生物质热解制备木醋液及其性质研究

对杉木屑、棉杆、竹屑三种生物质热解制得的木醋液产率、基本理化性质及其有机成分进行了分析研究。结果表明,三种生物质原料在350℃下热解制得的粗木醋液、精制木醋液产率相差不大,相同条件下氯化钾浸渍处理后的杉木屑热解所得的粗木醋液、精制木醋液的产率有所降低。三种原料制得的精制木醋液的理化性质不同,杉木屑木醋液的pH值最小,密度最大,竹屑和棉杆木醋液的有机酸含量相对较高。采用GC-MS对精制木醋液中的有机成分进行了分析,结果表明,杉木屑木醋液中的主要组分为酸类、酚类和酮类化合物,棉杆和竹屑木醋液中的主要组分除这三类有机物质外,还含有相对含量较高的醇类化合物。酸类和酚类化合物在三种木醋液中的相对含量依次是竹屑 >棉杆 >木屑;酮类的相对含量依次是木屑 >棉杆 >竹屑。氯化钾处理后的木屑热解所得的木醋液中主要组分酚类和酮类化合物的相对含量有所降低,酸类化合物的相对含量增加,主要表现为乙酸相对含量的增加。与杉木屑木醋液相比,KCl处理后的杉木屑木醋液中的醇类化合物相对含量增加了1倍左右。     

Correlation‐assisted nearest shrunken centroid classifier with applications for high dimensional spectral data

High throughput data are frequently observed in contemporary chemical studies. Classification through spectral information is an important issue in chemometrics. Linear discriminant analysis (LDA) fails in the large‐p‐small‐n situation for two main reasons: (1) the sample covariance matrix is singular when p > n and (2) there is an accumulation of noise in the estimation of the class centroid in high dimensional feature space. The Independence Rule is a class of methods used to overcome these drawbacks by ignoring the correlation information between spectral variables. However, a strong correlation is an essential characteristic of spectral data. We proposed a new correlation‐assisted nearest shrunken centroid classifier (CA‐NSC) to incorporate correlation information into the classification. CA‐NSC combines two sources of information [class centroid (mean) and correlation structure (variance)] to generate the classification. We used two real data analyses and a simulation study to verify our CA‐NSC method. In addition to NSC, we also performed a comparison with the soft independent modeling of class analogy (SIMCA) approach, which uses only correlation structure information for classification. The results show that CA‐NSC consistently improves on NSC and SIMCA. The misclassification rate of CA‐NSC is reduced by almost half compared with NSC in one of the real data analyses. Generally, correlation among variables will worsen the performance of NSC, even though the discriminatory information contained in the class centroid remains unchanged. If only correlation structure information is used (as in the case of SIMCA), the result will be satisfactory only when the correlation structure alone can provide sufficient information for classification. Copyright © 2015 John Wiley & Sons, Ltd.     

DOA estimation for uniform circular array without the source number based on beamspace transform and higher-order cumulant

Fourth-order cumulant is one of most widely used high-order cumulant for direction of arrival (DOA) estimation due to its ability of expanding the virtual array aperture as well as suppressing Gaussian noise. To address the two-dimensional (2D) DOA estimation problem, we propose a modified MUSIC scheme for uniform circular array (UCA) in this paper. Firstly, the fourth-order cumulant of UCA is considered to construct a new propagator, resulting in the elimination of a priori knowledge of the number of signals. Secondly, the UCA is transformed by beamspace transformation, reducing the time computational complexity of the algorithm since the two-dimensional grid search and singular value decomposition are avoided. And finally a low-rank recovery algorithm is adopted to improve the accuracy regarding the limited snapshots scenario. The numerical simulations validate the superiority of the proposed method.     

