Intercalated nanosized MO2 (M: Sn, Ce) layers between CNTs and Pt or PtSn nanoparticles catalysts
Bibliographic record
Abstract
Direct ethanol fuel cells (DEFCs) is receiving enormous attention as alternative electrical energy conversion systems. This paper gives an outline on some recent advances achieved in our laboratory regarding the development of high performing anode for ethanol oxidation. We developed multi-components binderless hierarchically organized layer onto layer nanostructured catalysts comprising a carbon paper (CP, current collector)/carbon nanotubes (CNTs, conductivity enhancer)/catalyst promoter (MOx, M: Sn; Ce)/Pt-based (electrocatalyst). The main focus was how to lower the onset oxidation potential (OOP) of ethanol at Pt75Sn25 catalyst. Towards that aim, metal oxides such as CeO2 and SnO2 were sought as catalyst promoters. It has been discovered that intercalating a nanostructured layer of SnO2 between CNTs and Pt75Sn25 considerably lowered the OOP of ethanol and also increased the specific mass activity (SMA) at low potentials. Indeed, the OOP at the CP/CNT/SnO2/Pt75Sn25 was 210 mV and 117 mV negative relative to that delivered by CP/CNT/Pt and CP/CNT/Pt75Sn25, respectively confirming by that the promoting effect of SnO2 of the oxidation of CO at low potentials. The SMA determined at slow potential scan rate of 5 mV/s at 0.4 V vs. Ag/AgCl revealed that CP/CNT/SnO2/Pt75Sn25 delivered an SMA of 1.2 times higher than that of the CP/CNT/Pt75Sn25 catalyst and 1.5 times greater than the one exhibited by the CP/CNT/CeO2/Pt75Sn25 catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".