Electrocorticography (ECoG) is becoming a common tool for clinical applications, such as preparing patients for epilepsy surgery or localizing tumor boundaries, as it successfully balances invasiveness and information quality. Clinical ECoG arrays use millimeter-scale electrodes and centimeter-scale pitch and cannot precisely map neural activity. Higher-resolution electrodes are of interest for both current clinical applications, providing access to more precise neural activity localization and novel applications, such as neural prosthetics, where current information density and spatial resolution is insufficient to suitably decode signals for a chronic brain-machine interface. Developing such electrodes is not trivial because their small contact area increases the electrode impedance, which seriously affects the signal-to-noise ratio, and adhering such an electrode to the brain surface becomes critical. The most straightforward approach requires increasing the array conformability with flexible substrates while improving the electrode performance using materials with superior electrochemical properties. In this paper, we propose an ultra-flexible and conformable polyimide-based micro-ECoG array of submillimeter recording sites electrochemically coated with high surface area conductive polymer-carbon nanotube composites to improve their brain-electrical coupling capabilities. We characterized our devices both electrochemically and by recording from rat somatosensory cortex in vivo. The performance of the coated and uncoated electrodes was directly compared by simultaneously recording the same neuronal activity during multiwhisker deflection stimulation. Finally, we assessed the effect of electrode size on the extraction of somatosensory evoked potentials and found that in contrast to the normal high-impedance microelectrodes, the recording capabilities of our low-impedance microelectrodes improved upon reducing their size from 0.2 to 0.1 mm.

A fully flexible pH sensor using nanoporous ZnO on extended gate thin film transistor (EGTFT) fabricated on polymeric substrate is demonstrated. The sensor adopts the Low Temperature Polycrystalline Silicon (LTPS) TFT technology for the active device, since it allows excellent electrical characteristics and good stability and opens the way towards the possibility of exploiting CMOS architectures in the future. The nanoporous ZnO sensitive film, consisting of very thin (20 nm) crystalline ZnO walls with a large surface-to-volume ratio, was chemically deposited at 90 °C, allowing simple process integration with conventional TFT micro-fabrication processes compatible with wide range of polymeric substrates. The pH sensor showed a near-ideal Nernstian response (∼59 mV/pH), indicating an ideality factor α ∼ 1 according to the conventional site binding model. The present results can pave the way to advanced flexible sensing systems, where sensors and local signal conditioning circuits will be integrated on the same flexible substrate.

In this paper, we present an ultraflexible tactile sensor, in a piezo-eletricoxide-semiconductor FET configuration, composed by a poly[vinylidenefluoride-co-trifluoroethylene] capacitor with an embedded readout circuitry, based on nMOS polysilicon electronics, integrated directly on polyimide. The ultraflexible device is designed according to an extended gate configuration. The sensor exhibits enhanced piezoelectric properties, thanks to the optimization of the poling procedure (with electric field up to 3 MV/cm), reaching a final piezoelectric coefficient d ₃₃ of 47 pC/N. The device has been electromechanically tested by applying perpendicular forces with a minishaker. The tactile sensor, biased in a common-source arrangement, shows a linear response to increasing sinusoidal stimuli (up to 2 N) and increasing operating frequencies (up to 1200 Hz), obtaining a response up to 430 mV/N at 200 Hz for the sensor with the highest value of d ₃₃ . The sensor performances were also tested after several cycles of controlled bending in different amount of humidity with the intent to investigate the device behavior in real conditions.

A novel fully wearable system based on a smart wristband equipped with stretchable strain gauge sensors and readout electronics have been assembled and tested to detect a set of movements of a hand crucial in rehabilitation procedures. The high sensitivity of the active devices embedded on the wristband do not need a direct contact with the skin, thus maximizing the comfort on the arm of the tester. The gestures done with the device have been auto-labeled by comparing the signals detected in real-time by the sensors with a commercial infrared device (Leap motion). Finally, the system has been evaluated with two machine-learning algorithms Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), reaching a reproducibility of 98% and 94%, respectively.