Photoassisted Deposition of Palladium Nanoparticles on Carbon Nitride for Efficient Oxygen Reduction
Bibliographic record
Abstract
One of the major challenges associated with fuel cells is the design of highly efficient electrocatalysts to reduce the high overpotential of the oxygen reduction reaction (ORR). In this study, a facile photoassisted method was employed for the direct deposition of palladium nanoparticles (Pd NPs) onto graphitic carbon nitride (g-C 3 N 4 ) for efficient oxygen reduction. The fabricated catalysts were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and Fourier transmission infrared (FTIR) spectroscopy. The electrocatalytic activity of the prepared catalysts was examined using cyclic voltammetry (CV), linear scan voltammetry (LSV), and chronoamperometry (CA). Our studies have shown that the synthesized Pd-g-C 3 N 4 nanocomposite exhibited robust electrocatalytic behavior toward the ORR in 0.1 M KOH solution, where the desirable four-electron pathway was achieved. This enhanced ORR activity might be attributed to synergetic effects between g-C 3 N 4 and Pd as well as the even dispersion of the small Pd NPs. In addition, the developed catalyst demonstrated a significantly improved tolerance against methanol as well as enhanced stability in comparison to the benchmark commercial platinum-loaded carbon catalysts.
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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".