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This paper proposes a novel methodology based on the Genetic Programming (GP) to derive behavioral models describing the transient evolution of the terminal voltage of a battery. These models analytically relate the battery voltage to its state of charge, charge/discharge rate, and temperature. Compared to the popular equivalent circuit-based models, one of the main advantages is the significant reduction of the effort to produce the experimental dataset required to identify the model parameters. The GP generates a family of optimal “candidate” analytical models, each associated with suitable metrics that quantify performance indicators like simplicity and accuracy. The methodology is applied to describe the transient discharge phase of a Lithium Iron Phosphate (LiFePO4 or LFP) battery under realistic operating conditions, considering the state-of-charge between 20% and 80%, discharge rates comprised between 0.25C and 1C, and temperature ranging from 5°C to 35°C. The GP provides different solutions that can be chosen by imposing the desired trade-off between accuracy and simplicity. Two models are selected and validated against experimental results. The chosen models guarantee a quite low level of the relative root mean square error (maximum 0.31% and 0.22%, respectively) over the range of analysis.

This paper investigates the possibility of realizing ice sensors based on the electrical response of thin strips made from pressed graphene nano-platelets. The novelty of this work resides in the use of the same graphene strips that can act as heating elements via the Joule effect, thus opening the route for a combined device able to both detect and remove ice. A planar capacitive sensor is designed and fabricated, in which the graphene strip acts as one of the armatures. The sensing principle is based on the high sensitivity of the planar capacitor to the change in electrical permittivity in the presence of ice, as shown in the experimental case study discussed here, can also be interpreted by means of a simple circuit and electromagnetic model. The properties of the sensor are analyzed, and the frequency range for its use as an ice detector has been established.

The design and optimization of coils for Inductive Power Transfer (IPT) systems is an iterative process conducted in Finite Element (FE) tools that takes a lot of time and computational resources. In order to overcome such limitations in the design process, new empirical equations for the evaluation of the self-inductance and mutual inductance values are proposed in this work. By means of a multi-objective genetic programming algorithm, the self-inductance, the mutual inductance and the coupling factor values obtained from FE simulations of IPT link are accounted by analytical equations, based on the geometric parameters defining the IPT link. The behavioral modeling results are compared with both FE-based and experimental results, showing a good accuracy.

The paper proposes novel solutions to improve the signal and thermal integrity of crossbar arrays of Resistive Random-Access Memories, that are among the most promising technologies for the 3D monolithic integration. These structures suffer from electrothermal issues, due to the heat generated by the power dissipation during the write process. This paper explores novel solutions based on new architectures and materials, for managing the issues related to the voltage drop along the interconnects and to thermal crosstalk between memory cells. The analyzed memristor is the 1 Diode - 1 Resistor memory. The two architectural solutions are given by a reverse architecture and a complementary resistive switching one. Compared to conventional architectures, both of them are also reducing the number of layers where the bias is applied. The electrothermal performance of these new structures is compared to that of the reference one, for a case-study given by a 4 × 4 × 4 array. To this end, a full-3D numerical Multiphysics model is implemented and successfully compared against other models in literature. The possibility of changing the interconnect materials is also analyzed. The results of this performance analysis clearly show the benefits of moving to these novel architectures, together with the choice of new materials.

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about ×25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments.

Water pollution is nowadays a global problem and the effective detection of pollutants is of fundamental importance. Herein, a facile, efficient, robust, and rapid (response time < 2 min) method for the determination of important quinone-based industrial pollutants such as hydroquinone and benzoquinone is reported. The recognition method is based on the use of screen-printed electrodes as sensing platforms, enhanced with carbon-based nanomaterials. The enhancement is achieved by modifying the working electrode of such platforms through highly sensitive membranes made of Single- or Multi-Walled Carbon Nanotubes (SWNTs and MWNTs) or by graphene nanoplatelets. The modified sensing platforms are first carefully morphologically and electrochemically characterized, whereupon they are tested in the detection of different pollutants (i.e., hydroquinone and benzoquinone) in water solution, by using both cyclic and square-wave voltammetry. In particular, the sensors based on film-deposited nanomaterials show good sensitivity with a limit of detection in the nanomolar range (0.04 and 0.07 μM for SWNT- and MWNT-modified SPEs, respectively) and a linear working range of 10 to 1000 ppb under optimal conditions. The results highlight the improved performance of these novel sensing platforms and the large-scale applicability of this method for other analytes (i.e., toxins, pollutants).

This paper provides a study of some relevant electro-thermal properties of commercial films made by pressed graphene nano-platelets (GNPs), in view of their use as heating elements in innovative de-icing systems for aerospace applications. The equivalent electrical resistivity and thermal emissivity were studied, by means of models and experimental characterization. Macroscopic strips with a length on the order of tens of centimeters were analyzed, either made by pure GNPs or by composite mixtures of GNPs and a small percentage of polymeric binders. Analytical models are derived and experimentally validated. The thermal response of these graphene films when acting as a heating element is studied and discussed.

This paper proposes an optimal strategy to parallelize the solution of large 3D magneto-quasi-static (MQS) problems, by combining the MPI and OpenMP approaches. The studied numerical problem comes from a weak-form integral formulation of a MQS problem and is finally cast in terms of a large linear system to be solved by means of a direct method. For this purpose, two main tasks are identified: the assembly and the inversion of the matrices. The paper focuses on the optimization of the resources required for assembling the matrices, by exploiting the feature of a hybrid OpenMP–MPI approach. Specifically, the job is shared between clusters of nodes in parallel by adopting an OpenMP paradigm at the node level and a MPI one at the process level between nodes. Compared with other solutions, such as pure MPI, this hybrid parallelization optimizes the available resources, with respect to the speed, allocated memory, and the communication between nodes. These advantages are clearly observed in the case studies analyzed in this paper, coming from the study of large plasma fusion machines, such as the fusion reactor ITER. Indeed, the MQS problems associated with such applications are characterized by a huge computational cost that requires parallel computing approaches.

