Technologies

At IFN-CNR, in collaboration with Politecnico di Milano-Department of Physics, we have developed Raman microscopy approaches compatible with the study and characterization of biological and industrial samples. In detail, our facility houses a self-built spontaneous confocal Raman microscope with the following characteristics: two excitation lasers (660nm and 785nm), inverted microscope (Olympus IX-73) and Princeton spectrometer / CCD.

Solid State Nuclear Magnetic Resonance spectroscopy (SSNMR) is today one of the most powerful techniques for characterizing solid and soft materials and systems. This spectroscopy allows the detailed characterization of structural and dynamic properties over large spatial (0.1-100 nm) and time (10²-10^-11 s) scales. Accessing these properties allows a deep knowledge of a material to be obtained and  its design and optimization to be oriented.

To the enterprises working in the field of nutrition/nutraceutics and drug development/repositioning, we offer the know-how and state-of-the-art instrumentation of our labs to monitor multiple relevant biological parameters at the cellular level: metabolic activity, vitality, health, but also stress and toxicity. The use of advanced imaging techniques based on fluorescent/bioluminescent probes together with the availability of time-lapse acquisitions, guarantee the cutting-edge analysis of different biological parameters over time.

The proposed system is based on a high-resolution electrocardiograph in which the electrodes are positioned on maternal abdomen. The acquired signals are processed using a completely unsupervised software for fetal ECG extraction based on independent component analysis, maternal ECG canceling and a quality index optimization. The electrocardiograph is constituted by a light-weight and light-dimension portable unit, which acquires the signals and transmit them to a computer where the analysis software runs.

The software is based on mathematical models able of simulating the time evolution of the different stages of a pest population starting from environmental data collected from weather stations located in an area of interest and information regarding the development stage of the host plant. The models are of two types: phenological, which provides information on the stages population as a function of time and demographic which also allows to know the abundance of each population stage.