The recent technological developments made electronic devices become imperative and indispensable, being present in our daily routines, all over the world. But, the continuous exposition of people to the electromagnetic radiation might cause illness. Electrosmog is the invisible electromagnetic radiation that results from the usage of electric equipment and wireless technologies. Some studies present electro sensibility as a contemporary illness affecting more and more people. This paper analyses some of the challenges this reality puts to the fashion design and how textile materials may be used to protect the human body against the harmful radiation and to develop smart cloths incorporating textile antennas able to capture these radiations and feed low-­frequency devices. Thus, one considers the notion of “Transparent Sustainability” and the search for the smart energy explorations of/or in the human body. This way, the association of fashion design and technology can transform the garment in a sustainable communication interface.

The concept of piezoelectric energy production is based on energy-harvesting devices using generation materials such as single crystals, ceramics, polymers, and composites. These production systems can harvest wasted environmental energy and convert it essentially into electrical energy. There are different nano- and microscale power harvesters which are increasingly useful for powering mobile electronics and low-power devices, even in hardly accessible areas. Despite many efforts in the development of new materials, the most widely used materials in device applications remain the ceramics of the lead zirconate titanate family, since they still present the higher output performances in the range of milliwatts of generated power.

Energy harvesting from the environment based on electroactive polymers has been increasing in recent years. Ferroelectric polymers are used as mechanical-to-electrical energy transducers in a wide range of applications, scavenging the surrounding energy to power low-power devices. These energy-harvesting systems operate by taking advantage of the piezoelectric, pyroelectric, and magnetoelectric properties of the polymers, harvesting wasted environmental energy and converting it mainly into electrical energy. There have been developed different nano- and micro-scale power harvesters with an increasing interest for powering mobile electronics and low-power devices, including applications in remote access areas. Novel electronic devices are developed based on low-power solutions, and therefore, polymer-based materials represent a suitable solution to power these devices. Among the different polymers, the most widely used in the device application is the poly(vinylidene fluoride) (PVDF) family, due to its higher output performance.

Portability improvement of technological devices have not been followed by energy availability of its batteries. Considering the low power consumption features of a variety of portable devices, the concept of energy harvesting from environmental sources and human body has gained a new relevance. In the search of methods and materials that suit this need, are the energy generated from the piezoelectricity, thermoelectricity and electromagnetism, among others.This paper reviews the advantages, disadvantages and future trend of energy harvesting methods, as well as its mechanisms in portable medical devices with low power consumption. The medical field is a promising sector for the use of these technologies by the need to extend the energy availability for several parameter monitoring as too allow various forms of continuous therapy. Therefore, coupling a energy harvesting system to existing battery in these devices may significantly improve their energy sustainability that at present, is one of its biggest limitations.

This paper presents spectrum opportunities for radio frequency (RF) energy harvesting identified through power density measurements from 350 MHz to 3 GHz. The field trials have been performed in two different cities (Covilha and Lisbon), by using the NARDA-SMR spectrum analyser with measuring antenna, and the Signal Hound spectrum analysers, respectively. The scope of our research considers RF energy harvesting devices, enabling to convert RF energy to direct current (DC), providing an alternative source to power supply wireless sensor network (WSN) devices. Printed antennas, able to operate at GSM (900/1800) bands, are proposed with gains of the order of 1.8-2.06 dBi and efficiency 77.6-84%. Guidelines for the choice of textile materials for a wearable antenna are also provided.

Geoenvironmental engineering involves defining solutions for complex problems, such as containment systems management, contaminant transport control, wastewater management, remediation of contaminated sites and valorization of geomaterials and wastes. In the last years, energy harvesting (EH)—or energy scavenging—methods and technologies have been developed to reduce the dependence on traditional energy sources, namely fossil fuels, and nuclear power, also responding to the increase in energy demands for human activities and to fulfill sustainable development goals. EH in geoenvironmental works and the surrounding soil and water environment includes a set of processes for capturing and accumulating energy from several sources considered wasted or unusable associated with soil dynamics; the stress and strain of geomaterials, hydraulic, vibrations, biochemical, light, heating and wind sources can be potential EH systems. Therefore, this work presents a review of the literature and critical analysis on the main opportunities for EH capturing, accumulating and use in geoenvironmental works, among basic electric concepts and mechanisms, analyzing these works in complex conditions involving biological-, chemical-, mechanical-, hydraulic- and thermal-coupled actions, concluding with the main investigation and challenges within geoenvironmental aspects for EH purposes.

