0.078 A m −2 Pt was measured, identical to that of the fresh catalyst. The electrochemical results are supported by detailed surface analysis. Pt−Pd bimetallic nanoclusters were identified, and the kinetically beneficial role of the partially reduced SnO 2 surface for oxygen and water adsorption is discussed. The oxygen mass transfer limiting current densities in the porous rotating disk electrodes (PRDE) were qualitatively correlated with changes of the catalyst layer morphology during stability testing in terms of the Bonnecaze and Ahlberg models.
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 | 0.000 |
| 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".