journal article·2023-11

Hydrogel derived N, P co-doped 3D honeycomb-like nano porous carbon: CAMN6P-3 and its electrochemical properties

Zuerguli Yisilamu, Xiaoting Zhao, Xieraili Maimaitiyiming, Anjie Liu

Journal of Nanoparticle Research· Volume 25· Issue 11
Publisher page

Abstract

The cost-effective and green preparation of electrocatalysts that are highly effective for both the cathode (reduction of oxygen) and anode (methanal oxidation) reactions is crucial for boosting the development of Direct Methanol fuel cells (DMFCs). This article describes a novel designed nitrogen and phosphorous co-doped honeycomb-like electrocatalyst (CAMN6P-3), which is derived from a hydrogel network formed with silk protein, polyaniline, and polyacrylamide. Notably, the CAMN6P-3 material has a surprisingly high nitrogen content (6.82%) and a high specific surface area (2494.55 m2 g−1). A high onset potential (1.11 V) was observed for the CAMN6P-3 catalyst, as well as a high limiting current density (5.4 mA.cm−2) during ORR. Furthermore, Pt/CAMN6P-3 demonstrates considerable catalytic activity as well as electrochemical stability for MOR as a catalyst carrier for Pt nanoparticles (Pt NPs). After long-term stability tests, the current density in the MOR was equivalent to a factor of twenty times that of the catalyst made from Pt/C. This superior performance can be attributed to the special nanostructure, where the honeycomb nanostructure not only provides an efficient channel for electron transfer and exposes more active sites, but also facilitates a high degree of dispersion of the Pt nanoparticles. From the perspective of sustainable development, combined with low-cost materials and the green preparation process, this work has a certain reference value for the development of highly efficient catalysts in the field of clean fuel cells.
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Research context

OpenAlex topics: Electrocatalysts for Energy Conversion · Fuel Cells and Related Materials · Advanced Battery Materials and Technologies

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

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