Oxidative chemical vapor deposition (oCVD) is a versatile technique that can simultaneously tailor properties (e.g., electrical, thermal conductivity) and morphology of polymer films at the nanoscale. In this work, we report the thermal conductivity of nanoscale oCVD grown poly(3,4-ethylenedioxythiophene) (PEDOT) films for the first time. Measurements as low as 0.16 W m⁻¹ K⁻¹ are obtained at room temperature for PEDOT films with thicknesses ranging from 50–100 nm. These values are lower than those for solution processed PEDOT films doped with the solubilizing agent PSS (polystyrene sulfonate). The thermal conductivity of oCVD grown PEDOT films show no clear dependence on electrical conductivity, which ranges from 1 S cm⁻¹ to 30 S cm⁻¹. It is suspected that at these electrical conductivities, the electronic contribution to the thermal conductivity is extremely small and that phonon transport is dominant. Our findings suggest that CVD polymerization is a promising route towards engineering polymer films that combine low thermal conductivity with relatively high electrical conductivity values.

A longstanding challenge for accurate sensing of biomolecules such as proteins concerns specifically detecting a target analyte in a complex sample (e.g., food) without suffering from nonspecific binding or interactions from the target itself or other analytes present in the sample. Every sensor suffers from this fundamental drawback, which limits its sensitivity, specificity, and longevity. Existing efforts to improve signal-to-noise ratio involve introducing additional steps to reduce nonspecific binding, which increases the cost of the sensor. Conducting polymer-based chemiresistive biosensors can be mechanically flexible, are inexpensive, label-free, and capable of detecting specific biomolecules in complex samples without purification steps, making them very versatile. In this paper, a poly (3,4-ethylenedioxyphene) (PEDOT) and poly (3-thiopheneethanol) (3TE) interpenetrating network on polypropylene–cellulose fabric is used as a platform for a chemiresistive biosensor, and the specific and nonspecific binding events are studied using the Biotin/Avidin and Gliadin/G12-specific complementary binding pairs. We observed that specific binding between these pairs results in a negative ΔR with the addition of the analyte and this response increases with increasing analyte concentration. Nonspecific binding was found to have the opposite response, a positive ΔR upon the addition of analyte was seen in nonspecific binding cases. We further demonstrate the ability of the sensor to detect a targeted protein in a dual-protein analyte solution. The machine-learning classifier, random forest, predicted the presence of Biotin with 75% accuracy in dual-analyte solutions. This capability of distinguishing between specific and nonspecific binding can be a step towards solving the problem of false positives or false negatives to which all biosensors are susceptible.