Synthesis of copper nano/microparticles via thermal decomposition and their conversion to copper oxide film

Abstract

Copper nano/microparticles were synthesized in octadecene at 290 °C by thermal decomposition method. Copper acetate monohydrate, stearic acid, and 1-octadecanol were used as copper precursor, capping and mild reducing agents, respectively, at the synthesis. Borosilicate glass substrates submerged into the reaction solution during the synthesis were coated by copper nano/microparticles. Thermal decomposition and coating techniques were combined in this study. Copper nano/microparticles were thoroughly characterised via X-ray powder diffraction, X-ray photoelectron, Raman, and attenuated total reflectance-Fourier transform infrared spectroscopies, and scanning and transmission electron microscopies. The average minimum Feret’s diameter of these synthesized copper particles was measured as ~87 ± 19 nm. Copper nano/microparticles were converted to copper oxide nano/microparticles by applying heat treatment at 250 °C. The phase composition of copper oxide nano/microparticles was determined by reference intensity ratio analysis. The energy gap of copper oxide nano/microparticles was determined as ~2.33 eV by using Tauc’s method. Their band gap PL emission was observed at ~2.15 eV.

Analysis on the Morphology and Interface of the Phosphate Coating Prepared on X39Cr13 and S355J2 Steels

“…According to the literature [ 26 ], the Feret diameter of a particle can be defined as the distance between two parallel tangent boundaries of the object. In particular, the maximum and minimum Feret diameters, are often used for the characterization of particle sizes [ 27 ]. According to the literature [ 28 ], the minimum and maximum Feret diameters of objects are compared according to the aspect ratio, whereas the axial ratio refers to the best match between the minor and major axes of the ellipse.…”

“…SEM images are composed of grayscale pixels corresponding to pseudo-heights that can be processed in order to derive various characteristics of the surface geometry. In the present paper, the two following approaches were used: (1) fractal analysis that makes use of scaling invariance between samples of averaged surface profiles [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ] and (2) statistical approach working on the separated segments of original images. In the first method, original SEM images (example shown in Figure 1 A) were averaged along the rows of the slow scan axis in order to obtain roughness profiles ( Figure 1 B), which were then processed into discrete structure functions according to the formula [ 31 ]: where τ is the discrete shift between original profile and its copy, m = τ /Δ is the integer number, Δ—the scan step, z k —the k-th sample of the mean profile and N —the number of samples in each profile.…”