The field of flexible antennas is witnessing an exponential growth due to the demand for wearable devices, Internet of Things (IoT) framework, point of care devices, personalized medicine platform, 5G technology, wireless sensor networks, and communication devices with a smaller form factor to name a few. The choice of non-rigid antennas is application specific and depends on the type of substrate, materials used, processing techniques, antenna performance, and the surrounding environment. There are numerous design innovations, new materials and material properties, intriguing fabrication methods, and niche applications. This review article focuses on the need for flexible antennas, materials, and processes used for fabricating the antennas, various material properties influencing antenna performance, and specific biomedical applications accompanied by the design considerations. After a comprehensive treatment of the above-mentioned topics, the article will focus on inherent challenges and future prospects of flexible antennas. Finally, an insight into the application of flexible antenna on future wireless solutions is discussed.

Wearable sensors are becoming an important tool for healthcare monitoring as they can provide continuous monitoring of clinically important biomarkers released in the biofluids such as sweat, saliva, tears, urine and interstitial fluids (ISF). Development of smart sensing technologies for stress detection in daily life is find applications in various field including healthcare, defence and sports industries. Although different modalities for stress detection are performed in the clinical laboratories, the measurement of hormone release from glands of the endocrine system is receiving wide attention. Cortisol is considered as a biomarker of stress as the levels are spike in response to stressors. Although most of the cortisol released from adrenaline glands bound by corticosteroid-binding globulin (CBG), active form of free cortisol is also appearing in the biofluids (sweat, saliva, urine, tear, and ISF). Electrochemical biosensors, due to their relative small farm factors and sensitivity are focused for developing wearable biosensors for non-invasive monitoring of biochemical markers. Recent technological advancements in the field of electrochemical sensors and material engineering have contributed widely to the growth of new generation wearable point of care (POC) systems that can allow for early diagnosis. Receptor molecules including antibodies, enzyme fragments, molecularly imprinted polymers, and other biomimetic materials have been explored for electrochemical sensing of cortisol. Interfacing of theses sensing strategies into fabrics and flexible wearable patches for sweat cortisol analysis are also attempted. This mini review focuses on development made in the area of cortisol sensors using fabrics and flexible substrates potential for wearable sensor applications. An overview of cortisol secretion in the body and its availability in different biological fluids are also discussed. The issues associated with realizing complete wearable sensor devices are also explored.

Electrochemical sensors have become a pivotal tool in ensuring the safety and security of the global food supply chain, which is crucial for public health, economic stability, and environmental sustainability. Modern food systems, with their complex global distribution and varied processing methods, require advanced solutions for detecting contaminants and maintaining food quality. This review delves into recent advancements in electrochemical food sensor technology, highlighting their operating principles, types, cutting-edge materials, and methods enhancing their effectiveness. These sensors are adept at identifying a broad range of foodborne pathogens, chemical contaminants, and adulterants while monitoring food freshness and quality. Innovations include using nanomaterials and conductive polymers and shifting towards miniaturized, portable devices for on-site and real-time analysis. The review also addresses challenges such as sensitivity, selectivity, and matrix effects, pointing out emerging trends and future research avenues to overcome these hurdles. Regulatory and standardization issues relevant to adopting these technologies in food safety protocols are also considered. Highlighting the last three years, this review emphasizes the indispensable role of electrochemical sensors in boosting food safety and security and the need for ongoing innovation and cross-disciplinary cooperation to advance this area.

In this article, an inkjet-printed circular-shaped monopole ultra-wideband (UWB) antenna with an inside-cut feed structure was implemented on a flexible polyethylene terephthalate (PET) substrate. The coplanar waveguide (CPW)-fed antenna was designed using ANSYS high-frequency structural simulator (HFSS), which operates at 3.04–10.70 GHz and 15.18–18 GHz (upper $K_u$ band) with a return loss < −10 dB and a VSWR < 2. The antenna, with the dimensions of 47 mm × 25 mm × 0.135 mm, exhibited omnidirectional radiation characteristics over the entire impedance bandwidth, with an average peak gain of 3.94 dBi. The simulated antenna structure was in good agreement with the experiment’s measured results under flat and bending conditions, making it conducive for flexible and wearable Internet of things (IoT) applications.

As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.