Parametric Optimization For Enhanced Sensitivity In A Colorimetric Chemical Sensor

Big Data processing tools have become increasingly powerful and have been applied to the area of personal chemical monitoring by companies such as Plume Labs and Rubix, requiring low-cost, capable sensors. The ubiquity of cell phone imagers has allowed for a revisiting of colorimetry as a viable chemical detection method. There has been a great deal of effort put into making colorimetric sensor arrays that can discriminate between a variety of analytes, but mainly in a qualitative sense with limited discussion regarding improving the performance of the sensor as a whole. However, these imaging devices have inherent limitations on their ultimate sensitivity. Other sensor configurations are being evaluated that can greatly enhance the sensitivity to a color change. Dye development continues in an effort to increase the specificity of the sensing event using currently available readout mechanisms, but what has been lacking has been a critical analysis of the readout mechanism for these molecular transducers. This work takes a quantitative approach to encourage a more rational design of colorimetric sensors with specific targets.