A Palladium based Composite Electrocatalyst of Pd/SnO2-CSS Showing an Excellent Electrocatalytic Activity for Ethanol Oxidation Reaction (EOR)
Bibliographic record
Abstract
In this work, for the first time, carbon solid spheres decorated with tin dioxide particles (denoted as SnO2-CSS) were prepared through a hydrothermal process assisted calcination method, and then, the prepared SnO2-CSS reacted with PdOꞏH2O hydrothermally producing the final palladium (Pd) based composite electrocatalysts (abbreviated as Pd/SnO2-CSS).In the whole preparation process, besides glucose and tin oxides (SnO2 and/or SnO) and PdOꞏH2O, no other substances were involved.The Pd/SnO2-CSS catalysts prepared in the presence of SnO2, SnO and SnO+SnO2 were, respectively, classified as catalyst a, b and c.The surface morphologies of all obtained samples were mainly characterized by SEM, revealing that the surface of SnO2-CSS were decorated by some irregular particles and a large number of nanoparticles were uniformly anchored on the surfaces of Pd/SnO2-CSS catalysts.The chemical compositions of all prepared catalysts were analyzed by XRD and XPS, indicating that SnO2, PdO, metallic Pd and carbon material were the main substances of all prepared catalysts.Most importantly, all prepared catalysts, as demonstrated by the CV (cyclic voltammetry) and CA (chronoamperometry) results, delivered an obvious electrocatalytic activity towards EOR.Particularly, in the CV test, the peak current density of the forward peak of EOR on catalyst b was, respectively, about 2.9 and 4.5 times higher than that on catalyst c and a.It was worth mentioning that in the CA test the polarized current density of EOR on catalyst b was still kept to be as high as 7.9 mA cm -2 even after 7200 s, substantially indicating that catalyst b was a promising electrocatalyst for the EOR based 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".