基于近红外荧光量子点的防伪技术基础研究

提出了一种新型的利用近红外荧光量子点作为载体的防伪方法,并进行了相关基础研究,应对了产品市场对防伪技术不断革新的需要;设计了微弱荧光光谱测试系统,系统研究了双波长荧光量子点防伪识别技术,得出双波长荧光光谱识别的基本方法,将近红外荧光量子点的荧光强度之间的差异作为识别的依据,设计出相应的编码方法即可以对信息进行加密;测试结果表明该方法切实可行;研究结果对近红外荧光量子点的防伪识别有重要的指导意义,为防伪技术打开了更广阔的发展空间。     

基于退火效应硒化铅融合量子点制备及探测器性能研究

硒化铅纳米结构材料因其多激子产生效应、快速光敏吸收和近红外发射而被广泛应用于太阳能电池和光电探测器，并成为探测器领域的研究热点。在制备硒化铅量子点的基础上，通过退火工艺得到了一种新型的纳米材料融合量子点。所需的最佳退火温度可以有效减少缺陷，提高融合量子点的活性。为了体现融合量子点与其他材料相比的优势，采用旋涂法制备薄膜并用于光电导器件的研制。退火温度从室温提高到470℃,通过透射电子显微镜观察了不同温度下硒化铅量子点的融合现象，并研究了量子点退火对光电导器件性能的影响。量子点之间的平均距离随着退火温度的升高而减小，这增强了量子点之间的相互融合。实验数据表明，硒化铅活性层的最佳退火温度为310℃,开关比K和响应度R在310℃时达到最大值，响应度增加3倍以上。这是因为硒化铅活性层中载流子的迁移率增强。并成功制备了光电导探测器Au/PbSe融合量子点/Au(100 nm),为后续制备新的量子点器件奠定了基础。     

显微近红外图像用于分辨2种塑料材料的成像方法研究

显微近红外成像技术将成像技术和光谱分析技术相结合,不仅可以获取样品的光谱信息,而且还可以获取样品的空间分布信息,这是传统的近红外光谱分析所无法比拟的.本文以外观及近红外光谱极为相似的2种塑料材料为研究对象,采用显微近红外成像系统采集了并排放置的2种塑料材料的显微近红外图像,研究了2种塑料材料的成像方法.研究结果表明,在5910 cm-1附近,2种材料的近红外光谱具有细微差别.采用化学成像方法可以分别得到材料Ⅰ和材料Ⅱ清晰的成像图.本研究为显微近红外成像分辨不同材料的成像方法以及农产品、食品包装材料安全性的快速判别提供了一定的参考.     

近红外高光谱图像数据预测技术

目的 受到传感器光谱响应范围的影响,可见光区域和近红外区域（400~2 500 nm）的高光谱数据通常使用不同的感光芯片进行成像,现有这一光谱区域典型的高光谱成像系统,如AVIRIS （airborne visible infrared imaging spectrometer）成像光谱仪,通常由多组感光芯片组成,整个成像系统成本和体积通常比较大,严重限制了该谱段高光谱探测技术的发展。为了能够扩展单感光芯片成像系统获得的高光谱图像的光谱范围,本文探索基于卷积神经网络的近红外光谱数据预测技术。方法 结合AVIRIS成像光谱仪的光谱配置,设计了基于残差学习的红外谱段图像预测网络,利用计算成像的方式从可见光范围的高光谱图像预测出近红外波段的光谱图像,并在典型的卫星高光谱遥感数据上进行红外光谱预测重构和基于重构的数据分类实验,以验证论文提出的红外光谱数据预测技术的可行性以及有效性。结果 本文设计的预测网络在Cuprite数据集上得到的预测近红外图像峰值信噪比为40.145 dB,结构相似度为0.996,光谱角为0.777 rad;在Salinas数据集上得到的预测近红外图像峰值信噪比为39.55 dB,结构相似性为0.997,光谱角为1.78 rad。在分类实验中,相比于只使用可见光图像,利用预测的近红外图像使得支持向量机（support vector machine,SVM）的准确率提升了0.6%,LeNet的准确率提升了1.1%。结论 基于AVIRIS传感器获取的两组典型卫星高光谱数据实验表明,本文提出的红外光谱数据预测技术不仅可基于计算成像的方式扩展可见光光谱成像系统的光谱成像范围,对于减小成像系统体积和质量具有重要意义,而且可有效提高可见光区域光谱图像数据在典型应用中的处理性能,对于提高高光谱数据处理精度提供新的技术支撑。     

