对映体选择性化学传感器与手性识别载体研究

对映体选择性化学传感器的研究是化学传感器研究领域新的重要课题。本文作为作者关于这一课题研究报道的开篇,综述了具有手性识别功能的对映体选择性化学传感器和作为其敏感材料的手性识别载体的研究概况,概述了其发展方面及有关问题,收引参考文献67篇。     

气敏新材料MXenes在呼出气体传感器中的应用

电子鼻结合人工智能对呼出气进行检测、分析和识别已成为非侵入性医疗检测领域的研究热点。然而,目前已报道的气体传感材料尚不能同时满足高灵敏度、高选择性和稳定的室温检测,阻碍了气体传感器在医疗健康领域的应用及发展,寻找合适的传感材料具有重要的意义和挑战。新型二维层状纳米材料MXenes具有种类多、比表面积大、导电性能强、表面含有丰富的官能团以及能带宽度可调等优异性能,是高灵敏、低能耗气体传感器的明星候选材料。本综述针对MXenes基材料的特殊结构,总结梳理了MXenes基材料在气体传感中的最新研究成果,聚焦于MXenes材料的气体传感机理和改性方法,对MXenes材料用于气体传感依然存在的问题和挑战进行深入探讨。     

不同种类敏感膜修饰的QCM气体传感器研究现状

石英晶体微天平(quartz crystal microbalance,QCM)是一种对质量变化敏感的器件,具有灵敏度高、成本低廉、操作简单、可实时在线检测等优点,在气体传感领域受到了广泛关注。敏感材料是石英晶体微天平气体传感器的关键组成部分,本文综述了不同敏感材料包括有机聚合物、超分子化合物、离子液体和分子液体以及近年来备受关注的纳米材料修饰的QCM对特定气体传感检测的研究现状,详细介绍了纳米材料为敏感膜的QCM气体传感器对不同气体传感检测的研究现状及相关敏感机理。最后,在国内外研究现状的基础上,展望了敏感材料的发展前景。QCM作为一种成本低廉、操作方便、测量精度高的气体传感检测器件,将会有更加广阔的应用前景。     

SAW气体传感器聚合物敏感膜材料研究进展

声表面波(surface acoustic wave,SAW)气体传感器具有灵敏度高、选择性好、响应时间短以及体积小,携带方便等优点,因而被广泛应用于环境监测、医疗卫生、化学侦检等领域中有毒有害气体的现场实时检测。敏感膜材料的特性是决定SAW传感器性能(如灵敏度、选择性、响应时间、寿命等)的关键因素。本文首先简要介绍了SAW气体传感器的响应原理和对敏感膜材料的要求,然后重点阐述了用于SAW气体传感器的有机聚合物敏感膜材料的研究进展,最后对其研究趋势做出简单预测。     

钼酸铋基气敏材料研究进展

气体传感器被广泛应用于检测工业和家庭中有毒有害气体。气体敏感材料是气体传感器中重要的组成部分,敏感材料的性质决定了气体传感器的性能。研制精度高、检测快、集成度高的气体检测器迫在眉睫。钼酸铋作为一种新型双金属氧化物气敏材料,具有高选择性、高敏感度的优势。本文从气敏机理、形貌控制、掺杂和复合材料构建方面对近年来钼酸铋作为气敏材料的研究进行了总结,并对钼酸铋基气敏材料未来的研究方向进行了展望。     

金属有机框架及其衍生的金属氧化物在电阻式气体传感器中的应用

高灵敏和选择性的气体传感器对于实时监测大气中有毒有害气体和早期的疾病诊断具有重要的意义.目前,传统的气敏材料仍然存在着许多问题亟待解决,例如:选择性差、检测极限不够低、使用寿命短等.作为一种多孔的配位聚合物,金属有机框架材料(MOFs)由于其超高的比表面积和较大的孔隙率在气体传感器领域已经得到广泛的应用.利用MOFs自...     

金属有机骨架材料在传感器中的应用

金属有机骨架(MOFs)是由有机配体与金属离子或金属离子簇通过配位作用自组装而成的一种具有永久孔道性的开放结晶骨架,通常也被称为多孔配位聚合物(PCPS)。因为其较大的比表面积、规整的孔道结构、良好的热稳定性和化学可裁剪性,使其在多个领域具有广阔的应用前景。近年来,随着MOFs在传感领域的发展,许多不同的功能基团被引入到MOFs的孔道中,研制出具有荧光识别性能的MOFs。本论文综述了近几年来基于MOFs的化学传感器在离子识别、pH检测、挥发性有机物和气体检测、爆炸物识别和生物分子检测等关键领域的研究进展,并对MOFs在化学传感器的应用前景进行了展望。     

改性碳纳米管气体传感器

碳纳米管气体传感器具有灵敏度高、响应速度快、尺寸小和能在室温下工作等诸多优点,是一种很有前景的气体传感器.然而本征碳纳米管气体传感器只对少数几种气体如NH3、O2、NO2和SO2敏感,检测范围有限;而且这类传感器的检测灵敏度和选择性也有待提高.研究表明对碳纳米管进行改性可以克服这些缺陷.目前已有的改性方法主要包括对碳纳米管表面有机修饰、对碳纳米管掺入无机杂原子以及径向力学变形等.本文对改性碳纳米管气体传感器研究的最新进展进行了综述,分析了上述改性方法在扩大碳纳米管气体传感器的检测范围、提高检测灵敏度和选择性方面的优势和不足,并对其研究前景进行了展望.     

