Machine learning (ML)-assisted design of epsilon-negative polymer nanocomposites requires a clear connection between experimentally controllable synthesis parameters, core-shell nanoparticle geometry, and the resulting effective optical response. The targeted optical response is unusual because the polymer film is predicted to exhibit near-zero or negative real effective permittivity in selected visible spectrum regions, arising from Ag core plasmonic polarizability, SiO2-mediated dielectric spacing, nanoparticle filling factor, and effective medium coupling rather than from the intrinsic polymer matrix. In this study, a two-stage ML-assisted synthesis-to-optics framework is developed for Ag@SiO2 core-shell nanoparticle/polymer composite films intended for visible spectrum effective permittivity screening. In the first stage, St & ouml;ber synthesis parameters, including water volume, ethanol volume, TEOS content, catalyst volume, reaction time, Ag nanoparticle size, and Ag nanoparticle concentration, were used to predict SiO2 shell thickness. In the second stage, Ag core size, SiO2 shell thickness, wavelength, and nanoparticle filling factor were used to screen the real effective permittivity of Ag@SiO2/polymer nanocomposites within an effective medium design space. Using a duplicate-aware validation workflow, Gradient Boosting provided the strongest held-out test performance for shell thickness prediction, with a test R-2 of 0.8997, MAE of 7.1822 nm, RMSE of 8.8344 nm, and cross-validation R-2 of 0.5371 +/- 0.4648. The relatively large cross-validation variability indicates that the model is useful for interpolation-based synthesis screening but should not be interpreted as fully robust across heterogeneous literature-derived data. For the optical response task, the highest held-out test performance was obtained by a Decision Tree model (test R-2 = 0.7586), but cross-validation results were unstable, indicating that the epsilon model should be interpreted as a design space screening tool rather than a generalizable predictor. Design window analysis identified candidate negative effective permittivity regions primarily at 400 nm and high nanoparticle filling factor, with predicted Re ( epsilon(eff) ) values ranging from -5.4229 to -0.2086 across selected windows. The main contribution of this work is the treatment of SiO2 shell thickness as a bridge variable between St & ouml;ber-derived synthesis control and effective permittivity screening. Experimental validation remains necessary to confirm the predicted design windows, particularly because shell uniformity, Ag core polydispersity, nanoparticle aggregation, polymer dispersion, high-filling-factor feasibility, and effective medium validity can strongly influence the measured optical response.

An epsilon-negative metamaterial (ENM) containing core@shell nanoparticles (NPs) was designed, where silver (Ag) NPs served as core and silica (SiO2) was used as spacer shell. AgNPs were synthesized in large scale, using microwave-assisted polyol method, in three average particle sizes, as 30, 54, and 61 nm, with a narrow particle size distribution. Optical absorption of Ag NPs was investigated using UV-Vis spectroscopy. Their optical behavior was also theoretically predicted for different thicknesses of the SiO2 shell immersed in media of different refractive indices using the Clausius Mossotti equation. Based on the results, optimal outputs were obtained with a SiO2 shell of 10 nm in thickness encompassing 54 nm Ag NPs based on the analytical model and numerical simulations here developed for core-shell structures. Then 10 nm SiO2 shell was grown on 54 nm Ag NPs by sol-gel synthesis. The NPs were then characterized by UV-Vis, TEM, SEM, EDX, DLS, and zeta potential analyses. The synthesized core-shell NPs can be used to establish epsilon-negative properties in polymer layers within visible range of wavelengths.

The world is in a frustrating situation, which is exacerbating due to the time-consuming process of the COVID-19 vaccine design and production. This chapter provides a comprehensive investigation of fundamentals, state-of-the-art and some perspectives to speed up the process of the design, optimization and production of the medicine for COVID-19 based on Deep Learning (DL) methods. The proposed platforms are able to be used as predictors to forecast antigens during the infection disregarding their abundance and immunogenicity with no requirement of growing the pathogen in vitro. First, we briefly survey the latest achievements and fundamentals of some DL methodologies, including Deep Boltzmann Machines (DBM), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Hopfield network and Long Short-Term Memory(LSTM). These techniques help us to reach an integrated approach for drug development by non-conventional antigens. We then propose several DL-based platforms to utilize for future applications regarding the latest publications and medical reports. Considering the evolving date on COVID-19 and its ever-changing nature, we believe this survey can give readers some useful ideas and directions to understand the application of Artificial Intelligence (AI) to accelerate the vaccine design not only for COVID-19 but also for many different diseases or viruses.

