Magnetically Enhanced Oxygen Evolution Reaction in Mild Alkaline Electrolytes by Building Catalysts on Magnetic Frame
Bibliographic record
Abstract
Abstract Under large current densities, the excessive hydroxide ion (OH) consumption hampers alkaline water splitting involving the oxygen evolution reaction (OER). High OH concentration (≈30 wt.%) is often used to enhance the catalytic activity of OER, but it also leads to higher corrosion in practical systems. To achieve higher catalytic activity in low OH concentration, catalysts on magnetic frame (CMF) are built to utilize the local magnetic convection induced from the host frame's magnetic field distributions. This way, a higher reaction rate can be achieved in relatively lower OH concentrations. A CMF model system with catalytically active CoFeO x nanograins grown on the magnetic Ni foam is demonstrated. The OER current of CoFeO x @NF receives ≈90% enhancement under 400 mT (900 mA cm −2 at 1.65 V) compared to that in zero field, and exhibits remarkable durability over 120 h. As a demonstration, the water‐splitting performance sees a maximum 45% magnetic enhancement under 400 mT in 1 m KOH (700 mA cm −2 at 2.4 V), equivalent to the concentration enhancement of the same electrode in a more corrosive 2 m KOH electrolyte. Therefore, the catalyst‐on‐magnetic‐frame strategy can make efficient use of the catalysts and achieve higher catalytic activity in low OH concentration by harvesting local magnetic convection.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".