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首页> 外文期刊>The journal of physical chemistry, C. Nanomaterials and interfaces >Theory for Cyclic Staircase Voltammetry of Two Step Charge Transfer Mechanism at Rough Electrodes
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Theory for Cyclic Staircase Voltammetry of Two Step Charge Transfer Mechanism at Rough Electrodes

机译:粗糙电极两步电荷转移机理的循环阶梯伏安理论

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We have developed theory for cyclic (staircase) voltammetry (CSCV) of a two step reversible charge transfer (EE) mechanism for redox species with unequal diffusion coefficients at rough electrodes. The various surface micros copies provide details of random morphology of the electrode which is characterized through the power spectrum in our theory. For the finite fractal electrode model, anomalous enhancement and scan rate dependence of the CSCV or CV response is caused by fractal dimension (D-H), lower length scale of fractality (l), and topothesy length (l(r)). The peak current corresponding to both charge transfer steps is enhanced with an increase in roughness of the fractal electrode (through increase in D-H or l(r), or decrease in l). The onset and offset of the anomalous regime is controlled by l(tau) and l, respectively. Results show that the electrode roughness has the potential to enhance the sensitivity of CSCV as an analytical technique. This is due to a decrease in the difference between the peak currents of two charge transfer steps and an increase in the ratio of the peak to the valley (minimum existing between the two peaks) current in the presence of roughness. Finally, we show that not accounting for roughness in data analysis may cause errors in estimation of composition, diffusion coefficient, improper assignment of electrode mechanism, and so forth.
机译:我们已经开发出了一种用于循环(楼梯)伏安法(CSCV)的理论,该理论用于在粗糙电极上具有不相等扩散系数的氧化还原物质的两步可逆电荷转移(EE)机制。各种表面微观副本提供了电极随机形态的详细信息,这通过我们理论中的功率谱来表征。对于有限的分形电极模型,CSCV或CV响应的异常增强和扫描速率依赖性是由分形维数(D-H),分形的较低长度标度(l)和总长度(l(r))引起的。随着分形电极粗糙度的增加(通过增加D-H或l(r)或减少l),对应于两个电荷转移步骤的峰值电流会增加。异常状态的发生和偏移分别由l(tau)和l控制。结果表明,作为一种分析技术,电极粗糙度具有增强CSCV灵敏度的潜力。这是由于在存在粗糙度的情况下,两个电荷转移步骤的峰值电流之间的差异减小,以及峰值与谷值的比率(两个峰值之间存在的最小值)的增加。最后,我们表明,在数据分析中不考虑粗糙度可能会导致组成估算,扩散系数和电极机构分配不当等​​方面的错误。

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