The graphene-based Field Effect Transistors (GFETs), due to their multi-parameter characteristics, are growing rapidly as an important detection component for the apt detection of disease biomarkers, such as DNA, in clinical diagnostics and biomedical research laboratories. In this paper, the non-equilibrium Green function (NEGF) is used to create a compact model of GFET in the ballistic regime as an important building block for DNA detection sensors. In the proposed method, the self-consistent solutions of two-dimensional Poisson's equation and NEGF, using the nearest neighbor tight-binding approach on honeycomb lattice structure of graphene, are modeled as an efficient numerical method. Then, the eight parameters of the phenomenological ambipolar virtual source (AVS) circuit model are calibrated by a least-square curve-fitting routine optimization algorithm with NEGF transfer function data. At last, some parameters of AVS that are affected by induced charge and potential of DNA biomolecules are optimized by an experimental dataset. The new compact model response, with an acceptable computational complexity, shows a good agreement with experimental data in reaction with DNA and can effectively be used in the plan and investigation of GFET biosensors.

Nanomaterials have become crucial to develop new technologies in several practical applications fields. Until now nanostructures have been mostly associated with electromagnetism and optics. The aim of this letter is to extend the applicability of such structures also to other wave-based phenomena, such as thermodynamics. Here, in analogy to electric nanocircuits, we present the concept of thermal circuit nanoelements. The basic circuit elements, namely, resistors, capacitors and inductors, are evaluated in terms of electromagnetic (electric permittivity epsilon) and thermal (conductivity k and convection coefficient h) nanostructure properties. Coupled nanocircuits and parallel/series combinations are also developed. The multi-functional nanostructure can simultaneously control and manipulate both electromagnetic and thermal waves, paving the way to realize more complex electrical and thermal devices.

The demand for medical follow-up is rising worldwide. Ensuring fast, reliable and efficient diagnostics would help meeting the demand by avoiding delays and errors. A promising technology in molecule and compound detection are the electromagnetic sensors, which received a huge attention in the last few years; mainly due to the increasing demand of devices able to improve quality, performance and safety in different industrial sectors. Despite reliable diagnostic technologies have been developed, some drawbacks are still present with the most common technologies: bandwidth, large dimensions and limited response control. Metasurfaces, bi-dimensional engineered materials exhibiting exotic electromagnetic properties, are representing an optimal solution to overcome these issues, permitting to enhance the existing diagnostic systems. However, they suffer from uneven design and manufacture processes. Therefore, in this paper, metasurface-based sensors are proposed and realized by using a new uniformized design method and an additive manufacturing process. The structures are finally experimentally tested and verified in different medical diagnostic applications, namely: cancer stage recognition and glucose/sugar levels measurement. The high performances shown by such meta-sensors, in terms of selectivity and sensibility, pave a new way to realize advanced platforms for non-invasive, high quality and faster patient diagnosis.

Electromagnetic sensors have received huge attention in the last few years, due to the rising demand of devices able to improve quality, performance and safety in different industrial sectors: both sensing and medical industries are outstanding examples. Despite reliable diagnostic technologies have been developed, some drawbacks are still present: bandwidth, large dimensions and limited response control. Metasurfaces, bi-dimensional engineered materials, represent an optimal solution to overcome such issues enhancing existing systems for an accurate diagnosis. Therefore, in this paper, metasurface-based sensors are proposed and realized by using new additive manufacturing processes. The structures are finally experimentally tested and verified in different medical diagnostic applications, namely: cancer stage recognition, glucose/sugar levels measurements and blood oxygen saturation detection. The high performances shown by such meta-sensors, in terms of selectivity and sensibility, pave a new way to realize advanced platforms for non-invasive, high quality and faster patient diagnosis.

