Harbinder Singh

This paper presents the design, analysis, fabrication and testing of an ultra-thin narrow-band metamaterial (MM) absorber tailored for improving the sensitivity of pressure sensors. The proposed resonator achieves an impressive absorption characteristic of 99.85% at 5.355[Formula: see text]GHz. A prototype is fabricated using standard PCB technology and experimental validation is conducted in an anechoic chamber to verify the simulated results. The sensor design incorporates an analytical diaphragm layer between two substrates, demonstrating the ability to detect minor shifts in pressure with an average sensitivity of 2080[Formula: see text]MHz/mm. Key sensor characteristics include a high quality factor of 49.28 and a substantial Figure of Merit of 43.45. The linear behavior of the sensor is quantified through curve fitting, showing good agreement with the simulated sensitivity. The designed MM absorber exhibits merit such as perfect absorptivity, linear sensitivity, narrow bandwidth, high Q-factor, and a substantial Figure of Merit, making it a strong contender for pressure sensing applications.

Over the past decade, deep learning (DL) and its use for radio frequency (RF) engineering and metamaterials has become increasingly important. The previous techniques used in RF processing and the design of metamaterials are limited to their high computation requirements, complex analytical design, and poor ability to model nonlinearities. DL offers a new class of tools to develop data-based models of RF signals and design RF systems through the process of Inverse Design, and allows for real-time analysis with very high analytical accuracy. The authors present a comprehensive discussion of prominent Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks, as well as other recent developments in DL applications for RF Engineering, including spectrum sensing, channel estimation, signal classification, and waveform generation. In addition, this paper also describes how DL models can predict the electromagnetic properties of metamaterials, which allows for inverse design and rapid prototyping using Autoencoders and Surrogate Models. Finally, this paper discusses the simulation tools, essential datasets, and performance evaluation metrics that were used to compare the performance of DL to traditional methods. While the initial results are promising, there are still issues with the current state of the art including limited data, low interpretability of the model, and poor generalizability across frequency bands and geometries. Future research directions will be provided in this paper, including the implementation of real-time intelligent metasurfaces and transfer learning using Physics-Informed Neural Networks (PINNs).

The extensive use of pesticides in modern agriculture has resulted in their persistent accumulation in environmental systems, posing significant risks to ecosystems and human health. Consequently, the development of integrated strategies for the sensitive detection and efficient removal of pesticide residues has become critically important. In this context, fluorescent inorganic–organic hybrid nanoparticles have emerged as versatile platforms owing to their tunable physicochemical properties and distinctive optical behavior. This review provides a comprehensive overview of recent advances in these hybrid nanomaterials for pesticide sensing and remediation. Particular emphasis is placed on the underlying photophysical mechanisms governing detection, including fluorescence quenching, Förster resonance energy transfer (FRET), inner filter effect (IFE), and photoinduced electron transfer (PET). In parallel, the role of interfacial interactions such as hydrogen bonding, electrostatic attraction, and π–π stacking in adsorption processes is critically discussed. Furthermore, these hybrid systems exhibit high adsorption capacities and rapid removal kinetics, enabling efficient pesticide elimination using both adsorption and catalytic degradation pathways. Overall, this review underscores the potential of fluorescent inorganic–organic hybrid nanoparticles as next-generation materials for sustainable environmental monitoring and remediation of pesticide contaminants.

