DataVXJournal of Nanoparticle ResearchPaper
journal article·2023-02Open access

Copper nitride/silver nanostructures synthesized via wet chemical reduction method for the oxygen reduction reaction

Aleksandra Ścigała, Robert Szczęsny, Piotr Kamedulski, Marek Trzcinski, Edward Szłyk

Journal of Nanoparticle Research· Volume 25· Issue 2
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Abstract

Abstract This work presents attempts to synthesize silver-doped copper nitride nanostructures using chemical solution methods. Copper(II) nitrate and silver(I) nitrate were used as precursors and the oleylamine as a reducing and capping agent. Homogeneous Cu3N/Ag nanostructures with a diameter of ~ 20 nm were obtained in a one-pot synthesis by the addition of the copper(II) salt precursor to the already-synthesized silver nanoparticles (Ag NPs). Synthesis in a two-pot procedure performed by adding Ag NPs to the reaction medium of the Cu3N synthesis resulted in the formation of a Cu3N@Ag nanocomposite, in which Ag NPs are uniformly distributed in the Cu3N matrix. The morphology, structure, and chemical composition of the obtained specimens were studied by TEM, XRD, XPS, and FT-IR methods, while optical properties using UV–Vis spectroscopy and spectrofluorimetry. The band gap energy decreased for Cu3N/Ag (Eg = 2.1 eV), in relation to pure Cu3N (Eg = 2.4. eV), suggesting the insertion of Ag atoms into the Cu3N crystal lattice. Additionally, Cu3N and Cu3N/Ag nanostructures were loaded on graphene (GNP) and tested as a catalyst in the oxygen reduction reaction (ORR) by cyclic voltammetry (CV) and linear sweep voltammetry (LSV). The Cu3N/Ag-modified GNP hybrid material revealed catalytic activity superior to that of Cu3N-based GNP hybrid material and pure GNP, comparable to that of a commercial Pt/C electrode. Graphical Abstract
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Research context

NanomaterialsCatalysis & surfaces

OpenAlex topics: Copper-based nanomaterials and applications · Electrocatalysts for Energy Conversion · Nanomaterials for catalytic reactions

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

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