In the last decade, electromagnetic metamaterials, thanks to their exotic properties and the possibility to control electromagnetic waves at will, become crucial building blocks to develop new and advanced technologies in several practical applications. Even though until now the metamaterial concept has been mostly associated with electromagnetism and optics; the same concepts can be also applied to other wave phenomena, such as thermodynamics. For this reason, here the aim is to realize a multi-functional metamaterial able to control and manipulate simultaneously both electromagnetic and thermal waves. The structure is, first, designed and fabricated by using additive manufacturing techniques. Then, it is tested for the following industrial applications: sensing and medical diagnostics (optical and thermal imaging), military/safety (electromagnetic and thermal guiding structures) and automotive (electrical vehicles battery electric and thermal management). Experimental results reveal that such multifunctional metamaterial can fully manipulate and process both electromagnetic and thermal waves at will. The proposed structure appears to be highly versatile and scalable, with great potential to be used also for other wave phenomena such as mechanics, acoustics and hydrodynamics.

High-yield monodispersed silver (Ag) nanospheres were modeled, designed, and synthesized by microwaveassisted (MW-assisted) polyol method from AgNO3, polyvinyl pyrrolidone (PVP), and ethylene glycol (EG), as precursors, at 145 degrees C within a short reaction time of 2 min, and the results were compared to those of conventional polyol method. Maintaining the PVP:AgNO3 molar ratio, the effect of increasing the amounts of AgNO3 and PVP at a constant amount of EG (40 mL) on the final product was evaluated. The synthesized nanoparticles (NPs) were characterized by SEM, UV-Vis spectroscopy, FTIR and DLS analysis. The results showed that with increasing the amount of AgNO3 to 0.5 and 1 g, monodispersed Ag nanoparticles (Ag NPs) with particle sizes of 54 and 61 nm were formed, as per the plasmon absorption peaks at 436 and 442 nm, respectively. Moreover, using 40 mL of the EG solution, we could obtain a high yield of the NPs (similar to 90%). The sub-gram yield was excellently high, offering great opportunities for commercializing the procedure. Also, the proposed study paves a new way for Ag NPs realization for different practical applications ranging from MW to optics.

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and achieved good performances. Despite the plethora of literature, limitations are still present: limited response control, narrow bandwidth, and large dimensions. MetaSurfaces, artificial 2D materials with peculiar electromagnetic properties, can help to overcome such issues. In this paper, a generic tool to model, design, and manufacture MetaSurface sensors is developed. First, their properties are evaluated in terms of impedance and constitutive parameters. Then, they are linked to the structure physical dimensions. Finally, the proposed method is applied to realize devices for advanced sensing and medical diagnostic applications: glucose measurements, cancer stage detection, water content recognition, and blood oxygen level analysis. The proposed method paves a new way to realize sensors and control their properties at will. Most importantly, it has great potential to be used for many other practical applications, beyond sensing and diagnostics.

Artificial sheet materials, known as MetaSurfaces, have been applied to fully control both space and surface waves due to their exceptional abilities to dynamically tailor wave fronts and polarization states, while maintaining small footprints. However, previous and current designs and manufactured MetaSurfaces are limited to specific types of surfaces. There exists no general but rigorous design methodology for MetaSurfaces with generic curvature. The aim of this paper is to develop an analytical approach to characterize the wave behavior over arbitrary curvilinear MetaSurfaces. The proposed method allows us to fully characterize all propagating and evanescent wave modes from the MetaSurfaces. We will validate the proposed technique by designing, realizing and testing an ultrathin MetaSurface cloak for surface waves. Good results are obtained in terms of bandwidth, polarization independence and fabrication simplicity.

Huge interest in controlling and manipulating electromagnetic waves is recently and strongly evolving, mainly due to the growing demand of reliable technologies in several application fields, such as communications, healthcare, military and safety. Despite the plethora of technologies existing nowadays, limitations are still present, such as limited speed, low response control, narrow bandwidth, and large dimensions of electronic devices. In this scenario, new engineered materials with unprecedented electromagnetic properties (i.e., MetaSurfaces) can represent a very suitable technology solution. In this paper, a MetaSurface structure is modelled, designed, manufactured and experimentally tested, to be used in several applications. Specifically, results are presented for (i) advanced medical diagnostics, (ii) enhance communication signals along curvilinear paths (i.e., bend optical fibers) and (iii) cloaking applications. Experimental results reveal that the MetaSurface can fully manipulate and process electromagnetic waves at will. The proposed structure appears to be highly versatile and scalable, with great potential to be used for many other practical applications.

