Modeling of pCO2 Point-of-Care Devices

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Li, Xu Liang
Modeling , Point of Care
A dynamic model is developed and presented that predicts the voltage response for a Severinghaus electrode-based point-of-care pCO2 sensor. Eight partial differential equations are derived to describe the diffusion and reaction phenomena in the sensor. The model is able to predict the potential response versus time behaviour from different CO2 concentrations in the calibration fluid and control fluids. The two most influential and uncertain parameters in the model are determined to be the forward rate constant for benzoquinone consumption at the gold surface ( k_(f_Au ) ), and the partition coefficient for CO2 between the membrane and the electrolyte (κ_(〖CO〗_(2_m ) )). These parameters were adjusted heuristically to obtain a good fit (within 2 mV) between the dynamic voltage response data and the model predictions during a critical 4 second period. The model predictions are sufficient for design sensitivity studies, however an improved fit might be possible using a formal least-squares parameter estimation approach, or if additional parameters were estimated. Several design parameters are varied to study the influence of the electrolyte concentration and the sensor geometry on the voltage response. The most influential design parameter studied is the amount of water present in the electrolyte during sensor operation. This can be affected by the amount of water evaporated during manufacturing and storage, and by the amount of water present when the sensor “wets up” again during operation. The amount of water picked up by the sensor in turn is affected by design parameters such as component/membrane dimensions and thicknesses. The initial buffer concentration in the electrolyte is the second most influential parameter. The resulting model can be used to perform “what if” analyses in order to understand the impact of design decisions on the sensor performance, and to potentially improve the sensor from performance and manufacturing cost perspectives.
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