journal article·2025-09

Tip‐Enhanced Oxygen Reduction on Pt@Ni Cones via Concentrated Electric and Magnetic Fields

Guojun Han, Mohamed A. Tamerd, Ling Gao, Qingsheng Guo, Cong Feng, Menghao Yang, Guoxing Miao, Jing Fu

Small Methods· Volume 9· Issue 9
Publisher page

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Electrocatalysts for Energy Conversion · Advanced battery technologies research · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Materials Science and Engineering Tongji University Shanghai 201804 P. R. China

Institute for Quantum Computing Department of Electrical and Computer Engineering University of Waterloo Ontario N2L 3G1 Canada

Funding

National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities

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