氟硼二吡咯亚甲基类荧光探针在离子检测中的应用进展

氟硼二吡咯亚甲基类(BODIPY)类化合物具有摩尔吸光系数高、荧光量子产率高、吸收波长在可见光或近红外区域、荧光寿命长和光稳定性好等特点,并且其荧光对溶剂的极性和pH均不敏感,是一类可应用于生物领域的荧光染料,近年来,此类化合物被广泛用于设计合成荧光传感器分子,用于各种离子的检测,此类荧光探针具有分析灵敏度高、选择性好等特点.综述了氟硼二吡咯亚甲基类荧光探针在离子识别和检测中的应用,并展望了该领域的前景.     

二氧化锡气体传感器对有机磷农药残留的动态检测

研究了一种快速检测和识别农药敌百虫、乙酰甲胺磷和乐果气体的新方法,即动态检测方法。这种方法利用单个SnO2气体传感器而非阵列在方波温度调制的状态下可实现3种农药气体的快速检测。实验结果表明：在0．02Hz的调制频率和250—300℃的温度调制范围,传感器表现出高的选择性和稳定性;利用FFT频谱和极坐标构建完成对敌百虫、乙酰甲胺磷和乐果的定性和定量检测。     

A comparison of different chemometrics approaches for the robust classification of electronic nose data

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365–372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56 % accuracy) and SVM with a polynomial kernel (91.66 % accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.     

基于石墨烯光子晶体光纤的流体传感器

与传统的传感器设备阵列相比,由于结构更为简单,具有广泛检测兼容性的光纤系统逐渐成为分布式监测的有力候选者。然而,受工作机制的限制,大多数光纤传感器仍局限于对折射率等物理参数进行探测,一种用于环境化学监测的全光纤分布式传感系统亟待研发。本工作中,我们向化学气相沉积法生长的石墨烯光子晶体光纤(Gr-PCF)中引入了一种化学传感机制。初步结果表明,石墨烯光子晶体光纤可以选择性地检测浓度为ppb级的二氧化氮气体,并在液体中表现出离子敏感性。石墨烯光子晶体光纤与光纤通信系统的波分、时分复用技术结合后,将为实现分布式光学传感环境问题提供巨大的潜力和机会。     

Enhancing Classification Performance of Multisensory Data through Extraction and Selection of Features

Linear discriminant analysis (LDA) has been widely used in the classification of multi sensor data fusion. This paper discusses the performance of LDA when the classifications were performed based on feature extraction and feature selection methods. Comparisons were also made based on single sensor modality. These strategies were studied using a honey dataset along with two types of sugar concentration collected from two types of sensors namely electronic nose (e-nose) and electronic tongue (e-tongue). Assessment of error rate was achieved using the leave-one-out procedure.     

石墨烯在自供能传感系统中的应用

随着现代社会智能化的加速发展,传感系统中传感器的数量、密度和分布范围不断增加,传统的供能方式难以满足如此复杂多变的传感器供能需求,从周围环境中收集能量并转化为电能的自供能传感器件是解决这一难题的有效途径。石墨烯不仅具有优异的传感性能,而且在各种能源器件中有广泛的应用,这为基于石墨烯的自供能传感器件设计提供了便利。近年来,人们已经研究和发展了多种多样的石墨烯自供能传感器件。本文基于自供能器件的基本能量供给原理,包括电化学供能、光伏供能、摩擦电供能、水伏供能以及热电、压电、热释电等其它供能,分别介绍了石墨烯在自供能传感器件中的应用,并展望了基于石墨烯的自供能传感器件的未来发展、挑战和前景。     

气驱油油气混相过程的界面传质特性及其分子机制

油气混相过程的界面传质特性对气驱提高原油采收率技术非常重要。本文针对吉林某油田的实际油组分,采用分子动力学模拟研究了气驱油过程,分析了不同气体和驱替压力下油气两相的状态变化以及界面特性,获得不同驱替气体的最小混相压力(MMP)。结果表明,随着驱替气体压力的升高,气相的密度逐渐增大,油相膨胀密度降低,气相与油相的混合程度增强,油气两相界面厚度增加,界面张力随之减小。同时发现,驱替相中二氧化碳浓度越高,在同等气体压力下,油气界面更厚,油气混合程度更高。纯CO₂驱油得到的MMP远远小于纯N₂驱油,当这两种气体摩尔比为1 : 1混合时MMP介于两种纯气体之间,说明要达到同样的驱油效果二氧化碳需要的压力更小。最后,本文从分子微观作用力角度解释了驱替气体不同时影响油气混相程度的机制,通过分子平均作用势曲线发现油相分子对CO₂的吸引力要大于N₂分子,因此CO₂分子更容易与油相混合,驱替效果更明显。     

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.     

