This thesis described the development of a new type of biosensor based on the integration of atomically thin graphene in a broadband radio-frequency (RF) coplanar waveguide (CPW). To combine both electrochemical and dielectric sensing concepts, a graphene channel is inserted into the CPW, which can then be functionalised using standard chemical processes developed for graphene direct-current (DC) sensors. The new RF sensors shown in this work inherit the strong response of graphene at low analyte concentrations, while enabling RF methods at concentrations not possible before. This is shown by limit of the detection for glucose, based on RF measurements, several orders of magnitude lower than that previously reported in literature, enabling measurements of glucose at levels found in human sweat. The combined effect of the two sensing responses can be seen as the high sensitivity at low concentrations, 7.30 dB/(mg/L), significantly higher than metallic state-of-the-art RF sensors, which gradually decreases as the graphene saturates and the response at higher concentrations aligns with that of previously reported metal sensors. This shows the extended operating range of the new sensors compared to the literature state-of-the-art. Similarly, for DNA strands, the graphene CPW sensors was able to distinguish between different types of DNA (perfect match, single mutation, and complete mismatch) at the limit of detection, 1 aΜ, improving the concentration response by orders of magnitude compared to existing RF DNA sensors and allowing for distinction of the types at lower concentration than those in contemporary DC graphene sensors. The additional effect of tunable graphene conductivity independently of the analyte, is the creation of multidimensional datasets for each S-parameter component by combining the frequency and potential responses. The resultant sensor response surfaces contain more information that can be used to determine the concentration and type of the analyte. In particular for DNA, the multidimensional approach, by considering the frequency and biasing condition for the largest joint changes in the parameters allowed for direct classification of the three DNA analyte types based on the sign of the changes in the parameters ($\Re /\Im Sij$ or $mag/\angle Sij$). Because of the multidimensional nature of the dataset, machine learning algorithms could be applied to extract features used for determining concentrations based on the raw measurements. For glucose the concentration was predicted with larger than 98% confidence, while the more feature-rich surfaces of the DNA response allowed the determination of the analyte type at 1 aΜ even with a simulated signal-to-noise ratio of 10 dB, proving the utility and resilience of the developed devices and measurement approach.

The above approaches were only enabled by the new type of sensor combining both graphenes electronic properties and RF sensing concepts in a single device.