Istituto Italiano di Tecnologia

About: Istituto Italiano di Tecnologia is a facility organization based out in Genoa, Italy. It is known for research contribution in the topics: Robot & Humanoid robot. The organization has 4561 authors who have published 14595 publications receiving 437558 citations. The organization is also known as: Italian Institute of Technology & IIT.

Tutorial: guidelines for standardized performance tests for electrodes intended for neural interfaces and bioelectronics.

Abstract: Implantable neural interfaces advance the possibilities for neuroscientists to study the brain. They are also promising for use in a multitude of bioelectronic therapies. Electrode technology plays a central role in these developments, as the electrode surfaces form the physical interfaces between technology and the biological targets. Despite this, a common understanding of how electrodes should best be evaluated and compared with respect to their efficiency in recording and stimulation is currently lacking. Without broadly accepted performance tests, it is difficult to rank the many suggestions for electrode materials available in the literature, or to identify where efforts should be focused to advance the field most efficiently. This tutorial critically discusses the most relevant performance tests for characterization of neural interface electrodes and explains their implementation, interpretation and respective limitations. We propose a unified standard to facilitate transparent reporting on electrode performance, promote efficient scientific process and ultimately accelerate translation into clinical practice. This tutorial describes a set of essential performance tests for characterization of neural interface electrodes. The authors provide guidelines for standardized implementation and reporting on electrode performances.

Two Photon Absorption in II-VI Semiconductors: The Influence of Dimensionality and Size.

Abstract: We report a comprehensive study on the two-photon absorption cross sections of colloidal CdSe nanoplatelets, -rods, and -dots of different sizes by the means of z-scan and two-photon excitation spectroscopy. Platelets combine large particle volumes with ultra strong confinement. In contrast to weakly confined nanocrystals, the TPA cross sections of CdSe nanoplatelets scale superlinearly with volume (V∼2) and show ten times more efficient two-photon absorption than nanorods or dots. This unexpectedly strong shape dependence goes well beyond the effect of local fields. The larger the particles’ aspect ratio, the greater is the confinement related electronic contribution to the increased two-photon absorption. Both electronic confinement and local field effects favor the platelets and make them unique two-photon absorbers with outstanding cross sections of up to 107 GM, the largest ever reported for (colloidal) semiconductor nanocrystals and ideally suited for two-photon imaging and nonlinear optoelectronics...

Scalable production of graphene inks via wet-jet milling exfoliation for screen-printed micro-supercapacitors

Abstract: The miniaturization of energy storage units is pivotal for the development of next-generation portable electronic devices. Micro-supercapacitors (MSCs) hold a great potential to work as on-chip micro-power sources and energy storage units complementing batteries and energy harvester systems. The scalable production of supercapacitor materials with cost-effective and high-throughput processing methods is crucial for the widespread application of MSCs. Here, we report wet-jet milling exfoliation of graphite to scale-up the production of graphene as supercapacitor material. The formulation of aqueous/alcohol-based graphene inks allows metal-free, flexible MSCs to be screen-printed. These MSCs exhibit areal capacitance (Careal) values up to 1.324 mF cm-2 (5.296 mF cm-2 for a single electrode), corresponding to an outstanding volumetric capacitance (Cvol) of 0.490 F cm-3 (1.961 F cm-3 for a single electrode). The screen-printed MSCs can operate up to power density above 20 mW cm-2 at energy density of 0.064 uWh cm-2. The devices exhibit excellent cycling stability over charge-discharge cycling (10000 cycles), bending cycling (100 cycles at bending radius of 1 cm) and folding (up to angles of 180°). Moreover, ethylene vinyl acetate-encapsulated MSCs retain their electrochemical properties after a home-laundry cycle, providing waterproof and washable properties for prospective application in wearable electronics.

Functional Materials for Two-Photon Polymerization in Microfabrication

Abstract: Direct laser writing methods based on two-photon polymerization (2PP) are powerful tools for the on-demand printing of precise and complex 3D architectures at the micro and nanometer scale. While much progress was made to increase the resolution and the feature size throughout the years, by carefully designing a material, one can confer specific functional properties to the printed structures thus making them appealing for peculiar and novel applications. This Review summarizes the state-of-the-art of functional resins and photoresists used in 2PP, discussing both the range of material functions available and the methods used to prepare them, highlighting advantages and disadvantages of different classes of materials in achieving certain properties.

Object-based affordances detection with Convolutional Neural Networks and dense Conditional Random Fields

Abstract: We present a new method to detect object affordances in real-world scenes using deep Convolutional Neural Networks (CNN), an object detector and dense Conditional Random Fields (CRF). Our system first trains an object detector to generate bounding box candidates from the images. A deep CNN is then used to learn the depth features from these bounding boxes. Finally, these feature maps are post-processed with dense CRF to improve the prediction along class boundaries. The experimental results on our new challenging dataset show that the proposed approach outperforms recent state-of-the-art methods by a substantial margin. Furthermore, from the detected affordances we introduce a grasping method that is robust to noisy data. We demonstrate the effectiveness of our framework on the full-size humanoid robot WALK-MAN using different objects in real-world scenarios.