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Abstract：
Enzymatic biofuel cell (EBFC)-based self-powered biochemical sensors obviate the need for external power sources thus enabling device miniaturization. While recent efforts driven by experimentalists illustrate the potential of EBFC-based sensors for real-time monitoring of physiologically relevant biochemicals, a robust mathematical model that quantifies the contributions of sensor components and empowers experimentalists to predict sensor performance is missing. In this paper, we provide an elegant yet simple equivalent circuit model that captures the complex, three-dimensional interplay among coupled catalytic redox reactions occurring in an EBFC-based sensor and predicts its output signal with high correlations to experimental observations. The model explains the trade-off among chemical design parameters such as the surface density of enzymes, various reaction constants as well as electrical parameters in the Butler-Volmer relationship. The model shows that the linear dynamic range and sensitivity of the EBFC-based sensor can be independently fine-tuned by changing the surface density of enzymes and electron mediators at the anode and by enhancing reductant concentrations at the cathode. The mathematical model derived in this work can be easily adapted to understand a wide range of two-electrode systems, including sensors, fuel cells, and energy storage devices.
摘要：
基于酶生物燃料电池(EBFC)的自供电生化传感器消除了对外部电源的需要,从而使得装置能够小型化。虽然最近由实验者推动的努力说明了基于EBFC的传感器用于实时监测生理相关生化物质的潜力，一个强大的数学模型，量化传感器组件的贡献，使实验人员能够预测传感器性能缺失。在本文中,我们提供了一个优雅而简单的等效电路模型，捕捉复杂的，在基于EBFC的传感器中发生的耦合催化氧化还原反应之间的三维相互作用，并预测其输出信号与实验观察结果高度相关。该模型解释了化学设计参数之间的权衡，例如酶的表面密度，Butler-Volmer关系中的各种反应常数以及电参数。该模型表明，基于EBFC的传感器的线性动态范围和灵敏度可以通过改变阳极处的酶和电子介体的表面密度以及通过提高阴极处的还原剂浓度来独立地微调。在这项工作中得出的数学模型可以很容易地适应理解广泛的双电极系统，包括传感器，燃料电池,和储能装置。
