Analysis of Redox Reactions on Pt-Sn based Nano-catalysts for Direct Methanol Fuel Cell Applications
Bibliographic record
Abstract
Low content Pt based catalysts (Pt-Mx, Mx: SnO2, Sn) were prepared by microwave assisted-thermal synthesis. Pt-SnO2/C catalyst showed good performance for methanol oxidation reaction. Besides, Pt1Sn1/C showed good performance for catalyzing the oxygen reduction reaction. The catalysts were characterized structurally by X-ray diffraction and transmission electron microscopy techniques. It was possible to observe the presence of nanoparticles obtained by the synthesis method used in this work. The chemical composition of every material was determined by energy dispersive spectroscopy analysis. The electrochemical characterization of the electrocatalytic materials was carried out in acid medium by cyclic voltammetry and rotating disk electrode techniques. Pt-SnO2/C and Pt1Sn1/C were compared with commercial PtRu/C and Pt/C catalysts respectively. Pt-SnO2/C showed better electrochemical characteristics than commercial PtRu/C for performing the methanol oxidation reaction (MOR). Pt1Sn1/C showed an exchange current density two orders of magnitude higher than commercial Pt/C for performing the oxygen reduction reaction (ORR). The materials were evaluated in an experimental direct methanol fuel cell (DMFC) operating during 10 hours. The electric power density loss showed by the DMFC made with commercial catalysts (PtRu/C and Pt/C) was 67% while the DMFC made with Pt-SnO2/C and Pt1Sn1/C showed an electric power loss ca. 16%. It means that the catalysts synthesized in this work can be considered as good candidates for experimental 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".