Graphene has become a material of choice for an increasing number of scientific and industrial applications. It has been used for gas sensing due to its favorable properties, such as a large specific surface area, as well as the sensitivity of its electrical parameters to adsorption processes occurring on its surface. Efforts are ongoing to produce graphene gas sensors by using methods that are compatible with scaling, simple deposition techniques on arbitrary substrates, and ease of use. In this paper, we demonstrate the fabrication of carbon dioxide gas sensors from Langmuir–Blodgett thin films of sulfonated polyaniline-functionalized graphene that was obtained by using electrochemical exfoliation. The sensor was tested within the highly relevant concentration range of 150 to 10,000 ppm and 0% to 100% at room temperature (15 to 35 °C). The results show that the sensor has both high sensitivity to low analyte concentrations and high dynamic range. The sensor response times are approximately 15 s. The fabrication method is simple, scalable, and compatible with arbitrary substrates, which makes it potentially interesting for many practical applications. The sensor is used for real-time carbon dioxide concentration monitoring based on a theoretical model matched to our experimental data. The sensor performance was unchanged over a period of several months.

In order to improve the interpretation of measurement results and to achieve the optimal performance of microfluidic biosensors, advanced mathematical models of their time response and noise are needed. The random nature of adsorption–desorption and mass transfer (MT) processes that generate the sensor response makes the sensor output signal inherently stochastic and necessitates the use of a stochastic approach in sensor response analysis. We present a stochastic model of the sensor time response, which takes into account the coupling of adsorption–desorption and MT processes. It is used for the analysis of response kinetics and ultimate noise performance of protein biosensors. We show that slow MT not only decelerates the response kinetics, but also increases the noise and decreases the sensor’s maximal achievable signal-to-noise ratio, thus degrading the ultimate sensor performance, including the minimal detectable/quantifiable analyte concentration. The results illustrate the significance of the presented model for the correct interpretation of measurement data, for the estimation of sensors’ noise performance metrics important for reliable analyte detection/quantification, as well as for sensor optimization in terms of the lower detection/quantification limit. They are also incentives for the further investigation of the MT influence in nanoscale sensors, as a possible cause of false-negative results in analyte detection experiments.

The importance of adsorption-based biochemical/biological sensors in biochemistry and biophysics is paramount. Their temporal response gives information about the presence of a biochemical/biological analyte, its concentration and its interactions with the adsorption sites (which may be an integral part of the surface itself or immobilized functionalizing molecules). Mathematical models of the temporal response taking into account as many relevant effects as possible are essential for obtaining reliable information. We present a novel model taking into account the bimodal affinity of a sensing surface (adsorption occurs on two distinct site types), and the adsorption-caused depletion of the analyte from the sample. We perform qualitative and quantitative analysis of the analyte depletion influence on the bimodal adsorption, and of the influence of the sensing surface inhomogeneity on the sensor temporal response, for different analyte concentrations and different fractions of two types of adsorption sites. Since the presented mathematical model deals with the realistic cases of the sensing surface non-uniformity and the finite amount of analyte present in the sensor reaction chamber, it enables improved accuracy in interpreting the measurement data. Our results are general, i.e. valid for any adsorption sensor (microcantilevers, plasmonics) and for arbitrary sensor dimensions.