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Sensitivity enhancements in stripping voltammetry from exposure to low frequency sound

机译:暴露于低频声音中的溶出伏安法的灵敏度增强

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Improving the sensitivity of voltammetric analysis containing low metal concentrations has been attempted by exposure to low frequency sound. It was found that audible sound in the low frequency range, around 100 Hz was very effective for this purpose, with an increase of the analytical signal up to about 300%, without any significant increase in the corresponding noise level. Ordinary stirring has to be avoided during the voltammetric scanning due to the low reproducibility when stirring takes place. Sound exposure, however, can be performed during the entire measurement, and this explains the increased sensitivity. Further it is concluded that sound exposure close to the resonant frequency, has improvement effects at least comparable to stirring, and thus all mechanical stirring can be avoided. Differential pulse anodic stripping voltammetry with a three-electrode cell system was used with a static mercury drop electrode exposed by low frequency sound, a Pt counter-electrode and a silver/silver chloride/saturated silver chloride/saturated potassium chloride reference electrode. However, it is presumed that other modes of voltammetry than DPASV would show similar behavior when exposed to audible sound. Low frequency sound is assumed to influence the physical conditions in the vicinity of the electrode by diffusion layer thinning, to increase the surface concentration. Moreover, the simplicity of using audible sound, with high reproducibility of the measurements, makes this analytical method potentially attractive. [References: 41]
机译:已经尝试通过暴露于低频声音来提高包含低金属浓度的伏安法分析的灵敏度。已经发现,在大约100 Hz的低频范围内的可听声音对此非常有效,分析信号的增加高达约300%,而相应的噪声水平却没有任何明显的增加。由于在进行搅拌时重现性低,所以在伏安扫描期间必须避免常规搅拌。但是,可以在整个测量过程中进行声音暴露,这说明了灵敏度的提高。进一步的结论是,接近共振频率的声音暴露具有至少与搅拌可比的改善效果,因此可以避免所有机械搅拌。使用三电极电池系统的差分脉冲阳极溶出伏安法,静电汞滴电极暴露于低频声中,Pt对电极,银/氯化银/饱和氯化银/饱和氯化钾参比电极。但是,可以假定,除DPASV之外,伏安法的其他模式在暴露于可听声音时也会显示出类似的行为。假定低频声音会通过扩散层变薄而影响电极附近的物理条件,从而增加表面浓度。此外,使用可听声音的简单性以及测量结果的高可重复性使这种分析方法具有潜在的吸引力。 [参考:41]

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