Perovskite-Like Mixed Oxides (LaSrMn1-x Ni (x) O4+delta, 0 a parts per thousand currency sign x a parts per thousand currency sign 1) as Catalyst for Catalytic NO Decomposition: TPD and TPR Studies

Catalytic NO decomposition on LaSrMn1-x Ni (x) O4+delta (0 a parts per thousand currency sign x a parts per thousand currency sign 1) is investigated. The activity of NO decomposition increases dramatically after the substitution of Ni for Mn, but decreases when Mn is completely replaced by Ni (x = 1.0). The optimum value is at x = 0.8. These indicate that the catalytic performance of the samples is contributed by the synergistic effect of Mn and Ni. O-2-TPD and H-2-TPR experiments are carried out to explain the change of activity. The former indicates that only when oxygen vacancy is created, could the catalyst show enhanced activity for NO decomposition; the latter suggests that the best activity is obtained from catalyst with the most matched redox potentials (in this work, the biggest Delta T and Delta E values).

Au nanoparticles grafted sandwich platform used amplified small molecule electrochemical aptasensor

We report a sensitively amplified electrochemical aptasensor using adenosine triphosphate (ATP) as a model. ATP is a multifunctional nucleotide thatis most important as a "molecular currency" of intracellular energy transfer. In the sensing process, duplexes consisting of partly complementary strand (PCS1), ATP aptamer (ABA) and another partly complementary strand (PCS2) were immobilized onto Au electrode through the 5'-HS on the PCS1. Meanwhile, PCS2 was grafted with the Au nanoparticles (AuNPs) to amplify the detection signals. In the absence of ATP, probe methylene blue (MB) bound to the DNA duplexes and also bound to guanine bases specifically to produce a strong differential pulse voltammetry (DPV) signal. But when ATP exists, the ABA-PCS2 or ABA-PCS1 part duplexes might be destroyed, which decreased the amount of MB on the electrode and led to obviously decreased DPV signal.

Field-amplified sample stacking capillary electrophoresis with electrochemiluminescence applied to the determination of illicit drugs on banknotes

Capillary electrophoresis (CE) with Ru(bpy)(3)(2+) electrochemiluminescence. (ECL) detection system was established to the determination of contamination of banknotes with controlled drugs and a high efficiency on-column field-amplified sample stacking (FASS) technique was also optimized to increase the ECL intensity. The method was illustrated using heroin and cocaine, which are two typical and popular illicit drugs. Highest sample stacking was obtained when 0.01 mM acetic acid was chosen for sample dissolution with electrokinetical injection for 6 s at 17 kV. Under the optimized conditions: ECL detection at 1.2 V, separation voltage 10.0 kV, 20 mM phosphate-acetate (pH 7.2) as running buffer, 5 mM Ru(bpy)(3)(2+) with 50 mM phosphate-acetate (pH 7.2) in the detection cell, the standard curves were linear in the range of 7.50 x 10(-8) to 1.00 x 10(-5) M for heroin and 2.50 x 10(-7) to 1.00 x 10(-4) M for cocaine and detection limits of 50 nM for heroin and 60 nM for cocaine were achieved (S/N = 3), respectively. Relative standard derivations of the ECL intensity and the migration time were 3.50 and 0.51% for heroin and 4.44 and 0.12% for cocaine, respectively.The developed method was successfully applied to the determination of heroin and cocaine on illicit drug contaminated banknotes without any damage of the paper currency.

纸币号码自动识别方法研究

纸币广泛地应用于人们的日常活动中，为了防伪纸币都统一印制了号码，每个印刷号码对应唯一的纸币印品。对印刷号码的检查普遍都是人工完成的，在长时间的检查工作中，很容易出现检测差错，而且效率低下。因此，对印刷号码自动检测识别系统进行研究是很必要的。印刷号码自动检测技术能降低生产成本，增强竞争力，具有很大的社会经济效益和应用前景。 本文针对人民币纸币印刷过程中对识别速度的特殊需求，开展快速纸币号码自动识别方法研究，且仅涉及号码中数字的识别。由于印刷过程中识别的对象为新币，且有良好的光照条件，因此，省略了图像预处理，仅通过字符定位分割和号码识别两个步骤实现纸币号码的自动识别。为提高处理速度，在号码分割定位中利用改进的投影法，避开图像倾斜校正环节，节省了分割时间；在号码识别阶段，提出了一种号码快速识别方法，综合利用数字的开口特征、数字的宽高比、穿线特征等多种特征直接识别定位好的数字。 为验证识别算法，采用以TMS320 C64系列DSP为处理器的VC4018型智能相机作为图像传感器和图像处理器，利用CCS环形光源作为照明设备，集成纸币传送装置和光电开关自动触发，建立了基于智能相机的纸币号码自动识别演示验证实验系统。 在良好光照条件下，对672张100元新人民币采集了10080幅图像，经测试，定位识别一张倾角在±10°范围内的纸币号码的时间在4ms以内，识别率为100%。实验结果表明，该算法简便，识别速度快，识别率高，且允许图像有较大的倾斜，可以满足实际工程在处理速度和识别率方面的需要。