自支撑柔性氮掺杂碳织物电极的制备与性能研究

本文以纯棉织物为基底,吡咯单体为氮源,采用简单的原位聚合-高温煅烧的方法制备了自支撑柔性氮掺杂织物（N-CT）. 利用傅立叶红外技术、X射线光电子能谱、比表面积测试、扫描电子显微镜对所得产物进行结构与形貌表征. 结果表明,碳化后聚吡咯主要以纳米碳球包覆在碳织物表面,N-CT电极的比表面积为495.0 m²·g^-1,其含氮量为2.26%. 电化学测试表明,在0.5 A·g^-1的电流密度下N-CT电极的比电容器为256.2 F·g^-1,经过5000次的恒流充放电循环后电容保持率为98.3%,库伦效率保持率在98.8%左右,具有良好的柔性和机械性能.     

酚醛层压材料的冲击力学行为及本构模型

为了研究酚醛层压材料的冲击力学行为并获得本构模型,利用万能试验机和整形修正的分离式霍普金森压杆(SHPB)装置,对材料试样进行了应变率范围为10-3~103 s-1的单轴压缩实验,得到了不同加载应变率下的应力应变曲线,对其在准静态、动态载荷下的压缩破坏机理进行了初步探讨。结果表明,酚醛层压材料具有较强的应变率效应,与准静态(1.67×10-3 s-1)时相比,在动态载荷(7×102 s-1)下,峰值应力增加了约10倍;破坏应变减少了约一半;在准静态和动态加载条件下试样力学性能的差异是由于纤维基体界面特性以及不同应变率下破坏模式的不同;采用朱-王-唐本构方程描述了酚醛层压材料力学行为,拟合得到了本构方程的系数,在加载过程中,理论计算值与实验结果吻合较好。     

净分析信号算法用于近红外模型优化的研究

将净分析信号概念引入多元复杂的银杏叶提取物分析中,利用净分析信号算法对近红外光谱图进行处理,并用净分析算法结合二维相关光谱算法对模型进行了优化,最终建立槲皮素、山柰素和异鼠李素三种组分同时测定的近红外定量模型。利用两种净分析信号算法(Lorber的NAS算法和Goicoechea 与 Olivieri的HLA/GO算法)作为光谱预处理方法,用一系列主因子重构后的光谱图建立校正模型,并通过净分析信号-二维相关光谱算法对图谱中主对角线处的波段进行选择,优化模型建立所采用的波段。所采用的两种基于净分析信号的预处理方法成功的应用于银杏叶中三种黄酮：槲皮素,山奈素和异鼠李素含量的同时测定。净分析信号预处理方法显著降低了建模所需主因子数,提高模型的稳健性。净分析信号-二维相关光谱算法能够优化建模波段,提高模型的预测能力。结论：净分析信号算法能够应用于近红外光谱模型预处理和优化过程,在复杂多组分且干扰未知植物药含量分析的研究中具有实际应用价值。     

Electric Field‐Aided Formation Combined with a Nanoimprinting Technique for Replicating a Plant Leaf

The surface of the taro plant leaf was replicated using a nanoimprinting technique (NIT) supplemented with an electric field. This field‐aided nanoimprinting method (FA‐NIT) consists of two steps: applying an electric field to a liquid polymer under the plant leaves and the curing process of the polymer with the applied electric field. An appropriate electric field was needed to induce the electrokinetic phenomena of a liquid polymer to obtain a good replicated surface. The roughness fabricated by the FA‐NIT was about 45% higher than the one prepared by NIT. The FA‐NIT method is a good supplementary technique to improve the quality of NIT.

     

Cotton Linters: An Alternative Cellulosic Raw Material

Summary: Cellulose in fibrous or in chemically modified form plays a relevant part in products for industrial applications and in products which are used in human daily life. Besides wood, cotton is an important natural source for cellulose. Linters are a by-product of the oil mills. The position of the linters in the technology of cotton is explained. In linters bleaching plant, the raw linters are purified mechanically and chemically. The principle of this process is shown. The resulting bleached linters (cotton linters pulp, CLP/cotton linters cellulose, CLC) have an alpha-cellulose content of about 99%. Due to their purity and to their properties, the cotton linters pulp is the cellulosic basis for a large number of special and niche products in the paper and chemical industry.     

