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Quantifying Measurement Fluctuations from Stochastic Surface Processes on Sensors with Heterogeneous Sensitivity

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Charmet, J 
Michaels, TCT 
Prasad, A 
Thiruvenkathanathan, P 

Abstract

Recent advances in micro- and nanotechnology have enabled the development of ultrasensitive sensors capable of detecting small numbers of species. In general, however, the response induced by the random adsorption of a small number of objects onto the surface of such sensors results in significant fluctuations due to the heterogeneous sensitivity inherent to many such sensors coupled to statistical fluctuations in the particle number. At present, this issue is addressed by considering either the limit of very large numbers of analytes, where fluctuations vanish, or the converse limit, where the sensor response is governed by individual analytes. Many cases of practical interest, however, fall between these two limits and remain challenging to analyze. Here, we address this limitation by deriving a general theoretical framework for quantifying measurement variations on mechanical resonators resulting from statistical-number fluctuations of analyte species. Our results provide insights into the stochastic processes in the sensing environment and offer opportunities to improve the performance of mechanical-resonator-based sensors. This metric can be used, among others, to aid in the design of robust sensor platforms to reach ultrahigh-resolution measurements using an array of sensors. These concepts, illustrated here in the context of biosensing, are general and can therefore be adapted and extended to other sensors with heterogeneous sensitivity.

Description

Keywords

40 Engineering, 51 Physical Sciences, Bioengineering, Biotechnology

Journal Title

Physical Review Applied

Conference Name

Journal ISSN

2331-7019
2331-7019

Volume Title

5

Publisher

American Physical Society (APS)
Sponsorship
Biotechnology and Biological Sciences Research Council (BB/J002119/1)
We acknowledge funding from the W. D. Armstrong fund, Biotechnology and Biological Sciences Research Council, Newman Foundation, St. John’s College–University of Cambridge, and European Research Council.