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Abstract Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems.

Nitrogen oxides (NOx) gases, such as nitrous oxide (N2O), nitrogen oxide (NO), and nitrogen dioxide (NO2), are considered the most hazardous exhausts exhaled by industries and stationary and non-stationary application engines. Investigation of catalytic decomposition of NO has been carried out on copper ion exchanged with different bases, such as COK12, Nb2O5, Y-zeolite, and ZSM5. The catalytic decomposition of NO is widely accepted as an excellent method for the abatement of NO. However, the catalyst that achieves the highest reactivity in terms of NO decomposition is still a matter of research. The present paper aims to extend the research on the reactivity of non-noble metal-based catalysts using the direct decomposition method to remove NO from diesel engine exhaust. The reactivity of catalysts was observed in a quartz fixed bed reactor of 10 mm diameter placed in a furnace maintained at a temperature of 200 °C to 600 °C. The flow of NO was controlled by a mass flow controller, and the gas chromatography technique was used to observe the reactivity of the catalysts. Analysis showed that adding Cu to COK12, Nb2O5, Y-zeolite, and ZSM5 supports resulted in a rise in NO decomposition compared to stand-alone supports. Further experimental trials on the performance of Cu-ZSM5 at varying flow rates of NO showed that the NO decomposition activity of the catalyst was higher at lower flow rates of NO.

Surface plasmons, continuous and cumulative electron vibrations confined to metal-dielectric interfaces, play a pivotal role in aggregating optical fields and energies on nanostructures. This confinement exploits the intrinsic subwavelength nature of their spatial profile, significantly enhancing light–matter interactions. Metals, semiconductors, and 2D materials exhibit plasmonic resonances at diverse wavelengths, spanning from ultraviolet (UV) to far infrared, dictated by their unique properties and structures. Surface plasmons offer a platform for various light–matter interaction mechanisms, capitalizing on the orders-of-magnitude enhancement of the electromagnetic field within plasmonic structures. This enhancement has been substantiated through theoretical, computational, and experimental studies. In this comprehensive review, we delve into the plasmon-enhanced processes on metallic and metamaterial-based sensors, considering factors such as geometrical influences, resonating wavelengths, chemical properties, and computational methods. Our exploration extends to practical applications, encompassing localized surface plasmon resonance (LSPR)-based planar waveguides, polymer-based biochip sensors, and LSPR-based fiber sensors. Ultimately, we aim to provide insights and guidelines for the development of next-generation, high-performance plasmonic technological devices.

Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (x-ray) spectroscopies, whose prevalence has increased following the advent of modern x-ray facilities including third-generation synchrotrons and x-ray free-electron lasers. While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable ‘ black-box ’ approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational x-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental x-ray spectroscopic studies.

Optical sensing technologies for hydrogen monitoring are of increasing importance in connection with the development and expanded use of hydrogen and for transition to the hydrogen economy. The past decades have witnessed a rapid development of optical sensors for hydrogen monitoring due to their excellent features of being immune to electromagnetic interference, highly sensitive, and widely applicable to a broad range of applications including gas sensing at the sub-ppm range. However, the selection of hydrogen selective metal and metal alloy plays an important role. Considering the major advancements in the field of optical sensing technologies, this review aims to provide an overview of the recent progress in hydrogen monitoring. Additionally, this review highlights the sensing principles, advantages, limitations, and future development.

Photonic researchers are increasingly exploiting nanotechnology due to the development of numerous prevalent nanosized manufacturing technologies, which has enabled novel shape-optimized nanostructures to be manufactured and investigated. Hybrid nanostructures that integrate dielectric resonators with plasmonic nanostructures are also offering new opportunities. In this work, we have explored a hybrid coupled nano-structured antenna with stacked multilayer lithium tantalate (LiTaO3) and Aluminum oxide (Al2O3), operating at wavelength ranging from 400 nm to 2000 nm. Here, the sensitivity response has been explored of these nano-structured hybrid arrays. It shows a strong electromagnetic confinement in the separation gap (g) of the dimers due to strong surface plasmon resonance (SPR). The influences of the structural dimensions have been investigated to optimize the sensitivity. The designed hybrid coupled nanostructure with the combination of 10 layers of gold (Au) and Lithium tantalate (LiTaO3) or Aluminum oxide (Al2O3) (five layers each) having height, h1 = h2 = 10 nm exhibits 730 and 660 nm/RIU sensitivity, respectively. The sensitivity of the proposed hybrid nanostructure has been compared with a single metallic (only gold) elliptical paired nanostructure. Depending on these findings, we demonstrated that a roughly two-fold increase in the sensitivity (S) can be obtained by utilizing a hybrid coupled nanostructure compared to an identical nanostructure, which competes with traditional sensors of the same height, (h). Our innovative novel plasmonic hybrid nanostructures provide a framework for developing plasmonic nanostructures for use in various sensing applications.

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

The increasing use of nanomaterials and scalable, high-yield nanofabrication process are revolutionizing the development of novel biosensors. Over the past decades, researches on nanotechnology-mediated biosensing have been on the forefront due to their potential application in healthcare, pharmaceutical, cell diagnosis, drug delivery, and water and air quality monitoring. The advancement of nanoscale science relies on a better understanding of theory, manufacturing and fabrication practices, and the application specific methods. The topology and tunable properties of nanoparticles, a part of nanoscale science, can be changed by different manufacturing processes, which separate them from their bulk counterparts. In the recent past, different nanostructures, such as nanosphere, nanorods, nanofiber, core–shell nanoparticles, nanotubes, and thin films, have been exploited to enhance the detectability of labelled or label-free biological molecules with a high accuracy. Furthermore, these engineered-materials-associated transducing devices, e.g., optical waveguides and metasurface-based scattering media, widened the horizon of biosensors over a broad wavelength range from deep-ultraviolet to far-infrared. This review provides a comprehensive overview of the major scientific achievements in nano-biosensors based on optical fiber, nanomaterials and terahertz-domain metasurface-based refractometric, labelled and label-free nano-biosensors.

Gold nanoantennas have been used in a variety of biomedical applications due to their attractive electronic and optical properties, which are shape- and size-dependent. Here, a periodic paired gold nanostructure exploiting surface plasmon resonance is proposed, which shows promising results for Refractive Index (RI) detection due to its high electric field confinement and diffraction limit. Here, single and paired gold nanostructured sensors were designed for real-time RI detection. The Full-Width at Half-Maximum (FWHM) and Figure-Of-Merit (FOM) were also calculated, which relate the sensitivity to the sharpness of the peak. The effect of different possible structural shapes and dimensions were studied to optimise the sensitivity response of nanosensing structures and identify an optimised elliptical nanoantenna with the major axis a, minor axis b, gap between the pair g, and heights h being 100 nm, 10 nm, 10 nm, and 40 nm, respectively. In this work, we investigated the bulk sensitivity, which is the spectral shift per refractive index unit due to the change in the surrounding material, and this value was calculated as 526–530 nm/RIU, while the FWHM was calculated around 110 nm with a FOM of 8.1. On the other hand, the surface sensing was related to the spectral shift due to the refractive index variation of the surface layer near the paired nanoantenna surface, and this value for the same antenna pair was calculated as 250 nm/RIU for a surface layer thickness of 4.5 nm.