Sensors and their applications have received attention in the last few years, mainly due to the high demand of devices able to make improvements in quality, performance and safety across many industrial sectors. The automotive and medical industries being outstanding examples. Different technologies have been used in this regard and have produced reliable devices and systems. Despite this, some crucial technical drawbacks, which limit performance and reliability, are still present: limited response control, bandwidth, and relatively large dimensions. Metasurfaces, engineered electromagnetic materials, can overcome and/or mitigate such issues. In this paper, devices based on metasurface technology for advanced sensing and medical diagnostic applications are manufactured and experimentally validated. First, their properties are designed in terms of electromagnetic parameters and structure physical dimensions. An additive manufacturing process will use the specifications to physically realize the sensors. They will then be experimentally tested in a variety of bio-medical applications. Metasurfaces pave a new way to realize sensors and use them for many practical applications beyond sensing and diagnostics.

Recently, a huge interest has been raised in controlling and manipulating electromagnetic waves by means of MetaSurfaces. This type of material makes possible the control of phase and amplitude of electromagnetic waves paving the way of different applications, like cloaking and communications in THz band. In this paper, we propose a novel approach for designing metasurface-based structures that are independent from the geometry and the frequency. As a proof of concept, we realize a 3D curvilinear metasurface and we show this approach can be effectively applied in different applications. In particular, for the THz-based system, we consider a transmitting antenna emitting a signal that impinges on the metasurface. Our analysis focuses on the evaluation of the attenuation of the incident signal on the metasurface in respect of characteristic design parameters, the length of the medium and the frequency.

MetaSurfaces are used to fully control electromagnetic waves' propagation properties. Specifically, in this paper we present a unified approach, consisting in modeling, design and practical realization, to manufacture arbitrary curvilinear MetaSurfaces. We will validate the proposed technique by designing, realizing and testing a MetaSurface structure for sensing and telecommunications applications. Good results are obtained in terms of bandwidth, polarization independence and fabrication simplicity. Most importantly, the proposed approach appears to be versatile and scalable.

We present a new approach to model, design and realize MetaSurface structures for sensing and telecommunications. The method turns out to be versatile and has great potential to be used for practical electromagnetic applications.

A modeling and design approach is proposed for nanoparticle-based electromagnetic devices. First, the structure properties were analytically studied using Maxwell’s equations. The method provides us a robust link between nanoparticles electromagnetic response (amplitude and phase) and their geometrical characteristics (shape, geometry, and dimensions). Secondly, new designs based on “metamaterial” concept are proposed, demonstrating great performances in terms of wide-angle range functionality and multi/wide behavior, compared to conventional devices working at the same frequencies. The approach offers potential applications to build-up new advanced platforms for sensing and medical diagnostics. Therefore, in the final part of the article, some practical examples are reported such as cancer detection, water content measurements, chemical analysis, glucose concentration measurements and blood diseases monitoring.

Recently, there have been a new interest in controlling and manipulating electromagnetic waves in different frequency ranges. The development of new two-dimensional materials, called meta-surfaces, have been used to control and tailor the electromagnetic waves propagation properties. Several studies focused their attention on phase modulation, very few on amplitude. However, there have been no general design procedure able to control both of them at the same time. Therefore, the aim of this paper is to develop a robust design tool to manipulate the electromagnetic wave behavior over flat and 3D objects. The proposed approach will be used to design, manufacture and experimentally measure metasurface-based devices for lenses, cloaking, manipulation and bending applications. Good agreement between analytical, numerical, and experimental results has been achieved, proving that the realized devices are wideband, polarization independent, easy to fabricate and can be scaled in different frequency ranges from microwave to optics.

Recently, a huge interest emerged in the field of surface waves, in particular the possibility to control and manipulate their propagation properties at will. The aim of this paper is to present a new approach to design new devices for surface waves manipulation. All the steps will be considered and interlinked each other: from modeling to manufacturing, through design. To validate the proposed approach, two structures, an alldielectric device, and a meta-surface implementation, for surface wave cloaking will be presented and compared. The proposed method offers great potential, not only in terms of device properties design, but also it makes easier the manufacturing process, in different practical application fields.