Neuron-Inspired Interpenetrative Network Composed of Cobalt–Phosphorus-Derived Nanoparticles Embedded within Porous Carbon Nanotubes for Efficient Hydrogen Production
journal contributionposted on 2016-06-17, 00:00 authored by Juanxia Shen, Zhi Yang, Mengzhan Ge, Ping Li, Huagui Nie, Qiran Cai, Cancan Gu, Keqin Yang, Shaoming Huang
The ongoing search for cheap and efficient hydrogen evolution reaction (HER) electrocatalysts to replace currently used catalysts based on Pt or its alloys has been considered as an prevalent strategy to produce renewable and clean hydrogen energy. Herein, inspired by the neuron structure in biological systems, we demonstrate a novel fabrication strategy via a simple two-step method for the synthesis of a neuronlike interpenetrative nanocomposite network of Co–P embedded in porous carbon nanotubes (NIN-Co–P/PCNTs). It is found that the interpenetrative network provides a natural transport path to accelerate the hydrogen production process. The embedded-type structure improves the utilization ratio of Co–P and the hollow, tubelike, and porous structure of PCNTs further promote charge and reactant transport. These factors allow the as-prepared NIN-Co–P/PCNTs to achieve a onset potential low to 43 mV, a Tafel slope as small as 40 mV/decade, an excellent stability, and a high turnover frequency value of 3.2 s–1 at η = 0.2 V in acidic conditions. These encouraging properties derived from the neuronlike interpenetrative network structure might offer new inspiration for the preparation of more nanocomposites for applications in other catalytic and optoelectronic field.
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Tafel slopeneuronlike interpenetrative nanocomposite network0.2 Vutilization ratiooptoelectronic fieldneuron structurenovel fabrication strategyneuronlike interpenetrative network structurehydrogen evolution reactioncarbon nanotubesreactant transporthydrogen production processEfficient Hydrogen ProductionHERCo43 mVtransport pathacidic conditionshydrogen energyPorous Carbon NanotubesPCNTinterpenetrative networkturnover frequency value