In recent decades, the susceptibility to degradation in both ambient and aqueous environments has prevented organic electronics from gaining rapid traction for sensing applications. Here we report an organic field-effect transistor sensor that overcomes this barrier using a solution-processable isoindigo-based polymer semiconductor. More importantly, these organic field-effect transistor sensors are stable in both freshwater and seawater environments over extended periods of time. The organic field-effect transistor sensors are further capable of selectively sensing heavy-metal ions in seawater. This discovery has potential for inexpensive, ink-jet printed, and large-scale environmental monitoring devices that can be deployed in areas once thought of as beyond the scope of organic materials.

Field effect transistors (FETs) are widely used for the label-free detection of analytes in chemical and biological experiments. Here we demonstrate that the apparent sensitivity of a dual-gated silicon nanowire FET to pH can go beyond the Nernst limit of 60 mV/pH at room temperature. This result can be explained by a simple capacitance model including all gates. The consistent and reproducible results build to a great extent on the hysteresis- and leakage-free operation. The dual-gate approach can be used to enhance small signals that are typical for bio- and chemical sensing at the nanoscale.

We observe very small gate-voltage shifts in the transfer characteristic of as-prepared graphene field-effect transistors (GFETs) when the pH of the buffer is changed. This observation is in strong contrast to Si-based ion-sensitive FETs. The low gate-shift of a GFET can be further reduced if the graphene surface is covered with a hydrophobic fluorobenzene layer. If a thin Al-oxide layer is applied instead, the opposite happens. This suggests that clean graphene does not sense the chemical potential of protons. A GFET can therefore be used as a reference electrode in an aqueous electrolyte. Our finding sheds light on the large variety of pH-induced gate shifts that have been published for GFETs in the recent literature.

Biodetection using organic field-effect transistors (OFETs) is gaining increasing interest for applications as diverse as food security, environmental monitoring, and medical diagnostics. However, there still lacks a comprehensive, empirical study on the fundamental limits of OFET sensors. In this paper, we present a thorough study of the various parameters affecting biosensing using an OFET decorated with gold nanoparticle (AuNP) binding sites. These parameters include the spacing between receptors, pH of the buffer, and ionic strength of the buffer. To this end, we employed the thrombin protein and its corresponding DNA binding aptamer to form our model detection system. We demonstrate a detection limit of 100 pM for this protein with high selectivity over other proteases in situ. We describe herein a feasible approach for protein detection with OFETs and a thorough investigation of parameters governing biodetection events using OFETs. Our obtained results should provide important guidelines to tailor the sensor's dynamic range to suit other desired OFET-based biodetection applications.

Silicon nanowire field-effect transistors have attracted substantial interest for various biochemical sensing applications, yet there remains uncertainty concerning their response to changes in the supporting electrolyte concentration. In this study, we use silicon nanowires coated with highly pH-sensitive hafnium oxide (HfO(2)) and aluminum oxide (Al(2)O(3)) to determine their response to variations in KCl concentration at several constant pH values. We observe a nonlinear sensor response as a function of ionic strength, which is independent of the pH value. Our results suggest that the signal is caused by the adsorption of anions (Cl(-)) rather than cations (K(+)) on both oxide surfaces. By comparing the data to three well-established models, we have found that none of those can explain the present data set. Finally, we propose a new model which gives excellent quantitative agreement with the data.