高效医学传感钙钛矿材料研究进展

卤化物钙钛矿材料作为一种新型半导体材料，具有优异的光电转换特性、能级结构可调、易于加工、结构和尺寸以及形貌可调、改性后优异的生物相容性等优点，在医学检测传感器中具有广阔的应用前景。本综述讨论了钙钛矿材料在生物医学传感领域的研究进展，钙钛矿医学传感器能通过光电转换、全光转换、电催化等多种物理或化学机制实现传感，具有可灵活选择的器件结构、性能指标和信号传递方式，用于人体代谢物质、神经递质、癌症相关物质和药物等医学物质的检测。钙钛矿医学传感器将为未来的医工多学科融合提供新希望，加快医工融合发展。     

蚕丝基智能纤维及织物：潜力、现状与未来展望

纤维及织物因具有良好的柔性、透气性以及适宜的力学性能而成为人们日常生活必不可少的材料。随着柔性电子器件的快速发展,纤维及织物在其自身优势的基础上,开始被人们赋予智能化特征,使得智能纤维和织物逐渐在可穿戴领域占据一席之地。天然蚕丝具有产量大、机械性能优异和生物可降解的优势。近年来,面向智能应用的蚕丝基纤维与织物逐渐发展,被用于传感、致动、光学器件、能量收集和储能等领域。本文将首先介绍天然蚕丝的层级结构和性能,并介绍各种形貌结构的再生蚕丝材料;然后根据其在智能纤维及织物中应用领域的不同,详细阐述蚕丝基智能纤维及织物的制备方法、性能及工作机制;最后讨论进一步发展所面临的挑战与机会,并对未来前景进行展望。     

Detection of Fungi and Oomycetes by Volatiles Using E-Nose and SPME-GC/MS Platforms

Fungi and oomycetes release volatiles into their environment which could be used for olfactory detection and identification of these organisms by electronic-nose (e-nose). The aim of this study was to survey volatile compound emission using an e-nose device and to identify released molecules through solid phase microextraction–gas chromatography/mass spectrometry (SPME–GC/MS) analysis to ultimately develop a detection system for fungi and fungi-like organisms. To this end, cultures of eight fungi (Armillaria gallica, Armillaria ostoyae, Fusarium avenaceum, Fusarium culmorum, Fusarium oxysporum, Fusarium poae, Rhizoctonia solani, Trichoderma asperellum) and four oomycetes (Phytophthora cactorum, P. cinnamomi, P. plurivora, P. ramorum) were tested with the e-nose system and investigated by means of SPME-GC/MS. Strains of F. poae, R. solani and T. asperellum appeared to be the most odoriferous. All investigated fungal species (except R. solani) produced sesquiterpenes in variable amounts, in contrast to the tested oomycetes strains. Other molecules such as aliphatic hydrocarbons, alcohols, aldehydes, esters and benzene derivatives were found in all samples. The results suggested that the major differences between respective VOC emission ranges of the tested species lie in sesquiterpene production, with fungi emitting some while oomycetes released none or smaller amounts of such molecules. Our e-nose system could discriminate between the odors emitted by P. ramorum, F. poae, T. asperellum and R. solani, which accounted for over 88% of the PCA variance. These preliminary results of fungal and oomycete detection make the e-nose device suitable for further sensor design as a potential tool for forest managers, other plant managers, as well as regulatory agencies such as quarantine services.     

大尺寸固体氧化物燃料电池的电极过程解析方法

电化学阻抗谱(Electrochemical Impedance Spectroscopy,EIS)作为一种原位/非原位的电化学表征技术,在固体氧化物燃料电池(Solid Oxide Fuel Cell,SOFC)尤其是小尺寸电池的研究中得到了广泛应用,而工业大尺寸电池的EIS研究较少且大多基于小尺寸电池的研究结果。本文对工业尺寸(10 cm × 10 cm)阳极支撑平板式SOFC搭建了EIS测试系统,并改变电池运行温度、阳极/阴极气体组分,对该电池进行了系统的EIS测试,而后采用不基于先验假设的弛豫时间分布法(Distribution of Relaxation Times,DRT)对EIS数据进行解析。通过比较分析不同条件下的DRT结果,揭示了DRT中各特征峰与电池中具体电极过程的对应关系。与小尺寸电池相比,由于大尺寸电池的有效面积较大且入口流量较小,气体转化过程在大尺寸电池中不容忽视。本文通过解析EIS实现了对工业大尺寸SOFC单电池中各项电极过程的分辨,该方法及结果能够进一步应用于SOFC原位表征、在线监测以及衰减机理等相关研究。