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Biological shot-noise and quantum-limited signal-to-noise ratio in affinity-based biosensors
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Image of FIG. 1.
FIG. 1.

Detection in affinity-based sensors requires (a) collision of analyte particles with the binding sites followed by (b) analyte capturing and transduction processes.

Image of FIG. 2.
FIG. 2.

Markov model for (a) probabilistic motion in the reaction chamber and (b) motion in the presence of immobilized binding sites. Each coordinate corresponds to a state in the process where particles can move into or leave. represents the location of the analyte particle at time and defines the coordinates of the state in the system.

Image of FIG. 3.
FIG. 3.

When the concentration of binding molecules is high enough that the reaction kinetics becomes insensitive to its increase, we practically ensure that (a) every analyte molecule has effectively a molecule in its proximity. This is equivalent in average to (b), a system where each molecule occupies a volume equal to , and analyte molecules have a reactive distance of .

Image of FIG. 4.
FIG. 4.

General Markov model for affinity-based biosensors with possible states for the analyte particles including captured states.

Image of FIG. 5.
FIG. 5.

An affinity biosensors structure with a cubic reaction chamber where mass-transfer processes are only relevant in one dimension.

Image of FIG. 6.
FIG. 6.

An electronic DNA hybridization sensor where the ISFET device detects the binding incidents. When charged DNA strands bind to their complementary structures (i.e., binding sites) of the sensing area, the charge profile in the channel is modified. Subsequently, the induced change in channel transconductance is electronically quantified and associated with DNA binding.

Image of FIG. 7.
FIG. 7.

Transient simulation results of the DNA biosensor where analyte number is 1000. A Monte Carlo random-walk simulation is carried out with three initial distributions. The lower and upper bounds are calculated using (24), and the asymptotic value is derived from (21).

Image of FIG. 8.
FIG. 8.

Simulated PSD of noise for and , which demonstrates the accuracy of the PSD approximation derived in (31).

Image of FIG. 9.
FIG. 9.

SNR from (33) vs speed from (23) is plotted as the reaction chamber is isomorphically scaled down, which demonstrates the fundamental trade-off between size and response time of affinity-based biosensors.


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Table I.

The closed-form approximations for the statistical characteristics of a one-dimensional biosensor structure.

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Table II.

Specifications of ISFET biosensor for electronic detection of DNA hybridization.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Biological shot-noise and quantum-limited signal-to-noise ratio in affinity-based biosensors