This paper presents the development of an innovative control, regulation and command system for hydronic radiant floors, more flexible and efficient that guarantees a better thermal comfort to the user and simultaneously improves the energy efficiency of this type of heating system. The majority of the actual control of hydronic radiant floors is done by thermostats that measure the air temperature and control the actuators (pumps and valves) in order to maintain the room at the specified temperature. These systems requires the frequent adjustment of thermostats set-point in order to obtain thermal comfort as it depends on other factors than just the air temperature, such as, the air humidity, external environmental conditions, radiant temperature, among others. This paper presents a control, regulation and command solution that requires minimum user intervention, as the user only has to choose the desirable thermal comfort level. The control algorithm is based on the calculation of PMV (Predicted Mean Vote) index as defined on Thermal Comfort Standard ISO 7730. Another advantage of the proposed system is related to the wireless and energy harvesting sensors and actuators that provide much more flexibility to the system.

Clean and renewable energy production methods have been highly researched over the last decades to fight climate changes caused by greenhouse gas emissions. Energy harvesters surged as a promising technique to overcome the inherent limitations of small and large scale forms of renewable energy production. However, power density, efficiency and energy management of such electric generators are highly dependent on forecasting methods that can accurately predict future energy values for varying mechanical excitations. Many forecasting methods have been researched in the past, but those based on deep reinforcement learning (DRL) have not yet been the subject of much research as they are relatively recent. In this study, a deep reinforcement learning-based time series forecasting model is developed and proven to predict future power outputs of an electromagnetic energy generator . Firstly, datasets related to the instantaneous power output of the generator were gathered and processed for training and testing procedures. Secondly, the proposed model was developed based on the deep deterministic policy gradient (DDPG) algorithm and optimized using the black hole (BH) heuristic optimization algorithm. Finally, a deep learning (DL) model, based on the long short-term memory (LSTM) algorithm was developed and optimized with the BH algorithm to establish an accuracy and computation time-wise comparison between DRL and DL. Experiments showed that the proposed model and the comparison model were equally able to accurately predict the future power values of the energy generator. Considering the chosen dataset for the optimization procedure, the proposed model was able to outperform the comparison model at the cost of training time. The mean squared error (MSE), the root mean squared error (RMSE) and the coefficient of determination (R2) evaluation measures of the proposed model were, respectively, 30.19%, 65.77% and 0.22% better than the comparison model. Moreover, the proposed model was 94.80% faster than the comparison model at predicting future power values. The findings within this investigation might achieve significant impact in both small and large-scale domains, as the validation of this forecasting model is a revolutionary gateway to the creation of inteligent and highly predictive energy generators.

Cardiovascular diseases remain the leading cause of death worldwide, so the importance of early diagnosis of cardiovascular diseases can hardly be overestimated. Real-time and continuous monitoring of the heart is the reality of tomorrow. Small non-invasive sensors installed directly on the skin, monitoring heart rate and blood pressure, transmitting data to a smartphone and analyzing data in flight will significantly improve cardiovascular risk prediction. In this regard, it is necessary to develop new technologies for measuring heart rate and blood pressure based on compact, accurate and energy-saving devices. Bioorganic piezoelectric materials such as diphenylalanine (FF) are promising for this application. This material is a dipeptide, which makes it simple and biocompatible. It has self-assembly properties that enable it to form various structures with piezoelectric properties. Among these structures are flexible piezoelectric thin films, which are ideal for use as sensors. In this work, structures consisting of FF films and FF vertical tubes with sufficiently high piezoelectric properties on flexible substrates were successfully prepared for the first time. The films were prepared by spin coating using acetic acid solution, which is a cheaper and more environmentally friendly solvent compared to the traditionally used HFIP. In addition, it was shown that the film can be deposited on top of PEDOT:PSS film, a conductive polymer. The vertical tubes were prepared by depositing water on the resulting film and then crystallizing it. Piezoelectric energy harvesting measurements of the resulting three-layer structure showed moderate energy production. Triboelectric measurements were not successful due to the strong triboelectric properties of the ITO/PET substrate used. However, the piezoelectric properties of the diphenylalanine vertical tubes (d₃₃=12.9 pmV ˉ¹)open the door to the preparation of multilayer structures on top of diphenylalanine films with promising properties. Thus, sensors and nanogenerator devices can be fabricated using low-cost and scalable methods.