For enhanced sensitivity, a theoretically designed surface plasmon resonance (SPR)-based gas sensor employing a BK7 prism, silver (Ag), nickel–zinc ferrite oxide (Ni[ ]Zn[ ]Fe₂O[ ] and zinc oxide (ZnO) is proposed using the Kretschmann configuration. In order to detect a wide range of gaseous analytes, this sensor architecture makes use of the high field confinement and charge transfer properties at the metal–dielectric interfaces. The gas sensor performance parameter is computed using the angular interrogation method in terms of sensitivity, detection accuracy (DA), and figure of merit (FoM). The maximum sensitivity of the suggested gas sensor is found to be 222.51[ ]/RIU below 0.3 (a.u.) at [ ] with remarkable FoM. The simulation results indicate that the proposed sensor achieves a maximum sensitivity of 222.51[ ]/RIU at a minimum reflectance of below 0.3 (a.u.), along with a high FoM, signifying its superior detection capability compared to conventional SPR gas sensors. The outcomes of our enhanced numerical analysis demonstrate that performance is enhanced when compared with a traditional gaseous sensor. Additionally, for the detection of NO₂ gas, the maximal sensitivity of 245.58[ ]/RIU is attained, demonstrating the sensor’s strong potential for selective and precise gas detection. Furthermore, the suggested gas sensor can identify gas analytes with different RI. Therefore, using a BK7 prism with the suggested gas sensor can be more effective for detecting various gases at 633[ ]nm wavelengths.

Metasurfaces have been widely investigated across the disciplines of optical biosensing due to their exceptional ability to manipulate electromagnetic fields. Consequently, over the past few years, there has been growing interest in the application of metasurfaces in optical biosensors in the field of biomedical sensing. While being label-free and offering real-time tracking, high sensitivity, and a quick response are among the benefits of conventional optical biosensors, the incorporation of metasurfaces improves their wavefront manipulation, selectivity for versatile sensing, and capacity for device miniaturization to satisfy increasingly complex application requirements. Furthermore, there is a lack of comprehensive evaluations that address the current research developments and future possibilities, despite the encouraging advancements in this emerging field. Hence, this work provides a comprehensive review and serves as a valuable resource for researchers exploring metasurface-based optical biosensors. This review delves into defining the basic sensing concepts, design procedures, and important figures of merit (FOM) for metasurface-based optical biosensors and their applications, including the detection of numerous analytes, such as viruses, toxins, antibodies, tumors, and drugs and the monitoring of blood sugar. Furthermore, presenting a critical evaluation of structural fabrication techniques with a view toward potential future commercialization, this review ends by highlighting several fascinating areas for further investigation. For this review article, a total of 5844 effective documents about metasurface optical biosensors were retrieved from the Scopus database. The VOSviewer version 1.6.20 bibliometric software was used for the scientific analysis of the data retrieved from the Scopus database from 2010 to 2025.

The extraction of metallic nanoparticles (MNPs) from waste electrical and electronic equipment (WEEE) has gained extensive attention from researchers for eco-friendly, reliable, and sustainable alternative protocol over the traditional linear economic approach (make-use-dispose) for boosting the circular economy. A plethora of MNPs including metals/metal oxide nanoparticles having a size dimension ranging from 1–100 nanometers (nm) have been extracted from these WEEE by using different chemical, physical, and biological methods. Recovery of certain precious MNPs can be achieved by dismantling and recycling electronic waste items in the form of gold (Au), platinum (Pt), zinc oxide (ZnO), silver (Ag), and copper oxide (CuO). These MNPs provide a huge range of applications such as antibacterial, therapeutic, target drug delivery, and biotechnological applications. This comprehensive review provides in-depth knowledge of the synthesis of MNPs using different techniques from WEEE and delves into their potential applications in biomedical fields with in-depth mechanisms. This article also discussed global challenges and opportunities in this area for adopting the concept of circular economy to conserve natural resources for future generations and hence create a greener environment and protect our planet.

Plant-derived carbon dots (P-CDs) are gaining attention in environmental remediation due to their cost-effectiveness, availability, and lower toxicity compared with chemically synthesized carbon dots. This review comprehensively examines the recent advancements in the synthesis and application of P-CDs, with a particular emphasis on their efficacy in the sensing of heavy metals, which are among the most pervasive environmental contaminants. A detailed comparative analysis is presented by evaluating the performance of P-CDs against their chemically synthesized counterparts based on key parameters, such as optimal operating conditions and detection limits. Furthermore, sensing the potential of P-CDs towards every heavy metal ion has been discussed with in-depth mechanistic insights. Additionally, this review explores the industrial applications and future directions of P-CDs. This review provides a comprehensive analysis of -P-CDs for heavy metal sensing, aiming to enhance their sensitivity and selectivity toward heavy metal ions.