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<p>Herein, we reported a simple, low-cost, extremely sensitive non-enzymatic copper oxide nanotaper (CuO NT)-based electrochemical glucose sensor. CuO NT was synthesized using hydrothermal methods and characterized using a scanning electron microscope (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscope. The carrier concentration, diffusion length, depletion width, and potential barrier were estimated using the Mott-Schottky plot. Glucose sensing performance was studied using cyclic voltammetry, amperometry, and electrochemical impedance spectroscopy at different glucose concentrations. The CuO NT showed glucose sensitivity of 1.0977 mAmM<sup>−1</sup>cm<sup>−2</sup> in the linear detection range of 5 to 300 μM with a limit of detection (LOD) of 1.467 μM. Also, the CuO NT showed excellent selectivity and stability, which makes it a promising material for non-enzymatic and noninvasive saliva glucose sensing. Further, the CuO NT-based glucose sensor was modeled using an artificial neural network (ANN) to predict the unknown glucose concentration.</p>

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Hydrothermally Grown p-Type CuO Nanotaper for Saliva Glucose Sensing Application

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Tanmoy Majumder,
  • Kaberi Saha,
  • Kamalesh Debnath,
  • Jehova Jire L. Hmar,
  • Raju Patel

摘要

Herein, we reported a simple, low-cost, extremely sensitive non-enzymatic copper oxide nanotaper (CuO NT)-based electrochemical glucose sensor. CuO NT was synthesized using hydrothermal methods and characterized using a scanning electron microscope (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscope. The carrier concentration, diffusion length, depletion width, and potential barrier were estimated using the Mott-Schottky plot. Glucose sensing performance was studied using cyclic voltammetry, amperometry, and electrochemical impedance spectroscopy at different glucose concentrations. The CuO NT showed glucose sensitivity of 1.0977 mAmM−1cm−2 in the linear detection range of 5 to 300 μM with a limit of detection (LOD) of 1.467 μM. Also, the CuO NT showed excellent selectivity and stability, which makes it a promising material for non-enzymatic and noninvasive saliva glucose sensing. Further, the CuO NT-based glucose sensor was modeled using an artificial neural network (ANN) to predict the unknown glucose concentration.