journal article·2018-07Open access

Optimization of process parameters and its effect on structure and morphology of CuO nanoparticle synthesized via the sol−gel technique

Hafsa Siddiqui, Mohammad Ramzan Parra, Fozia Z. Haque

Abstract

This article describes systematic basic research on the optimization of the processing parameters of sol−gel technique for synthesis of the high purity CuO nanoparticles. Effect of the synthesis parameters such as copper salt concentration, solvent and gelating agent, optimized one at a time, was investigated by employing XRD, TEM, FESEM, micro-Raman, UV-visible-NIR and PL spectroscopies. XRD results clearly demonstrate the monoclinic structure of CuO nanoparticles with traceable impurities at a lower molar concentration of Cu2+, transition of nucleation system from homogeneous to heterogeneous state with the increase in concentration of Cu2+ from 0.05 to 0.15 M. It was also found that the isopropyl alcohol offers better results in comparison to ethanol and water. Moreover, the lattice parameters, space group, and crystal system were determined by powder X-ray diffraction method. Further we propose the optimization of synthesis process using ethylene glycol and citric acid (EG:CA). The Raman analysis confirmed the influence of ethylene glycol and citric acid ratio and TEM observations confirmed that EG:CA 1:2 ratio formulate homogenous flower-like nanostructures. The optical absorption of CuO nanostructures can be easily tuned by varying the concentration of citric acid without changing other conditions; it shows the role of synthesis parameters more significant. Our results suggest that the prepared CuO nanostructures have a potential to be used as absorbing material in solar cell applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Copper-based nanomaterials and applications · ZnO doping and properties · Electronic and Structural Properties of Oxides

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

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