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The current percolation in polymer-sorted semiconducting (7,5) single-walled carbon nanotube (SWNT) networks, processed from solution, is investigated using a combination of electrical field-effect measurements, atomic force microcopy (AFM) and conductive AFM (C-AFM) techniques. From AFM measurements, the nanotube length in the as-processed (7,5) SWNTs network is found to range from ~100 nm to ~1500 nm, with a SWNT surface density well above the percolation threshold and a maximum surface coverage ≈ 58%. Analysis of the field-effect charge transport measurements in the SWNT network using a two-dimensional -2 -plane and reveal the isotropic nature of the as-spun (7,5) SWNT networks. This work demonstrates the tremendous potential of combining advanced scanning probe techniques with field-effect charge transport measurements for quantification of key network parameters including current percolation, surface coverage and degree of SWNT alignment. Most importantly, the proposed approach is general and applicable to other nanoscale networks, including metallic nanowires as well as hybrid nano-composites.

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In the direction of disaggregated and cognitive optical networks, this work proposes and experimentally tests a vendor-agnostic optical line controller architecture capable of autonomously setting the working point of optical amplifiers to maximize the capacity of a ROADM-to-ROADM (reconfigurable optical add–drop multiplexer) link. From a procedural point of view, once the equipment is installed, the presented software framework performs an automatic characterization of the line, span by span, to abstract the properties of the physical layer. This process requires the exploitation of monitoring devices such as optical channel monitors and optical time domain reflectometers, available, in a future perspective, in each amplification site. On the basis of this information, an optimization algorithm determines the working point of each amplifier to maximize the quality of transmission (QoT) over the entire band. The optical line controller has been experimentally tested in the laboratory using two different control strategies, achieving in both cases a homogeneous QoT for each channel close to the maximum average and an excellent match with respect to emulation results. In this framework, the Gaussian noise simulation in Python (GNPy) open source Python library is used as the physical model for optical propagation through the fiber, and the covariance matrix adaptation evolution strategy is used as an optimization algorithm to identify properties of each fiber span and to maximize the link capacity.

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