Technologies

AIDD is an integrated tool and a radically new way to discovery new drugs for neurodegenerative diseases (Alzheimer’s, Epilepsy, Ageing, etc.).

The dramatic global health emergency due to the SARS-CoV-2 pandemic requires new diagnostic devices capable of identifying the presence of virus particles in patient biological samples. In this direction, the development of an innovative low-cost test, which provides the result within a few minutes, which is reproducible and which can reveal the direct presence of even a few viral particles, would be of fundamental importance for the monitoring and containment of the pandemic.

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.

Detection devices for the presence of molecules of interest (analytes) enjoyed a renewed burst with the introduction of biological components (biosensors). Their high specificity is often used in various fields, from environmental monitoring and biomedicine to the protection and promotion of agri-food products. However, the high cost of production and the lack of compatibility with mass sampling (high-throughput) sometimes limit their use.

The aim of the research group is the creation of 3D models (microorgan/ organoids) constructed using samples obtained from patients, both biopsy samples and samples collected with non-invasive techniques (exhaled breath condensate, induced sputum, blood samples).

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.

Environmental monitoring is a rapidly growing field, both in academia and industry. The use of wearables for environmental monitoring is a promising technique, as it allows data to be collected continuously and comprehensively. The main problem with using wearables for environmental monitoring is the size and weight of the system, as well as the high degree of specialization required to develop a fully functional device.