Adsorption-based microsensors for protein detection: influence of protein structural transformations and adsorption-induced analyte depletion of the analyzed sample on the sensor noise

The noise originating from the random nature of processes that affect the time evolution of the number of adsorbed molecules is inherent to adsorption-based biosensors. It is a fundamental noise, so it determines the ultimate detection limit of such sensors. In this paper we develop a noise model of protein microsensors, which takes into account the combined effect of three processes: reversible adsorption, protein structural transformation, and depletion of the analyzed sample of protein molecules during adsorption. This is the first non-linear noise model that considers the random change of the biomolecule spatial structure, the effect that often follows the adsorption of proteins on a sensing surface. We use the developed model for the analysis of the effects that protein structural transformation and depletion of the analyzed sample have on the noise. The results of the analysis show their pronounced influence on the shape of the noise spectrum and on the noise power in the frequency range of interest, especially at low protein concentrations. The presented noise model and analysis are important for the estimation and optimization of sensing performance during sensor development, as well as for the development of highly sensitive measurement methods based on noise measurement.