Low Pt content catalyst supported on nitrogen and phosphorus-codoped carbon nanotubes for electrocatalytic O2 reaction in acidic medium-Reference-Cited by-同舟云学术

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Low Pt content catalyst supported on nitrogen and phosphorus-codoped carbon nanotubes for electrocatalytic O2 reaction in acidic medium

Published:2015-03 Issue: Volume:142 Page:115-118
ISSN:0167-577X
Container-title:Materials Letters
language:en
Short-container-title:Materials Letters

Author:

Liu Ziwu,Qu Junfeng,Fu Xin,Wang Quande,Zhong Guoyu,Peng Feng

Funder

NSFC

Natural Science Foundation of Jiangsu Province, China

Guangdong Provincial Key Laboratory for Green Chemical Product Technology

Key Laboratory of Fuel Cell Technology of Guangdong Province

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science

Reference17 articles.

1. An overview on non-platinum cathode catalysts for direct methanol fuel cell

2. Oxygen Electroreduction Catalyzed by Gold Nanoclusters: Strong Core Size Effects

3. Novel catalyst support materials for PEMfuelcells: current status and future prospects

4. Nitrogen-Doped Carbon Nanotubes as Platinum Catalyst Supports for Oxygen Reduction Reaction in Proton Exchange Membrane Fuel Cells

5. Enhanced methanol oxidation activity of Pt catalyst supported on the phosphorus-doped multiwalled carbon nanotubes in alkaline medium

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1. P-containing coated carbons from phosphonium ionic liquids as catalyst supports for fuel cell applications;Sustainable Energy & Fuels;2023

2. Improved electrocatalytic properties of bundled B/N co-doped electrospun carbon nanofibers with Pt nanostructures through dopant-induced metal-support interaction (DIMSI);Materials Science and Engineering: B;2022-10

3. Optimization of platinum loading on partially fluorinated carbon catalysts for enhanced proton exchange membrane fuel cell performance;Journal of Power Sources;2022-09

4. Recent progress in carbon nanotubes support materials for Pt-based cathode catalysts in PEM fuel cells;International Journal of Hydrogen Energy;2022-08

5. Phenolic Compound–Loaded Nanosystems: Artificial Neural Network Modeling to Predict Particle Size, Polydispersity Index, and Encapsulation Efficiency;Food and Bioprocess Technology;2019-06-23

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