Tip‐Enhanced Oxygen Reduction on Pt@Ni Cones via Concentrated Electric and Magnetic Fields
Bibliographic record
Abstract
Abstract Efficient oxygen reduction reaction (ORR) is crucial for energy conversion technologies, yet its sluggish kinetics remain a significant challenge. Beyond optimizing the catalyst's intrinsic properties, field effects can also profoundly impact the microenvironment at the catalyst/electrolyte interface, thereby affecting electrocatalytic performance. This study explores the tip‐enhanced ORR by leveraging the coupled effects of electric and magnetic fields at the reaction interface. The Pt‐loaded ferromagnetic Ni cone electrode generates strong localized electric fields that reorganize interfacial water molecules into a more ordered structure. This strengthens hydrogen bonding between the electrolyte and reaction intermediates, which in turn facilitates proton‐coupled electron transfer and accelerates the rate‐limiting *OH desorption into interfacial water matrix. Additionally, the amplified electric fields at tips accelerate OH − electromigration away from the catalyst surface, while the migration current couples with the concentrated magnetic fields near tips to drive strong magnetohydrodynamic flows. These intensified flows effectively enhance O 2 molecule delivery to wider electrode surface and facilitate better OH − product removal, thus improving the overall reaction efficiency. This strategy of concentrating both E/M fields using the field‐effect catalyst optimizes the ORR kinetics at the interface as well as the mass transport dynamics near interface, offering an effective approach for advancing ORR performance.
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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".