A new possible mechanism of signal detection in the THz range is investigated, based on the excitation of resonances due to the tunneling effect between two graphene nanoribbons. A simple detector is proposed, where two graphene nanoribbons are used to contact two copper electrodes. The terminal voltages are shown to exhibit strong resonances when the frequency of an external impinging field is tuned to the characteristic tunneling frequency of the graphene layer pair. An electrodynamic model for the electron transport along the graphene nanoribbons is extended here to include the tunneling effect, and a coupled transmission line model is finally derived. This model is able to predict not only the tunneling resonance, but also the well-known plasmon resonances, related to the propagation of slow surface waves.

A semi-classical electrodynamical model is derived to describe the electrical transport along graphene, based on the modified Boltzmann transport equation. The model is derived in the typical operating conditions predicted for future integrated circuits nano-interconnects, i.e., a low bias condition and an operating frequency up to 1 THz. A generalized non-local dispersive Ohm’s law is derived, which can be regarded as the constitutive equation for the material. The behavior of the electrical conductivity is studied with reference to a 2D case (the infinite graphene layer) and a 1D case (the graphene nanoribbons). The modulation effects of the nanoribbons’ size and chirality are highlighted, as well as the spatial dispersion introduced in the 2D case by the dyadic nature of the conductivity.

This paper proposes an efficient technique to solve the electromagnetic scattering problem, in the near zone of scatterers illuminated by external fields. The technique is based on a differential formulation of the Helmholtz equation discretized in terms of a finite element method (FEM). In order to numerically solve the problem, it is necessary to truncate the unbounded solution domain to obtain a bounded computational domain. This is usually done by defining fictitious boundaries where absorbing conditions are imposed, for example by applying the perfect matching layer (PML) approach. In this paper, these boundary conditions are expressed in an analytical form by using the Dirichlet-to-Neumann (DtN) operator. Compared to classical solutions such as PML, the proposed approach based on the DtN: (i) avoids the errors related to approximated boundary conditions; (ii) allows placing the boundary in close proximity to the scatterers, thus, reducing the solution domain to be meshed and the related computational cost; (iii) allows dealing with objects of arbitrary shapes and materials, since the shape of the boundary independent from those of the scatterers. Case-studies on problems related to the scattering from cable bundles demonstrate the accuracy and the computational advantage of the proposed technique, compared to existing ones.

This paper investigates the electrical properties in the microwave range of a contact made by graphene nanoplatelets. The final goal is that of estimating the range of values for the equivalent electrical complex permittivity of a contact obtained by integrating low-cost graphene in the form of nanoplatelets (GNPs) into a high-frequency electrical circuit. To this end, a microstrip-like circuit is designed and realized, where the graphene nanoplatelets are self-assembled into a gap between two copper electrodes. An experimental characterization is carried out, both to study the structural properties of the nanomaterials and of the realized devices, and to measure the electromagnetic scattering parameters in the microwave range by means of a microstrip technique. A full-wave electromagnetic model is also derived and used to investigate the relationship between the measured quantities and the physical and geometrical parameters. The combined use of the experimental and simulation results allows for retrieving the values of the equivalent complex permittivity. The equivalent electrical conductivity values are found to be well below the values expected for isolated graphene nanoplatelets. The real part of the electrical relative permittivity attains values comparable to those obtained with GNP nanocomposites.

This paper deals with the electromagnetic compatibility (EMC) issues related to the direct and radiated emissions from a high-speed integrated circuits (ICs). These emissions are evaluated here by means of circuital and electromagnetic models. As for the conducted emission, an equivalent circuit model is derived to describe the IC and the effect of its loads (package, printed circuit board, decaps, etc.), based on the Integrated Circuit Emission Model template (ICEM). As for the radiated emission, an electromagnetic model is proposed, based on the superposition of the fields generated in the far field region by the loop currents flowing into the IC and the package pins. A custom experimental setup is designed for validating the models. Specifically, for the radiated emission measurement, a custom test board is designed and realized, able to highlight the contribution of the direct emission from the IC, usually hidden by the indirect emission coming from the printed circuit board. Measurements of the package currents and of the far-field emitted fields are carried out, providing a satisfactory agreement with the model predictions.

Understanding unwanted mutual interactions between devices at the nanoscale is crucial for the study of the electromagnetic compatibility in nanoelectronic and nanophotonic systems. Anomalous electromagnetic coupling (crosstalk) between nanodevices may arise from the combination of electromagnetic interaction and quantum entanglement. In this paper we study in detail the crosstalk between two identical nanodevices, each consisting of a quantum emitter (atom, quantum dot, etc), capacitively coupled to a pair of nanoelectrodes. Using the generalized susceptibility concept, the overall system is modeled as a two-port within the framework of the electrical circuit theory and it is characterized by the admittance matrix. We show that the entanglement changes dramatically the physical picture of the electromagnetic crosstalk. In particular, the excitation produced in one of the ports may be redistributed in equal parts between both the ports, in spite of the rather small electromagnetic interactions. Such an anomalous crosstalk is expected to appear at optical frequencies in lateral GaAs double quantum dots. A possible experimental set up is also discussed. The classical concepts of interference in the operation of electronic devices, which have been known since the early days of radio-communications and are associated with electromagnetic compatibility, should then be reconsidered at the nanoscale.