Electrochemical impedance based sensors (EIS Sensors) have promise to provide detection of a wide array of molecular biomarkers of interest. The possibility to “plug and play”, the ability to integrate into a miniaturized format, and the potential to make reagentless sensors cause these sensors to be of high interest for performance monitoring. However, the self-assembled monolayer (SAM) containing the biorecognition element and issues that impact drift and noise are seldom investigated thoroughly.
The work presented here takes an in-depth look at packing density in the SAM and the effect on signal, as well as factors that may cause sensor drift. Through studying SAM density and drift, we are able to optimize signal and minimize noise, especially in highly sensitive sensors, even enabling detection of cortisol, a cognitive level biomarker of interest. Using both a proof-of-concept lysozyme aptamer, as well as a cortisol aptamer, we were able to observe the impact of packing density on sensor performance, and the consistency of these aptamers enabled through optimization of self-assembled monolayer (SAM) formation. With the results from the lysozyme SAM optimization, we followed a similar methodology to optimize the SAM for detection of cortisol. In order to broaden out the scope of this work, we used these finely tuned SAMs and performed a direct comparison of optimal and suboptimal SAMs on gated Field-Effect Transistor (FET) sensors as well.
We conclude that not only does SAM density result in an order of magnitude change in output signal, but that understanding and compensating for drift factors enable for reliable, consistent, reproducible sensors for implementation in a broad array of applications.