Recent Developments of Methanol Electrooxidation Using Nickel‐based Nanocatalysts
Bibliographic record
Abstract
Abstract Studies of methanol electrooxidation reactions have achieved considerable advancements in the recent years for their encouraging contributions in the exciting fields of energy conversion and storage, organic syntheses, wastewater treatment, sensing, medicinal and environmental analyses, and many others thriving fields of modern technology. Accordingly, the fabrication of less expensive, efficient and decent quality electrocatalysts with high material stability, faster electro‐oxidation kinetics, as well as less carbonation and decent corrosion inhibition activities are highly urged. Consequently, scientists around the globe are in continuous search for alternative economical smart anode‐nanocatalysts for methanol oxidations with superior electrochemical performances. In the recent years, various inexpensive and readily‐available nanocatalysts of non‐noble metals like Ni, Co, Cu, etc., in reference to electrooxidation of organic molecules have been reported. The present work highlights the recent progress accomplished by rapidly flourishing nickel‐based electrocatalysts for electrooxidation of methanol. The discussion comprehensively includes the basic mechanistic understandings and fundamentals for achieving high efficacy of methanol electrooxidation with nickel‐based electrocatalysts. Also, the current challenges faced in this emerging area have been outlined to achieve superior, productive, and commercially viable catalysts for methanol electrooxidation in the near future.
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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 | 0.000 |
| 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 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".