Potential Application of Organic Electronics in Electrical Sensing of Insects and Integrated Pest Management towards Developing Ecofriendly Replacements for Chemical Insecticides.

Synthetic insecticides are widely used against plant pest insects to protect the crops. However, many insecticides have poor selectivity and are toxic also to beneficial insects, animals, and humans. In addition, insecticide residues can remain on fruits for many days, jeopardizing food safety. For these reasons, a reusable, low-cost electronic trap that can attract, detect, and identify, but attack only the pest while leaving beneficial insects unharmed could provide a sustainable, nature-friendly replacement. Here, for the first time, research results are presented suggesting the great potential and compatibility of organic electronic devices and technologies with pest management. Electrical characterizations confirm that an insect's body has relatively high dielectric permittivity. Adaptive memcapacitor circuits can track the impedance change for insect detection. Other experiments show that printed polymer piezoelectric transducers on a plastic substrate can collect information about the weight and activity of insects for identification. The breakdown voltage of most insects´ integument is measured to be <200 V. Long channel organic transistors easily work at such high voltages while being safe to touch for humans thanks to their inherent low current. This feasibility study paves the way for the future development of organic electronics for physical pest control and biodiversity protection.

Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification.

Early detection of malign patterns in patients' biological signals can save millions of lives. Despite the steady improvement of artificial intelligence-based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients' data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow-power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues.