Magnetic Field Enhanced Oxygen Reduction Reaction via Oxygen Diffusion Speedup
Bibliographic record
Abstract
The mass-transfer of oxygen in liquid phases (including in the bulk electrolyte and near the electrode surface) is a critical step to deliver oxygen to catalyst sites (especially immersed catalyst sites) and use the full capacity of oxygen reduction reaction (ORR). Despite the extensive efforts of optimizing the complex three-phase reaction interfaces to enhance the gaseous oxygen transfer, strong limitations remain due to oxygen's poor solubility and slow diffusion in electrolytes. Herein, a magnetic method for boosting the directional hydrodynamic pumping of oxygen toward immersed catalyst sites is demonstrated which allows the ORR to reach otherwise inaccessible catalytic regions where high currents normally would have depleted oxygen. For Pt foil electrodes without forced oxygen saturation in KOH electrolytes, the mass-transfer-limited current densities can be improved by 60% under an external magnetic field of 435 mT due to the synergistic effect between bulk- and surface-magnetohydrodynamic (MHD) flows induced by Lorentz forces. The residual magnetic fields are further used at the surface of magnetic materials (such as CoPt alloys and Pt/FeCo heterostructures) to enhance the surface-MHD effect, which helps to retain part of the ORR enhancement permanently without applying external magnetic fields.
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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".