Plasma Synthesized Trilayered Rhodium−Platinum−Tin Oxide Nanostructures with Enhanced Tolerance to CO Poisoning and High Electroactivity for Ethanol Oxidation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The future of fuel cells technology will require porous or very organized multicomponent catalytic layers that can be prepared by thin film growth methods. Reducing the cost of these energy systems will also necessitate that the catalytic layers be binderless and contain low amount of the noble catalyst until efficient non‐noble catalysts are discovered. To address these requirements, monolayered SnO 2 , Pt and Rh, bi‐layered Pt/SnO 2 and Rh/Pt and novel tri‐layered Rh (various thicknesses)/Pt/SnO 2 catalysts supported on carbon paper are synthesized at room temperature via pulsed laser deposition. The catalysts are evaluated for their catalytic performance for the ethanol oxidation reaction (EOR), durability, and tolerance to CO‐poisoning. All the Rh/Pt/SnO 2 catalysts produce high CO‐tolerance, high EOR catalytic activity and durability as compared to pure Pt. The possible mechanism by which SnO 2 and Rh atoms enhanced the performance is also considered herein, and an optimal Rh/Pt/SnO 2 structure having a 10 nm thickness of Rh layer offers a promising anode catalyst for ethanol fuel cells. Notably, the onset potential for CO oxidation is extraordinarily 430 mV lower than on Pt, and the mass activity for EOR and durability are 2.25 and 4.2 times higher than on Pt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it