Quantification of the Effect of an External Magnetic Field on Water Oxidation with Cobalt Oxide Anodes
Bibliographic record
Abstract
Here, we quantify the effect of an external magnetic field (β) on the oxygen evolution reaction (OER) for a cobalt oxide|fluorine-doped tin oxide coated glass (CoO x |FTO) anode. A bespoke apparatus enables us to precisely determine the relationship between magnetic flux density (β) and OER activity at the surface of a CoO x |FTO anode. The apparatus includes a strong NdFeB magnet ( β max = 450 ± 1 mT) capable of producing a magnetic field of 371 ± 1 mT at the surface of the anode. The distance between the magnet and the anode surface is controlled by a linear actuator, enabling submillimeter distance positioning of the magnet relative to the anode surface. We couple this apparatus with a finite element analysis magnetic model that was validated by Hall probe measurements to determine the value of β at the anode surface. At the largest tested magnetic field strength of β = 371 ± 1 mT, a 4.7% increase in current at 1.5 V vs the normal hydrogen electrode (NHE) and a change in the Tafel slope of 14.5 mV/dec were observed. We demonstrate through a series of OER measurements at sequential values of β that the enhancement consists of two distinct regions. The possible use of this effect to improve the energy efficiency of commercial water electrolyzers is discussed, and major challenges pertaining to the accurate measurement of the phenomenon are demonstrated.
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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 | 0.001 |
| 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".