A highly active, stable and synergistic Pt nanoparticles/Mo2C nanotube catalyst for methanol electro-oxidation
Bibliographic record
Abstract
Poor electrocatalytic activity and carbon monoxide (CO) poisoning of the anode in Pt-based catalysts are still two major challenges facing direct methanol fuel cells. Herein, we demonstrate a highly active and stable Pt nanoparticle/Mo2C nanotube catalyst for methanol electro-oxidation. Pt nanoparticles were deposited on Mo2C nanotubes using a controllable atomic layer deposition (ALD) technique. This catalyst showed much higher catalytic activity for methanol oxidation and superior CO tolerance, when compared with those of the conventional Pt/C and PtRu/C catalysts. The experimental evidence from X-ray absorption near-edge structure spectroscopy and scanning transmission X-ray microscopy clearly support a strong chemical interaction between the Pt nanoparticles and Mo2C nanotubes. Our studies show that the existence of Mo2C not only minimizes the required Pt usage but also significantly enhances CO tolerance and thus improves their durability. These results provide a promising strategy for the design of highly active next-generation catalysts. Platinum nanoparticles on Mo2C nanotubesact as a stable, highly active catalyst for methanol electro-oxidation, find a binational team led by Chunwen Sun from Institute of Physics, Chinese Academy of Sciences. Methanol electro-oxidation is a critical reaction in direct methanol fuel cells, but conventional methods for catalysing it using Pt-based catalysts loaded on carbon suffer from low activities and CO poisoning of the anode. Now, researchers in China and Canada have discovered that a catalyst produced by depositing Pt nanoparticles on Mo2C nanotubes by controlled atomic layer deposition can overcome both problems. Based on X-ray spectroscopy and microscopy measurements, they attribute this to synergistic effects between the two components. Their results reveal that the presence of Mo2C both reduces the amount of Pt needed (thus lowering costs) and enhances CO tolerance (thereby improving durability), indicating that it is a promising strategy for designing highly active next-generation catalysts. In this paper, we demonstrate a highly active and stable Pt nanoparticle/Mo2C nanotube catalyst for methanol electro-oxidation. Well-dispersed Pt nanoparticles were deposited on Mo2C nanotubes using a controllable atomic layer deposition (ALD) technique. This catalyst showed much higher catalytic activity for methanol oxidation and superior CO tolerance, when compared with those of the conventional Pt/C and PtRu/C catalysts. These results provide a promising strategy for the design of highly active next-generation catalysts.
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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 | 0.001 | 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 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".