Scholarlink

<p>This study investigates the optimisation of electrospinning parameters for synthesising zinc oxide (ZnO)-reinforced polyvinylpyrrolidone (PVP) nanofibers, focusing on establishing statistically robust correlations between process variables and fibre morphology. The key parameters analysed include PVP concentration, applied voltage, solution flow rate, and needle-to-collector distance. Multi-correlation statistical methods (Pearson, Spearman rank, and Kendall Tau) were applied to quantify parameter influence, while root mean square error (RMSE) values validated the predictive accuracy of the models. The results showthat PVP concentration has the strongest positive correlation with fibre diameter (Pearson <i>r</i> = 0.992), with an increase in PVP concentration (11–54% w/v) leading to fibres that are nearly 30% thicker. The flow rate showed a little positive association (<i>r</i> = 0.089), whilst the voltage showed a rather insignificant negative correlation (<i>r</i> = − 0.069), which indicates that higher voltages marginally induce thinner fibres. Very little of an impact was exerted on the fibre diameter by the distance that separated the needle from the collection. The evaluation of the accuracy of the forecast produced RMSE values that were lower than 10&#xa0;nm, which confirmed the reliability of the method. According to the findings, the concentration of PVP is the most important component that determines the morphology of the fibre, whereas the magnitude of the effects that voltage and flow rate have are reduced. In order to reduce the amount of experimental trial-and-error in nanofiber optimisation, this work demonstrates the usefulness of merging statistical correlation with predictive analysis. This work goes beyond straightforward mechanical comprehension. In the areas of photocatalysis, energy storage, and biological scaffolding, the ZnO–PVP nanofibers that were created show considerable potential for use. This research aligns with the United Nations Sustainable Development Goals (SDG 9, SDG 12, and SDG 7) by promoting sustainable nanomanufacturing practices, reducing experimental redundancy, and enabling the development of nanofiber materials for clean-energy and advanced engineering applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-correlation analysis of optimizing the electrospinning parameters for ZnO + PVP nanofiber synthesis using statistical coefficient methods

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

查看全文
  • Princy Randhawa,
  • V. Shwetha,
  • Harshada Vishal Mhetre,
  • Anand Pandey,
  • R. Sonawane Chandrakant,
  • Pavan Hiremath

摘要

This study investigates the optimisation of electrospinning parameters for synthesising zinc oxide (ZnO)-reinforced polyvinylpyrrolidone (PVP) nanofibers, focusing on establishing statistically robust correlations between process variables and fibre morphology. The key parameters analysed include PVP concentration, applied voltage, solution flow rate, and needle-to-collector distance. Multi-correlation statistical methods (Pearson, Spearman rank, and Kendall Tau) were applied to quantify parameter influence, while root mean square error (RMSE) values validated the predictive accuracy of the models. The results showthat PVP concentration has the strongest positive correlation with fibre diameter (Pearson r = 0.992), with an increase in PVP concentration (11–54% w/v) leading to fibres that are nearly 30% thicker. The flow rate showed a little positive association (r = 0.089), whilst the voltage showed a rather insignificant negative correlation (r = − 0.069), which indicates that higher voltages marginally induce thinner fibres. Very little of an impact was exerted on the fibre diameter by the distance that separated the needle from the collection. The evaluation of the accuracy of the forecast produced RMSE values that were lower than 10 nm, which confirmed the reliability of the method. According to the findings, the concentration of PVP is the most important component that determines the morphology of the fibre, whereas the magnitude of the effects that voltage and flow rate have are reduced. In order to reduce the amount of experimental trial-and-error in nanofiber optimisation, this work demonstrates the usefulness of merging statistical correlation with predictive analysis. This work goes beyond straightforward mechanical comprehension. In the areas of photocatalysis, energy storage, and biological scaffolding, the ZnO–PVP nanofibers that were created show considerable potential for use. This research aligns with the United Nations Sustainable Development Goals (SDG 9, SDG 12, and SDG 7) by promoting sustainable nanomanufacturing practices, reducing experimental redundancy, and enabling the development of nanofiber materials for clean-energy and advanced engineering applications.