蜂蜜掺假检测用Mg0.75Co0.15Ni0.1Fe2O4纳米柔性超材料的制备及性能分析

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Md. Bakey Billa;Touhidul Alam;Mohammad Tariqul Islam
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引用次数: 0

摘要

广泛存在的蜂蜜掺假问题造成了重大的健康风险和经济损失,需要更有效和可靠的检测方法。传统的技术通常耗时、昂贵,并且需要复杂的设备。此外,传统上用于基于超材料的传感器的衬底存在刚性,有限的灵敏度和选择性等挑战。本研究旨在通过制备Mg0.75Co0.15Ni0.1Fe2O4纳米颗粒,并评估其在蜂蜜掺假柔性超材料传感器中的性能来解决这一问题。使用介电评估套件(DAK)-3.5测量衬底的介电特性,发现介电常数为1.71。该传感器是在镁钴铁氧体衬底上制作的,具有改进的迷宫状结构。该超材料在模拟和测量的7.6-8 GHz频率范围内均表现出$\mu $负特性,适合于传感应用。为了优化传感器的性能,在先进设计系统(ADS)中开发了电路模型,并通过CST微波工作室仿真验证,显示出更高的实时效率。使用纯蜂蜜和掺入5%和10%糖精和糖的蜂蜜来评估传感器的性能。介电常数随掺假浓度的增加而增加,从纯蜂蜜的12.5增加到添加10%糖精的蜂蜜的15。相应的谐振频移从230 MHz增加到480 MHz。灵敏度范围从20到60 MHz/掺杂模拟和测量。模拟数据与实测数据的相对误差保持在0.4%以下,证实了传感器的准确性。有效介电常数与谐振频移之间的线性关系,记录在研究的数字中,展示了一种可预测的方法来确定蜂蜜掺假水平,增强了该传感器在工业食品质量控制中的实际适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preparation and Performance Analysis of Mg0.75Co0.15Ni0.1Fe2O4 Nanoparticle-Based Flexible Metamaterial for Honey Adulteration Detection
The widespread issue of honey adulteration poses significant health risks and economic losses, necessitating more efficient and reliable detection methods. Traditional techniques are often time-consuming, expensive, and require sophisticated equipment. Moreover, the substrates traditionally used in metamaterial-based sensors present challenges such as rigidity, limited sensitivity, and selectivity. This study aims to address this problem by preparing Mg0.75Co0.15Ni0.1Fe2O4 nanoparticles and evaluating their performance in a flexible metamaterial sensor for honey adulteration detection. The dielectric property of the substrate is measured using a dielectric assessment kit (DAK)-3.5, with dielectric constants found to be 1.71. The proposed sensor fabricated on a Mg-Co ferrite substrate with a modified maze-shaped structure. The metamaterial exhibits $\mu $ -negative characteristics within the frequency range of 7.6–8 GHz both simulated and measured, making it suitable for sensing applications. To optimize sensor performance, a circuit model is developed in Advanced Design System (ADS) and verified with CST microwave studio simulations, showing improved real-time efficiency. The sensor’s performance is evaluated using pure honey and honey adulterated with 5% and 10% saccharine and sugar. The dielectric constant increased with adulterant concentration, from 12.5 for pure honey to 15 for honey with 10% saccharine. The corresponding resonant frequency shifts increased from 230 to 480 MHz. Sensitivity ranged from 20 to 60 MHz/adulterant both simulated and measured. The relative error between simulated and measured data remained below 0.4%, confirming the sensor’s accuracy. The linear relationship between the effective dielectric constant and the resonant frequency shift, documented in the study’s figures, demonstrates a predictable method to determine honey adulteration levels, enhancing the practical applicability of this sensor in industrial food quality control.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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