显微共聚焦拉曼光谱结合群分析的黑色直液笔墨迹鉴别研究

黑色直液笔是一种新型书写工具,目前对该种笔墨迹的相关研究较少。为给文件检验工作中墨迹的分析提供新的参考依据,本实验使用显微共聚焦拉曼光谱技术,采集了30支不同品牌、型号的黑色直液笔墨迹光谱数据,进行Savitzky-Golay卷积平滑处理后,依据光谱图的拉曼位移及拉曼谱峰差异对墨迹进行初步分析。设置聚类方法为组间联接,区间距离测量方式为平方欧式距离,对采集的光谱数据进行群分析,将30支黑色直液笔墨迹样本分成了3类,并与品牌建立了相关联系;同时通过主成分分析验证了群分析的可靠性和准确性。研究表明,显微共聚焦拉曼光谱技术结合群分析方法可实现对黑色直液笔墨迹的无损分析及有效鉴别,该方法操作简便、结果准确,适用于法庭科学文件检验。     

Fabrication and Characterization of Zinc Oxide Nanowire Based Two-electrode Capacitive Biosensors on Flexible Substrates for Estimating Glucose Content in a Sample

The present work deals with fabrication and characterization of the zinc oxide (ZnO) nanowire based novel two-electrode capacitive biosensors on flexible Polyethylene terephthalate (PET) substrates for accurate estimation of glucose by analyzing the fundamental dielectric nature of the relevant sample. The morphology and crystalline quality of grown nanowires are analyzed by using field-emission scanning electron microscope (FESEM) and X-ray diffractometer (XRD), respectively. Current and capacitance values of the device have been studied for ten different glucose concentrations relevant to the physiological standards. The analytical performance of the devices in terms of enzyme activity, reliability and flexibility has also been evaluated.     

过渡态理论在化学体系中的2个应用实例*

在大学本科一二年级的物理化学教学中,化学动力学章节的学习涉及到对不同时间尺度的认识。本文利用过渡态理论得到的速率常数表达公式,以乙烷分子碳/碳单键旋转和水分子氢键交换的动力学过程为例,对这2个应用实例的动力学过程所发生的时间尺度做出估算。这些时间尺度的估算对于学生理解不同动力学过程的物理图像至关重要,同时也有助于加强学生对重要公式的理解并能够将这些公式在化学体系中灵活运用。     

One‐Pot Synthesis of 4‐Aryl‐NH‐1,2,3‐Triazoles through Three‐Component Reaction of Aldehydes,Nitroalkanes and NaN3

A one‐pot three‐component reaction of aldehydes, nitroalkanes and NaN₃ for the synthesis of NH ‐1,2,3‐triazoles has been developed. The reaction provides a safe, efficient and step‐economic approach for the synthesis of various NH ‐1,2,3‐triazoles in good to excellent yields.     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

高效液相色谱法同时测定化妆品中的10种美白活性成分及2种禁用成分

建立了同时测定化妆品中10种美白活性成分及2种禁用成分的高效液相色谱分析方法。水基、乳液等含油脂较少的样品采用0.02 mol/L磷酸二氢钾溶液(pH 6.0)直接提取;油脂含量高的样品及蜡基、粉基类的样品先加入2.5 m L二氯甲烷溶解后再用0.02 mol/L磷酸二氢钾溶液(pH 6.0)提取。提取液在9 500r/min下离心后用0.22μm滤膜过滤。样品采用Eclipse XDB-C_(18)色谱柱为固定相,以0.02 mol/L磷酸二氢钾溶液(pH 6.0)和甲醇溶液为流动相,梯度洗脱,流速为1.0 m L/min,柱温为25℃,使用二极管阵列检测器(DAD)进行检测,检测波长为230 nm和250 nm,外标法定量。结果显示:12种化合物在2.5~100 mg/L范围内线性关系良好,相关系数(r)均大于0.999 0。方法的定量下限(以信噪比为10计)为0.006 5%~0.025%,添加水平为0.025%~0.5%时回收率为87%~102%,相对标准偏差均小于4%。该方法前处理简单、回收率高、精密度好,适用于化妆品中10种美白活性成分及2种禁用成分的快速测定。     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。