用于乳腺癌挥发性有机化合物生物标志物传感的硫代金纳米花

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Malkari Sravani;Aniruddh Bahadur Yadav;Shashi Prakash Mishra;G. V. Sivanath Reddy;Rahul Checker
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引用次数: 0

摘要

乳腺癌是一种恶性疾病,早期发现可显著改善患者预后。因此,采用了各种先进技术来进行早期检测。一种很有前景的方法是使用基于硫代金纳米颗粒的化学电阻,对呼出的细胞代谢副产物挥发性有机化合物(VOCs)进行分类。这些传感器可以选择性地检测一系列挥发性有机化合物,通过选择特定分子形状的硫醇来实现可调性。通过能谱分析(EDS)、x射线衍射(XRD)和扫描电镜分析(SEM),我们合成了具有高表面积体积比、面心立方(FCC)晶体结构、平均尺寸为203 nm的金纳米花(AuNFs)。将unfs分散在去离子水(DI)中,移液后滴涂在SiO2/p-Si(100)衬底上的指间金电极上,形成初始电阻高达5 k $\Omega $的连续薄膜。随后,采用简单可控滴涂法,用2-乙基-1-己硫醇、2-甲基-1-丙硫醇和苯乙基硫醇对AuNF膜进行了功能化处理;与配体离子交换法相比,该技术具有优势。巯基化使薄膜电阻增加到约10 k $\Omega $。由此产生的化学电阻传感器对乳腺癌VOC生物标志物(包括2-乙基-1-己醇、2-丙醇和七醛)表现出极好的敏感性。值得注意的是,2-甲基-1-丙硫醇功能化的aunf对七醛表现出最高的灵敏度(21.07%),优于其他硫醇修饰的传感器和文献中先前报道的值。传感器的响应速度和恢复时间分别为5秒和6秒。据作者所知,这种新颖的传感器设计和这种方法以前都没有报道过。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thiolated Gold Nanoflowers for Breast Cancer Volatile Organic Compound Biomarker Sensing
Breast cancer is a malignant disease, and patient prognosis significantly improves when it is detected at an early stage. Consequently, various advanced techniques have been adopted to enable early stage detection. One promising approach involves the classification of volatile organic compounds (VOCs) byproducts of cellular metabolism that are exhaled in breath, using chemiresistors based on thiolated gold nanoparticles. These sensors can selectively detect a range of VOCs, with tunability achieved by selecting thiols of specific molecular shapes. In this work, we synthesized gold nanoflowers (AuNFs) with a high surface-area-to-volume ratio, a face-centered cubic (FCC) crystalline structure, and an average size of 203 nm, as confirmed by energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The AuNFs were dispersed in deionized (DI) water, pipetted, and drop coated onto interdigital gold electrodes patterned on SiO2/p-Si (100) substrates to form a continuous film with an initial resistance of up to 5 k $\Omega $ . Subsequently, the AuNF films were functionalized with 2-ethyl-1-hexanethiol, 2-methyl-1-propanethiol, and phenylethyl mercaptan using a simple and controllable drop coating method; this technique offers advantages over the ligand ion exchange method. Thiolation increased the film resistance to approximately 10 k $\Omega $ . The resulting chemiresistor sensors demonstrated excellent sensitivity to breast cancer VOC biomarkers, including 2-ethyl-1-hexanol, 2-propanol, and heptaldehyde. Notably, 2-methyl-1-propanethiol functionalized AuNFs exhibited the highest sensitivity (21.07%) toward heptaldehyde, outperforming other thiol-modified sensors and previously reported values in literature. The sensors also demonstrated ultrafast response and recovery times of 5 and 6 s, respectively. To the best of the author’s knowledge, neither this novel sensor design nor this approach has been previously reported.
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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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