Molybdenum–Based Electrocatalysts for Direct Alcohol Fuel Cells: A Critical Review
Bibliographic record
Abstract
Direct alcohol fuel cells (DAFCs) have gained much attention as promising energy conversion devices due to their ability to utilize alcohol as a fuel source. In this regard, Molybdenum-based electrocatalysts (Mo-ECs) have emerged as a substitution for expensive Pt and Ru–based co-catalyst electrode materials in DAFCs, owing to their unique electrochemical properties useful for alcohol oxidation. The catalytic activity of Mo-ECs displays an increase in alcohol oxidation current density by several folds to 1000–2000 mA mgPt−1, compared to commercial Pt and PtRu catalysts of 10–100 mA mgPt−1. In addition, the methanol oxidation peak and onset potential have been significantly reduced by 100–200 mV and 0.5–0.6 V, respectively. The performance of Mo-ECs in both acidic and alkaline media has shown the potential to significantly reduce the Pt loading. This review aims to provide a comprehensive overview of the bifunctional mechanism involved in the oxidation of alcohols and factors affecting the electrocatalytic oxidation of alcohol, such as synthesis method, structural properties, and catalytic support materials. Furthermore, the challenges and prospects of Mo-ECs for DAFCs anode materials are discussed. This in-depth review serves as valuable insight toward enhancing the performance and efficiency of DAFC by employing Mo-ECs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".