流动注射化学发光法测定水样中痕量锡

在HCl介质中,KMnO4 甲醛 Sn(Ⅱ)为强化学发光体系,利用流动注射和巯基棉分离技术建立了测定锡的新方法,探讨了发光反应的机理。方法的线性范围为0.1～30μg L,检出限为0.04μg L,对1.0μg L的Sn(Ⅱ)标准溶液连续11次测定的相对标准偏差为2.1%。方法已用于环境水样中痕量锡的测定。     

Polar Constituents from Sageretia Thea Leaf Characterized by HPLC‐SPE‐NMR Assisted Approaches

Nine glycosides ( 1–9 ) were characterized from the n‐butanol‐soluble fraction of the ethanolic extract of the leaves of Sageretia thea by the general approach. Among these, Compounds 6 and 7 were identified as a mixture. Application of HPLC‐SPE‐NMR in two selected fractions led to the separation of this mixture and the characterization of three additional minors ( 10–12 ). Among these, 7‐O‐methylmyricetin 3‐O‐α‐l ‐arabinofuranoside ( 8 ) is a new natural product and eight compounds, i.e. glucofragulin A ( 1 ), quercetin‐3‐O‐α‐l ‐arabinopyranoside ( 5 ), 3‐O‐β‐d ‐galactopyranoside ( 6 ), 3‐O‐β‐d ‐glucopyranoside ( 7 ), and 3‐O‐α‐l ‐arabinofuranoside ( 11 ), myricetin‐3‐O‐α‐l ‐arabinofuranoside ( 9 ) and 3‐O‐β‐d‐glucopyranoside ( 10 ), and quercetrin ( 12 ), are found for the first time from the title plant.     

TCF selenium preconcentration in geological materials for determination at sub-mugg(-1) with INAA (Se/TCF-INAA)

In geological samples, Se concentration ranges from 1 × 10⁻⁹ g g⁻¹ up to 1 × 10⁻³ g g⁻¹. The analytical difficulty at low concentration (<1 μg g⁻¹), is one of the main reasons why the geological cycle of Se is poorly known. The analytical method that consisted of preconcentration of Se with thiol cotton fiber (TCF) followed by graphite furnace atomic absorption spectrometry (GFAAS) has been modified by finishing with instrumental neutron activation analysis (INAA). The modified technique involves sample dissolution (HF-HNO₃-H₂O₂) and evaporation to dryness at low temperature (55-60 °C) to avoid selenium volatilization. Se^VI is converted to Se^IV by adding 6 M HCl to the dry residuum and the solution is then heated in a covered boiling bath (95-100 °C). The solution is diluted to obtain 0.6 M HCl and then collected on TCF. The TCF is placed in a polyethylene vial for irradiation in the SLOWPOKE II reactor (Montréal) for 30 s at a neutron flux of 10¹⁵ m⁻² s⁻¹. The 162 keV peak of ^77mSe (half-life 17.36 s) is read for 20 s after a decay of 7 s. The amount of sample to be dissolved is controlled by two competing effects. To obtain low detection limits, a larger amount of sample should be dissolved. On the other hand, the TCF could become saturated with chalcophile elements when large sample is used. Sulfur is a good indicator of the amount of Se and chalcophile elements present. In S poor sample (<100 μg g⁻¹) 3.0 g of sample was used and the L_D was ∼2 ng g⁻¹. In S high samples (>1.5% S) 0.05 g of sample was used and the L_D was ∼120 ng g⁻¹. The present work also includes suggested Se concentration for eight international geological reference materials (IGRM) that compare favorably with literature values.     

玉米叶片的光谱响应及其氮素含量预测研究

以不同施肥水平下两年玉米田间试验为基础,利用高光谱技术探讨大喇叭口期不同层次玉米叶片光谱响应的敏感区域,并依据叶片氮素含量与原始光谱反射率及其一阶导数的相关性,最终构建了叶片氮素含量的预测模型。结果表明：不同施肥水平下叶片光谱反射率差别明显区域集中在550 nm附近波段、761～1 300 nm波段,不同层次间叶片光谱反射率差别明显区域集中在550 nm附近波段,叶片氮素含量与470～760 nm波段光谱反射率及其一阶导数呈极显著相关。经过对比筛选,以光谱指数DSI(564,681)和DSI(681,707)构建的指数预测模型效果最好,预测精度达93.43%和93.39%,能有效估测叶片氮素含量。     

基于近红外光谱技术的小麦条锈病和叶锈病的早期诊断

为实现小麦条锈病和叶锈病的早期诊断,利用近红外光谱技术结合定性偏最小二乘法(DPLS)建立了一种鉴别这两种病害的方法。试验将150片小麦叶片(健康叶片、条锈病潜育叶片、条锈病发病叶片、叶锈病潜育叶片、叶锈病发病叶片各30片)分为5类,扫描获得近红外光谱,建立小麦叶片DPLS近红外光谱鉴别模型。原始光谱数据经二阶导数处理后,在4 000～8 000 cm-1范围内,当利用不同建模比建模时,建模集的平均识别率为96.56％,检验集的平均识别率为91.85％,证明了模型的稳定性。当建模比为2∶1、主成分数为10时,模型识别效果较好,建模集的识别准确率为97.00%,检验集的识别准确率为96.00%。表明应用近红外光谱技术建立的小麦条锈病和叶锈病早期诊断的定性鉴别方法是可行的。