Fractal Design for Advancing the Performance of Chemoresistive Sensors

The rapid advancement of internet of things (IoT)-enabled applications along with connected automation in sensing technologies is the heart of future intelligent systems. The probable applications have significant implications, from chemical process monitoring to agriculture, mining, space, wearable electronics, industrial manufacturing, smart cities, and point-of-care (PoC) diagnostics. Advancing sensor performance such as sensitivity to detect trace amounts (ppb-ppm) of analytes (gas/VOCs), selectivity, portability, and low cost is critical for many of these applications. These advancements are mainly achieved by selecting and optimizing sensing materials by their surface functionalization and/or structural optimization to achieve favorable transport characteristics or chemical binding/reaction sites. Surprisingly, the sensor geometry, shapes, and patterns were not considered as critical parameters, and most of these sensors were designed by following simple planar and interdigitated electrode geometry. In this study, we introduce a new bioinspired fractal approach to design chemoresistive sensors with fractal geometry, which grasp the architecture of fern leaves represented by the geometric group of space-filling curves of fractal patterns. These fractal sensors were printed by an extrusion process on a flexible substrate (PET) using specially formulated graphene ink as a sensing material, which provided significant enhancement of the active surface area to volume ratio and allowed high-resolution fractal patterning along with a reduced current transportation path. To demonstrate the advantages and influence of fractal geometry on sensor performance, here, three different kinds of sensors were fabricated based on different fractal geometrics (Sierpinski, Peano, and Hilbert), and the sensing performance was explored toward different VOC analytes (e.g., ethanol, methanol, and acetone). Among all these fractal-designed sensors including interdigitate sensors, the Hilbert-designed printed sensor shows enhanced sensing properties in terms of fast response time (6 s for 30 ppm), response value (14%), enhanced detection range (5-100 ppm), high selectivity, and low interference to humidity (up to RH 80%) for ethanol at room temperature (20 °C). Moreover, a significant improvement of this sensor performance was observed by applying the mechanical deformation (positive bending) technique. The practical application of this sensor was successfully demonstrated by monitoring food spoilage using a commercial box of strawberries as a model. Based on these presented results, this biofractal biomimetic VOC sensor is demonstrated for a prospective application in food monitoring.