量子结构光电传感器读出电路的成像系统

针对目前国内CMOS量子结构光电传感器件调测、应用技术的缺乏与不足,设计了一种由低维量子结构光电传感器读出电路、ARM和通用计算机成像软件3部分模块组成的光电成像系统;利用单片ARM实现对读出电路模块的控制和数据采集,以USB方式通信,在通用计算机上编写设备驱动和成像软件呈现光电传感器所对景象的灰度图像;系统硬件简洁,可通过计算机图形化界面灵活,精确地控制光电传感器件积分时阃、偏压等参数,并有效获取传感器输出值;应用结果表明,系统为光电传感器的测量、调试以及应用开发提供了极大便利.     

基于3D打印的近红外光谱成像系统前端设计

针对当前近红外成像光谱系统研发成本高、结构复杂、便携性差等问题,提出了基于3D打印的近红外光谱成像系统前端设计。通过3D打印技术设计系统前端发射器、接收器和采集头套,并与近红外光谱成像系统结合,设计出尺寸小、硬度强、易扩展的前端采集设备。利用高灵敏度的OPT101、ADS1299和GS1011完成信号采集与传输,通过3D打印可实现精度高、无线传输、可实时检测脑部血氧浓度的近红外光谱成像系统。     

适合近红外光谱数据特征的降维方法对比分析

因近红外光谱具有波长点多、谱带归属困难、光谱重叠严重及光谱分布结构未知等问题,在进行近红外光谱关键特征提取和数据特征空间映射时难以准确获知合适降维方法。为了解决该问题,本文对比分析了典型线性和非线性降维方法 ,并用烟叶近红外光谱数据从数据降维可视化和分类准确性识别率角度分别进行了实验验证。结果表明,线性降维算法,特别是PCA、LDA算法,比较适合应用于烟叶近红外光谱降维分析中,非线性降维算法因其泛化学习能力与推广能力差以及本征维数估计困难不适合应用于近红外光谱降维分析。     

具有组分跟踪的混合原油馏分性质检测方法设计

混合原油馏分性质检测目前多采用近红外技术，并基于偏最小二乘法或拓扑学技术对原油样品建模，但存在对馏分性质检测精度低以及求解稳健性不足等问题。针对炼化企业混合原油馏分性质检测，设计了具有组分跟踪的混合原油馏分性质检测方法，基于近红外光谱，通过组分跟踪技术压缩混合原油中组分原油种类以及各自比例的求解范围，提升检测速度及稳健性，最后基于比例及组分原油性质预估混合原油馏分性质。结果表明，相较于混合原油性质检测现有方法，采用该方法可使混合原油馏分性质检测更加准确，并能有效减少优化求解范围，有利于稳定产品质量，提升企业经济效益。     

近红外动目标CCD成像跟踪系统

介绍了以电荷耦合面阵器件作为图像探测器的近红外动目标成像跟踪系统。在系统中采用了一些关键技术，从而使系统能够对近红外动目标高质量成像，并能进行有效准确的定位跟踪。     

面向量子博弈论的光量子芯片设计及实验

量子博弈论是量子信息和经典博弈论的交叉研究方向。理论研究表明，量子博弈模型不仅能够突破经典博弈模型的收益上限，更是有望用于深入理解和突破量子通信、量子计算等领域的很多基础问题。针对一种利益冲突的贝叶斯量子博弈模型，提出了一种可编程的光量子芯片结构，首次运用硅基光量子芯片实验完成了量子博弈实验。通过动态生成和调控片上量子纠缠态，实验证实了量子博弈相对经典博弈的博弈优势，展示了光量子芯片在量子博弈论研究中的重要作用，为量子信息领域更复杂问题的研究提供了重要的实验手段。     

量子点荧光标记技术在生物医学领域的应用

量子点作为一种新型的纳米荧光材料在生物学领域的应用已引起国际上的普遍关注,量子点独特的荧光性能使其在这一领域的应用有着无可比拟的优越性.该文主要就量子点的荧光性能,基于量子点标记的生物荧光探针的制备及其在生物医学领域中的应用研究进展作一概述.     

水溶性量子点CdSe／ZnS与蛋白质非特异性相互作用研究

研究了巯基乙酸修饰的水溶性量子点CdSe/ZnS与不同蛋白质的非特异性相互作用.发现牛血清白蛋白,卵清蛋白,血红蛋白和免疫球蛋白G均能增强巯基乙酸修饰的水溶性量子点CdSe/ZnS的荧光,而细胞色素C却使量子点的荧光猝灭;探讨了量子点与蛋白质作用导致荧光强度变化的原因.这些结果表明,在使用巯基乙酸修饰的水溶性CdSe/ZnS量子点作为生物探针时,必须要考虑不同蛋白质与量子点的非特异性相互作用.     

Amplification of fluorescence emission of CdSe/ZnS QDs entrapped in a sol-gel matrix, a new approach for detection of trace level of PAHs

In this work, CdSe/ZnS core/shell QDs with emission wavelengths of 535 nm, 545 nm, 555 nm and 575 nm were synthesized and the ligands on their surface were exchanged with mercaptopropionic acid (MPA) to make them water-soluble. Hydrophilic QDs were incorporated into a sol-gel GA matrix of 3-aminopropyl trimethoxysilane (APTMS) and 3-glycidoxypropyl trimethoxysilane (GPTMS) to fabricate QD-entrapped membranes. The fluorescence intensity of the QDs entrapped in the sol-gel membrane was increased after being activated by the energy transfer from polycyclic aromatic hydrocarbon compounds (PAHs). The signal increase of the QDs was proportional to the increase in the concentration of the PAHs. Herein, trace levels of anthracene (ANT), phenanthrene (PHE) and pyrene (PYR) were detected through the enhancement of the fluorescence intensity of the CdSe/ZnS QD-entrapped membranes. The linear detection ranges were 0.01-0.1 μM for ANT and PHE and 0.005-0.05 μM for PYR. The QD-entrapped sol-gel membranes also showed quite good stability for the detection of PAHs over a period of 2 months.     

Effects of K and Na ions on the fluorescence of colloidal CdSe/CdS and CdSe/ZnS quantum dots

In this work we studied the effects of K⁺ and Na⁺ ions on fluorescence properties of the colloidal quantum dots (QDs). It was found that the fluorescence intensity was enhanced when low concentration of ions was introduced to QD solutions, while it became weakened when ion concentrations reached their physiological levels in many bio environments. Such fluorescence changes can be attributed to interactions between QD surface ligands and ions as well as the Coulomb potential of ions that displaces the wave functions of the electron and hole confined inside the QD. These results are important for understanding the influence of different biological environments, such as extracellular and intracellular compartments, on optical properties of colloidal QDs.     

