Room Temperature Synthesis of Mixed Platinum and Tin Oxide Nanocomposite Catalyst with Enhanced Mass Activity and Durability for Ethanol Electrooxidation in an Acidic Medium
Bibliographic record
Abstract
Pt-transition metal oxides are emerging as new class of electrodes for alcohol oxidation reactions. We prepare at room temperature Pt-SnO 2 nanocomposites of various surface morphologies onto carbon nanotubes for enhanced ethanol oxidation reaction (EOR) by cross-beam pulsed laser deposition technique. Synthesized nanocomposites are characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), XRD, Raman spectroscopy, and transmission electron microscopy (TEM). It is observed that the interaction of SnO 2 with Pt modifies the electronic structure of the latter inducing the formation of ionized Pt 2+ and Pt 4+ while Sn revealed mixed Sn 4+ and Sn 2+ cations. An optimum electrocatalytic performance toward EOR is obtained with a Pt-SnO 2 grown under 0.5 Torr of He atmosphere. Versus a CNT/Pt electrode, this CNT/Pt-SnO 2 electrode (i) oxidizes ethanol at much lower potentials (86 mV negative), and (ii) displays a superior specific mass activity of 1.6 times and 2.2 times by cyclic voltammetry and long-term stability, respectively. Such performance is ascribed to a combination of: (i) SnO 2 has abundant hydroxyls on the surface and decrease the CO poisoning based on a bifunctional mechanism; (ii) modification of the electronic structure of Pt (electronic effect) and (iii) a semi-porous surface morphology favorable for the mass transfer of ethanol molecules.
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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".