# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 3 Fluctuation-Enhanced Gas Sensing: A Brief Historical Overview  
Most chemical sensors produce a single output, such as, for example, the steady-state value of the resistance of a Taguchi sensor [18], or the steady-state current value of a MOS sensor. To generate a separate pattern corresponding to different chemical compositions, like in the case of electronic noses, a number (6-40) of different types of sensors are needed, which makes the system expensive and unreliable for practical applications. On the other hand, fluctuation-enhanced sensing (FES), as illustrated in Figure 3, by analyzing the fluctuations rather than the DC value, is capable of generating complex patterns even by starting from a single sensor [41; 42; 43; 44; 45; 46; 47; 48] and this fact represents a great advantage with respect to other more conventional sensing techniques. Figure 2: Operating mode of fluctuation-enhanced odor sensing by biological noses. When applying FES, instead of using the mean value (time average) of the sensor signal, the small stochastic fluctuations around the mean value are amplified and statistically analyzed. Resistive film sensors, due to their grainy structure, present electronic resistance fluctuations several orders of magnitude higher if compared to the case of single crystalline materials, and these fluctuations are strongly influenced by the random diffusion dynamics of agents in the proximity of inter-grain junctions and by adsorption-desorption noise. Stochastic analytical tools are used to generate a one-dimensional or two-dimensional pattern from the time fluctuations and pattern recognition tools are employed for their analysis. The origin of FES dates back to more than two decades ago but the name \"fluctuation-enhanced sensing\" was coined by [PERSON] (SPAWAR, US Navy) in 2001. The solution of employing electrical noise (spontaneous fluctuations) to identify chemical agents was first proposed by [PERSON] and coworkers [41; 42] in 1994-1995 by showing the high sensitivity of conductance noise spectra of conducting polymers to the ambient gas composition. [PERSON] and coworkers [43], a few years later (1997), also published similar observations about the conductance noise spectra of semiconductor resistors with non-passivated surfaces. In 1998, [PERSON] and coworkers designed the first FES system capable of quantitative analysis of gas mixtures and conducted mathematical analysis about the limits and the sensor number requirement versus the number of agents [44; 45; 46]. In [45] it was demonstrated the effectiveness of the sampling-and-hold technique as a way of \"freezing the odor\" (see Section 6.2.1) in a Taguchi sensor and later a more extensive analysis on the subject was published by [PERSON] et al. [47]. In 2001, [PERSON] et al. were the first ones to use higher-order statistics to enhance the extracted information from the stochastic signal component [48] and they confirmed the robustness of their approach with successive works [49; 50; 51]. [PERSON] et al. showed FES via invasion noise effects at room temperature in nanoparticle films [52]. [PERSON] and coworkers analyzed the situation of Surface Acoustic Wave (SAW) sensors and predicted the FES spectrum for SAW and MOS sensors with surface diffusion [53; 54]. Commercial-Off-The-Shelf (COTS) sensors exposed to environmental pollutants and gas combinations were also studied [55; 56; 57]. In nanoparticle sensors with a temperature gradient, the possibility of using the noise of the thermoelectric voltage for FES was demonstrated [51]. [PERSON] et al. analyzed the sensitivity enhancement in the FES mode and compared it to the classical mode in nanoparticle sensors and found an enhancement of a factor of 300 [58]. [PERSON] et al. [59; 60] published FES theories for the cases of adsorption-desorption noise and chemisorption-induced noise and, recently, in [61] [PERSON] et al.