Embedded Files
Staff
Martella Daniele (UniFi, Contact Person)
Sara Nocentini (INRiM, Contact Person)
PhD and Post-Doc
Simone Donato (Post-Doc INRiM, Contact Person)
Spinoso Vincenzo (PhD Student UniFi, Contact Person)
Metal-insulator-metal (MEMS) microstructures based on Liquid Crystalline Networks
Metal-insulator-metal (MEMS) microstructures based on Liquid Crystalline Networks
This project revolutionizes chemical sensing through the development of Metal-Insulator-Metal (MIM) microstructures utilizing Liquid Crystalline Networks (LCNs). LCN-based smart polymers undergo controlled, reversible changes in shape in response to factors like light, pH, and temperature. These multi-responsive materials are well-suited for various applications, including biomedical engineering, robotics, and responsive coatings. LCNs as an insulating layer in MIM structures, sandwiched between metal layers, forming dynamically tunable optical cavities (Fabry-Perot cavities) and Capacitors.
MIM sensors offer numerous applications due to their ability to adapt and respond to various stimuli. Capacitive sensors are particularly useful for harsh environments, offering robust, simple electrical measurements suitable for industrial or medical diagnostics. The versatility of our sensors and their compatibility with standard electronics makes them promising tools for real-time monitoring, environmental analysis, and smart devices, driving innovation in diverse technological fields.
Advancing Wearable Technology for Chronic Disease Management
Advancing Wearable Technology for Chronic Disease Management
This project focuses on the design and development of cutting-edge wearable sensors that utilize microfluidic devices integrated with electrochemical sensors. The goal is to create a non-invasive, real-time monitoring system capable of analyzing biomarkers directly from bodily fluids such as sweat. By combining elastic polymeric microfluidics for precise fluid handling with electrochemical detection for sensitive, selective analysis, these devices aim to provide continuous, personalized health data. This innovative approach offers the potential for continuous monitoring, enhanced patient care, and proactive management of chronic conditions such as diabetes, cardiovascular diseases, and chronic kidney illnesses. The project seeks to revolutionize personalized healthcare through the seamless integration of advanced sensor technology into everyday life.
Filter-array reconstruction spectroscopy is used to characterize arbitrary light sources based on the transmittance through a set of filters that have been previously resolved with high spectral resolution. Illuminating them allows for achieving spectral reconstruction with enhanced resolution than that obtained with a comparable array of evenly frequency-separated gaussian band-pass filters.
Enhanced spectral resolution is achievable using broadband optical filters with smoothly varying, angular-robust transfer functions. The selection of smooth transfer functions ensures sufficient spectral diversity to computationally retrieve sparse and denser signals accurately.
GASMAS and Optical Porosimetry
GASMAS and Optical Porosimetry
As part of the IPHOQS infrastructure project, our group is developing novel and advanced spectroscopy techniques and materials for applications in sustainable green technologies. At the CNR-LENS unit, we will address various aspects of green photonics, including the development of innovative systems for detecting microplastics and waste management. Our activities will focus on creating low-cost, alignment-free spectroscopic facilities for monitoring greenhouse gases and other pollutants.
Staff
Camilla Parmeggiani (UniFi, Contact Person)
PhD and Post-Doc
Neri Fuochi (UniFi, Contact Person)
Broadband filters
for spectroscopy
Broadband filters
for spectroscopy
Spectroscopic applications strive for high spectral resolution and broad bandwidths, often facing a tradeoff between the two. Recent advancements in super-resolved spectroscopy offer promising solutions, particularly beneficial for compact and cost-effective instruments in various fields like sensing, quality control, environmental monitoring, and biometric authentication. These techniques, employing sparse sampling, artificial intelligence, and post-processing reconstruction algorithms, enable efficient spectral investigation. Reconstructive spectroscopy, a versatile processing technique, reconstructs signals from a limited number of measurements under the assumption of signal sparsity in a chosen domain. Resolution enhancement via spectral reconstruction has been successfully demonstrated. By projecting the target spectrum onto a random basis of non-ideal broadband spectral filters and utilizing regularization algorithms, highly resolved spectral signals can be reconstructed, even below the Nyquist sampling theorem.
Staff & Contacts
Leonardo Baini
We are developing a robust, compact apparatus for gas sensing in highly scattering porous matrices, utilizing solid-state tunable lasers and effective medium methods for pathlength enhancement calibration and optical porosimetry estimation.
Page updated
Report abuse