基于CdSe/ZnS核壳型量子点的高灵敏、快速细菌计数新方法的研究

以乙酰丙酮镉和硬脂酸锌为前驱体,合成了巯基丙酸修饰的CdSe/ZnS核壳型量子点(QDs)。并将其作为荧光探针,以金黄色葡萄球菌(S.aureus)为目标细菌,建立了一种高灵敏的、简单快速的细菌计数新方法,并借助荧光显微镜成功的进行成像探测研究。通过考察量子点浓度、孵育时间等因素的影响,确定了细菌定量检测的最佳条件。在最优化的实验条件下,体系的相对荧光强度随细菌数量的增加而增大。该方法的线性范围为102CFU/mL～106CFU/mL,检测限为102CFU/mL,线性回归方程为Y=427.586X-677.022(R=0.996 49)。本方法有效克服了传统的细菌计数方法存在的缺陷,具有较高的灵敏度和较好的重现性(实际样品检测的RSD=3.6%～8.1%),且操作简单、检测时间短、成本低,有很好的潜在应用价值。     

Vein detection with near‐infrared organic photodetectors for biometric authentication

We combine a low dark current and high‐detectivity near‐infrared (NIR)‐sensitive organic photodetector with a high‐resolution 508 pixels per inch (ppi) oxide thin‐film transistor (TFT) backplane to create a large‐area thin NIR detector, using processes that are compatible with flat‐panel display fabrication. The detector is characterized showing high uniformity and linearity. With the use of a NIR light source, the detector is capable of imaging the (pattern of) veins under the skin in reflection, leading to improved biometric authentication.     

Embedding PbS quantum dots in MAPbCl0.5Br2.5 perovskite single crystal for near-infrared detection

Organometal lead-halide perovskites demonstrate excellent photovoltaic performance in the visible (Vis) region. However, their lack of response in the near-infrared (NIR) region limits their applications in broadband photodetectors. Here, PbS quantum dots (QDs) were successfully embedded in MAPbCl_0.5Br_2.5 single crystal by solution-processed epitaxial growth. Perovskites can effectively act as efficient charge transporters. The mobility of MAPbCl_0.5Br_2.5 with 1 wt% PbS quantum dots crystal is about 85 cm²/(V·S), which is similar to that of pure MAPbCl_0.5Br_2.5 single crystal. Meanwhile, the composite crystal exhibited a broadband response wavelength from the Vis (625 nm) to the NIR (850 nm) with fast response times (τ_on/τ_off = 6/9 ms and τ_on/τ_off = 19/43 ms in the Vis and NIR regions, respectively). The photodetector demonstrates a responsivity of 0.02 mA/W with a specific detectivity of 1.2 × 10⁸ Jones under 5.1 mW/cm² 850-nm illumination at 10-V bias. These results suggest that we have obtained high-quality composite crystals with improved photo response in the Vis to NIR region.     

量子点生物传感器及其在生物医学分析检测中的应用

综述了基于新型半导体纳米材料—量子点(QDs)的各种生物传感器如荧光生物传感器、微流控芯片生物传感器、光纤生物传感器、适配体生物传感器、分子马达生物传感器等的原理、特点,并对其在生物医学分析检测中的应用与其发展的局限性与发展前景进行了综述.     

Illumination invariant face recognition using near-infrared images

Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups     

Improvement of the visibility of concealed features in artwork NIR reflectograms by information separation

Near Infrared (NIR) reflectography, coupled to visible (VIS) one, is a spectrophotometric imaging technique employed to probe both the inner and the outer layers of artworks. NIR reflectograms may partially contain information pertinent to the visible spectrum (due to the poor pigment transparency in NIR) and this decreases their comprehensibility. This work presents an innovative digital processing methodology for accentuating information contained in the infrared reflectograms. The proposed method consists of inducing minor changes in pixel intensity by suppressing VIS information content from NIR information content. The method creates such enhanced NIR reflectogram by extrapolating VIS reflectogram to a reflectogram recorded in NIR range and by subtracting it from the measured values in the near infrared spectral sub-band. As an extrapolator we suggest a feed forward artificial neural network (ANN). Significant results of improved visualization are exemplified on reflectograms acquired with a VIS-NIR $〈400,2250〉\text{nm}$ scanning device on real paintings such as Madonna dei Fusi attributed to Leonardo da Vinci. Parameters of the method, artificial neural network and separability of used pigments are discussed.