The basic parameters of a sensor element deﬁning its ultimate performance are sensitivity and intrinsic noise. In plasmonic gas sensors both are determined by refractive index changes due to adsorption and desorption (a–d) of target analyte particles to the sensor active area. In this paper we present a general model that can be simultaneously used to determine sensitivity and intrinsic noise of a plasmonic sensor both during transients and in steady-state and is valid for multi-analyte environments. The model utilizes the conventional probabilistic approach. It is derived without any assumptions about the stochastic nature of the fundamental (a–d) process. It reveals how all stochastic properties of the processes with (pseudo) ﬁrst order kinetics with the initial number of particles equal to zero can be fully determined from the deterministic solution, without any previous stochastic analysis. Based on the proposed model it is possible to establish the optimum moment for readout when ﬂuctuations are minimal. Transients last longer and ﬂuctuations are lower at lower temperatures. The insight into the transient dynamics opens the possibility to use a single element sensor for multiple analyte sensing. Another result is that a–d noise is higher for smaller adsorption areas, which may be important for micro and nanosystems generally, since each of them has to be kept immersed in some kind of environment and thus be subject to contamination by adsorption that can signiﬁcantly inﬂuence their behavior. Besides being applicable for plasmonic sensors of trace amounts of gases and other nanoplasmonic devices used in sensing, the model is applicable for other adsorption-based sensors, as well as for the investigations of stochastic phenomena in micro and nanostructures.
- Stochastic analysis,
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