气体传感阵列的构建及其对人体挥发性代谢物指纹谱的识别

Acquiring information of metabolic product in human body is becoming an important method for disease diagnosis. Medical research proved that trace amount of organic and inorganic molecules contained in volatile metabolic products constitute a important class of disease biomarker, and variation in the concentration or distribution of these molecules will provide valuable information for clinical diagnosis. Therefore, human volatile metabolic fingerprintis (VMF) have now regarded as important information in metabonomics research. Collection and recognization of VMF will become a new type of non-invasive diagnosis technology. In this project, graphene modified with ionic liquids, oligonucleotides or peptides with diverse chemical structure will be used as gas sensing elements. To compatible with current electronic circuit, graphene-based macroscopic material will be fabricated through substrate-supporting and self-supporting methods,respectively. This type of sensing materials perhaps have multiple mechanism for gas sensing. Gas molecules adsorbed on the surface of the functionalized graphene may not only change the charge density on the graphene sheet, but also affect the electron hopping ability between adjacent graphene sheets. The multiple sensing mechanism allow the graphene-based array to respond to large species of organic vapor and typical inorganic gas simultanousely. VMF analyze system will be constructed by assembling the sensing array chip with a multi-channel resistance meter and sample collecting device. This system will be used in collecting and analyzing large amount of clinical samples. The final goal of this project is to use the VMF analyzer to discriminate and recognize between disease person and health control with the assistance of statistical data analysis. The technology based on VMF analysis will find promising application in disease screening and diagnosis together with current established diagnostic technology.

对人体代谢产物分子信息的获取已经成为疾病诊断的重要手段。医学研究表明人体挥发性代谢物中微量有机和无机气体分布和含量的变化包含着重要的疾病信息。这些微量气体构成的挥发性代谢物指纹谱(VMF)已被认为是一种重要的代谢组学信息，对VMF的快速获取和识别将成为一种新型的非介入式诊断技术。本项目拟采用结构多样化的离子液体、寡肽和寡核苷酸修饰石墨烯，将其组装成选择性气体识别单元，并构筑负载型和自支撑型石墨烯气体传感材料，实现与宏观电子器件的耦合。该气体传感材料可能具有多种物理响应机制：气体分子在其表面的吸附将不仅影响单层石墨烯的电荷密度，也可能影响石墨烯层间的电子跨越能力。这种多响应机制使石墨烯阵列传感芯片能对多种有机和无机气体产生电学响应。该传感芯片可与多通道电阻测试仪和气体采样装置共同构成VMF分析系统，通过大量临床样本VMF的分析，建立数据集，并用统计学手段实现疾病患者和正常人群间的区分和识别。

对人体代谢产物分子信息的获取已经成为疾病诊断的重要手段。医学研究表明人体呼出气体中微量有机和无机气体分布和含量的变化包含着重要的疾病信息。这些微量气体构成的呼出气体指纹已被认为是另一种代谢组学信息，对呼出气体指纹的快速获取和识别将成为 一种新型的非介入式诊断技术。然而，目前的气体传感器技术判别复杂气体仍面临着巨大的挑战。主要技术难点包括气体检测灵敏度，选择性，器件的小型化与稳定性，器件的抗干扰能力等。本项目围绕上述关键问题在传感材料、器件构成、信号维度等方面开展了研究。具体成果如下：（1）在传感材料方面，研究以二维纳米材料和一维半导体纳米材料为基础导电介质的复合传感材料。提出了室温离子液体修饰（RTILs）还原氧化石墨烯（rGO）的方法，六种RTILs@rGO对不同类别的气体表现出差别化响应。此外，提出了过渡金属离子(M)诱导GO的自组装方法，M-GO自组装体系经过原位还原后的M@rGO对微量无机及有机气体表现出更高的灵敏度和交叉响应能力。通过电荷转移理论及二维材料层间膨胀理论对传感机理进行了合理的解释。M@rGO阵列对106例临床采集的人体呼出气体进行了检测，结果表明该传感阵列对肺癌患者的呼出气体判别的敏感性特特异性均达到92%以上。制备了金属氧化物（MO）-硅纳米线（SiNWs）核壳型纳米结构的方法，不同类别的MO@SiNWs可在室温下实现对多种气体的交叉响应，并表现出较高的稳定性。（2）器件研究上分别在纸芯片和ITO-PET薄膜芯片上构建了八通道二维材料传感阵列。通过电化学辅助沉积和聚合物介导的自组装方式降低了器件的噪音并提高了一致性。通过SiNWs顶端接触的方式构建了MO@SiNWs阵列式气体传感器件。（3）信号维度方面，本项目提出了光调制，半导体p-n正反向测试的方法提高了信号测量维度，这种物理阵列与虚拟阵列的耦合有望显著提升传感器阵列对复杂气体的识别能力。将上述传感芯片与多通道电学信号采集仪（或智能手机）、气体采样装置共同构成气体指纹分析系统，通过统计学分析手段实现了复杂人体气体的区分和识别。本项目的研究成果将对个人健康管理及无创医学诊断具有重要意义。