Synthesis of low‐cost <scp>Co‐Sn‐Pd</scp>/<scp>rGO</scp> catalysts via ultrasonic irradiation and their electrocatalytic activities toward oxygen reduction reaction
Bibliographic record
Abstract
Abstract Reduced graphene supported Co‐Sn‐Pd nanoparticle catalysts(Co0.2SnxPdy/rGO, x + y = 0.4) were successfully synthesized by reducing the trace amounts of ions (Pd2+, Sn2+, and Co2+) in the presence of reduced graphene via an ultrasonic irradiation method. Characterization of the Co0.2SnxPdy/rGO catalysts by X‐ray diffraction (XRD) and transmission electron microscopy (TEM) indicates that the Co0.2Sn0.2Pd0.2/rGO catalyst with a mean diameter of ~3.8 nm is close to a single‐phase solid solution. X‐ray photoelectron spectroscopy (XPS) shows the binding energy of metallic Pd shifts to a higher angle in the Co0.2SnxPdy/rGO (x + y = 0.4) catalysts, indicating a shift of Pd electronic centre upon alloying with Co and Sn. Conventionally, ternary Co‐Sn‐Pd/rGO catalysts show greater oxygen reduction reaction (ORR) activities and durability than binary Pd‐Co/rGO catalyst and 20 wt% Pt/C catalyst even at a very low Pd content. Among of the synthetic catalysts, the Co0.2Sn0.2Pd0.2/rGO catalyst exhibits the best ORR activity (E1/2 = 0.91 V, k = −57 mV · dec−1) and dominates a 4‐electron pathway in the ORR process (n = 3.95 ± 0.09, H2O2<4%). Also, the Co0.2SnxPdy/rGO (x + y = 0.4) catalysts display a remarkable advantage of high methanol tolerance compared to that of the commercial 20 wt% Pt/C catalyst, which make them potential candidates for direct methanol fuel cells.
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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".