SECM imaging of spatial variability in biosensor architectures

It can be assumed that by integration of many at least partially different sensing elements in a single sensor the gathered information about a measured sample can be significantly increased by recording the readouts of each individual sensing element. Each surface area which is modified with a specific sensing chemistry exhibits spatial inhomogeneity and can hence be treated similar to a sensor array, provided the possibility to extract localized information about the sensor response.In order to demonstrate this approach enzyme–polymer spots were fabricated on glass surfaces and the localized sensor response upon addition of the enzyme’s substrate was visualized using scanning electrochemical microscopy (SECM) in the generator-collector mode. SECM images obtained at different substrate concentrations were used as a basis for investigation of spatial variability of the surface activity by means of feature extraction techniques. It was shown that by discretization of a sensing structure (e.g., a spot of polymer with entrapped glucose oxidase) numerous unique sources of analytical information can be identified within the overall sensing structure. The variety of the characteristics of the calibration graphs allows for the selection of those substructures which are the best carriers of a particular information. Substructures with similar response characteristics resembling similarities in their information content can be grouped thus securing the backup of information. It was found that similarities in the response characteristics are related to the location of sensing element within the overall sensing structure.