B-ME developed the first thermoplastic composite electrode film based on bio-derived and biodegradable polyesters and carbon nano-fibers. It is metal-free, highly electrically conductive and possess good thermo-mechanical properties, a challenging combination of three features in a single product. This is the first-of-its-kind product, as, to the best of our knowledge, no thermoplastic biobased electrode film has been effectively produced and used so far.
Technologies
In this section it is possible to view, also through targeted research, the technologies inserted in the PROMO-TT Database. For further information on the technologies and to contact the CNR Research Teams who developed them, it is necessary to contact the Project Manager (see the references at the bottom of each record card).
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Silicon nanowires (SiNWs) are 1D structures with diameter ranging from few tens to hundreds of nanometers and length varying from few tens of nanometers to millimiters. SiNWs are fabricated in the labs of the IMM-CNR, Rome Unit, by using bottom-up technologies such as plasma enhanced chemical vapor deposition (PECVD) at low growth temperature ((≤350°C), allowing the use of plastic and glassy substrates. Their electrical properties can be tuned by controlling the p/n doping during the growth.
The constant demand for more powerful and energy-efficient electronic devices than existing ones is challenging scientists and companies to develop innovative solutions that can address such primary technological needs. Based on a recent scientific discovery made by our team we have developed a technology for superfast and extremely scalable logic and computing circuits with minimal energy losses, which has the potential to become the leading technology in the future world of largescale computing and telecommunication infrastructures.
The platform allows acquisition of data from commercial and custom sensors. By now, the system has been embedded in a wearable wristband where elastomeric based strain gauge have been integrated to detect fine hand/wrist/arm movements. The platform integrates inertial sensors (accelerometers, gyroscopes) to acquire more details about the subject movements. A sensor-fusion algorithm enables advanced movement recognition (gesture, 3D orientation). A machine-learning algorithm is in development to increase the performance of the platform.