Gui Yanghai, Xie Changsheng, Xu Jiaqiang, Wang Guoqing
Henan Provincial Key Laboratory of Surface & Interface Science, Zhengzhou University of Light Industry, Zhengzhou, PR China.
J Hazard Mater. 2009 May 30;164(2-3):1030-5. doi: 10.1016/j.jhazmat.2008.09.011. Epub 2008 Sep 10.
In the present study, four explosives of NH(4)NO(3), mineral explosives (ME), picric acid (PA) and 2,6-dinitrotoluene (2,6-DNT) have been investigated by using ZnO-doped nanoparticle sensors with additives of Sb(2)O(3), TiO(2), V(2)O(5) and WO(3). Firstly, eighteen ZnO-doped nanoparticle sensors were optimized and selected six best sensors to compose a new optimized array. Then, the detection capability of the sensor array was studied by using static sampling method. The results showed that with the increase in concentration of samples, the sensitivities of the sensors also increased, and the lowest detection limit of the four samples were low to 3.34 microg/L. At last, for the sake of approaching closer practical application, these four explosives were also studied with full dynamic sampling method and the results demonstrated that all the samples could be well identified completely at the concentration of 15.4 microg/L when maximum values of slope were extracted as the feature parameters to DFA analysis.
在本研究中，使用掺杂ZnO的纳米颗粒传感器，并添加Sb₂O₃、TiO₂、V₂O₅和WO₃，对四种炸药——硝酸铵（NH₄NO₃）、矿用炸药（ME）、苦味酸（PA）和2,6-二硝基甲苯（2,6-DNT）进行了研究。首先，对18个掺杂ZnO的纳米颗粒传感器进行优化，选出6个最佳传感器组成一个新的优化阵列。然后，采用静态采样方法研究了传感器阵列的检测能力。结果表明，随着样品浓度的增加，传感器的灵敏度也增加，四种样品的最低检测限低至3.34μg/L。最后，为了更接近实际应用，还采用全动态采样方法对这四种炸药进行了研究，结果表明，当提取斜率最大值作为DFA分析的特征参数时，所有样品在浓度为15.4μg/L时都能被很好地完全识别。
