Biosensor time response and noise models that take into account spatial rearrangement of adsorbed biomolecules

The growing need for high-performance in-situ biosensing is driving the development of micro/nanobiosensors, which have already shown a significant potential for highly sensitive detection of biological specimens or biologically relevant chemical substances, in applications such as real time monitoring of the biological pollution in air, water and food, or the health conditions of living organisms. Research efforts are being made to push their performance further, beyond the current limits. In this sense, it is important to investigate physical processes and phenomena that inevitably affect the generation of the sensor response and its fluctuations, thus setting the fundamental performance limit. The basis of these investigations is the development and application of mathematical models of sensor time response and noise, which take into account all relevant processes.

Adsorption-based biosensing relies on the reversible adsorption process of biomolecules on a sensing surface. In addition to producing the response of the sensor, this process, stochastic in nature, is also a source of noise that affects the performance of micro/nanosensors. Spatial rearrangement of adsorbed biomolecules changes the binding/unbinding kinetics to two-step process behavior, and therefore affects both the sensor’s time response and its fluctuations. We present the improved model of the sensor time response and noise, considering biomolecular rearrangement, and evaluate the extent of its influence for various rates of rearrangement and adsorption/desorption processes. The development of improved mathematical models of temporal response and noise of sensors, which include the effects pronounced in the real applications of these devices, is indispensable for both a correct interpretation of the measurement results and estimation of sensor performance limits, and thus for the achievement of reliable detection of the target agent in the analyzed samples.