The uncertainty-quantified calibration curves in Fig. provide a graphical approach to the inverse problem: given a response of the sensor, we can see the typical range of analyte concentrations that could have likely produced it. Both (i) a small slope of the calibration curve and (ii) thick bands make the confidence interval on the predicted analyte concentration large. While large residence times decrease the thickness of the bands, they also decrease the slope of the calibration curve. This explains the existence of an optimal residence time. How does the selectivity of the catalyst in the filter affect the optimal residence time? Fig. shows the performance of the CF-GS as a function of residence time for various catalyst selectivities k i /k a (depicted by color). For a given residence time of gas in the CF, a more selective catalyst reduces the expected error in the predicted partial pressure of the analyte. For a given catalyst (selectivity), we mark the optimal residence time with |. As the selectivity of the catalyst increases, the optimal residence time and optimal performance increases. Interestingly, Fig. shows that, if the residence time is too long (and the catalyst is poorly selective), the performance of the sensor is improved by removing the catalytic filter! For reference, the dotted line pertains to a CF with a perfectly selective catalyst, k i /k a → ∞. In this situation, if the residence time in the filter is large, the contribution to the response of the sensor by the interferent will be completely eliminated and the analyte will be completely preserved. However, the error is still nonzero because measurement noise still corrupts the predicted gas composition.