Mahindra University

The ability to bend, stretch, and roll metamaterial devices on flexible substrates adds a new dimension to aspects of manipulating electromagnetic waves and promises a new wave of device designs and functionalities. This work reviews terahertz and optical metamaterials realized on flexible and elastomeric substrates, along with techniques and approaches to lend tunability to the devices. Substrate electromagnetic and mechanical characteristics suitable for flexible metamaterials are summarized for readers, followed by fabrication and processing techniques, and finally novel approaches used to-date to attain tunability. Future directions and emerging areas of interests are identified with these promising to transform metamaterial design and translate metamaterials into practical devices.

The continued increase in urbanisation and industrialisation across the world has dramatically increased the amount and variety of waste, and, in particular, wastewater, being generated. Wastewaters contain a large variety of both organic and inorganic contaminants. Various wastewater treatment technologies have been developed over the last few decades to address the increasing concern around effective contaminant removal from wastewater. Electrocoagulation (EC) is one such technology that is broad-based, highly reliable, and cost-effective. It also has a high pollutant removal efficiency and generates less sludge when compared with other techniques. However, despite being effectively used to treat a wide range of wastewater, a thorough examination of its efficiency under various process variables has not been critically examined. Various operating factors, such as pH, current density, the conductivity of the solution, electrode material, and mixing conditions, impact the electrocoagulation system. This paper aims to provide a comprehensive overview of the electrocoagulation technique and examine the current challenges to the efficiency of the technique due to the various operating conditions. Some recent advances in the EC technology that present opportunities to improve treatment efficiency and increase the scope to treat newer varieties of wastewater are addressed.

Microplastic disposal into riverine ecosystems is an emergent ecological hazard that mainly originated from land-based sources. This paper presents a comprehensive review on physical processes involved in microplastics transport in riverine ecosystems. Microplastic transport is governed by physical characteristics (e.g., plastic particle density, shape, and size) and hydrodynamics (e.g., laminar and turbulent flow conditions). High-density microplastics are likely to prevail near riverbeds, whereas low-density particles float over river surfaces. Microplastic transport occurs either due to gravity-driven (vertical transport) or settling (horizontal transport) in river ecosystems. Microplastics are subjected to various natural phenomena such as suspension, deposition, detachment, resuspension, and translocation during transport processes. Limited information is available on settling and rising velocities for various polymeric plastic particles. Therefore, this paper highlights how appropriately empirical transport models explain vertical and horizontal distribution of microplastic in riverine ecosystems. Microplastics interact, and thus feedback loops within the environment govern their fate, particularly as these ecosystems are under increasing biodiversity loss and climate change threat. This review provides outlines for fate and transport of microplastics in riverine ecosystems, which will help scientists, policymakers, and stakeholders in better monitoring and mitigating microplastics pollution.

The scarcity of water resources and environmental pollution have highlighted the need for sustainable wastewater treatment. Existing conventional treatment systems are energy-intensive and not always able to meet stringent disposal standards. Recently, algal-bacterial systems have emerged as environmentally friendly sustainable processes for wastewater treatment and resource recovery. The algal-bacterial systems work on the principle of the symbiotic relationship between algae and bacteria. This paper comprehensively discusses the most recent studies on algal-bacterial systems for wastewater treatment, factors affecting the treatment, and aspects of resource recovery from the biomass. The algal-bacterial interaction includes cell-to-cell communication, substrate exchange, and horizontal gene transfer. The quorum sensing (QS) molecules and their effects on algal-bacterial interactions are briefly discussed. The effect of the factors such as pH, temperature, C/N/P ratio, light intensity, and external aeration on the algal-bacterial systems have been discussed. An overview of the modeling aspects of algal-bacterial systems has been provided. The algal-bacterial systems have the potential for removing micropollutants because of the diverse possible interactions between algae-bacteria. The removal mechanisms of micropollutants - sorption, biodegradation, and photodegradation, have been reviewed. The harvesting methods and resource recovery aspects have been presented. The major challenges associated with algal-bacterial systems for real scale implementation and future perspectives have been discussed. Integrating wastewater treatment with the algal biorefinery concept reduces the overall waste component in a wastewater treatment system by converting the biomass into a useful product, resulting in a sustainable system that contributes to the circular bioeconomy.

Hardware accelerators implement custom architectures to significantly speed up computations in a wide range of domains. As performance scaling in server-class CPUs slows, we propose the integration of hardware accelerators in the cloud as a way to maintain a positive performance trend. Field programmable gate arrays (FPGAs) represent the ideal way to integrate accelerators in the cloud, since they can be reprogrammed as needs change and allow multiple accelerators to share optimised communication infrastructure. We discuss a framework that integrates reconfigurable accelerators in a standard server with virtualised resource management and communication. We then present a case study that quantifies the efficiency benefits and break-even point for integrating FPGAs in the cloud.

We investigate thin film sensing capabilities of a terahertz (THz) metamaterial, which comprises of an array of single split gap ring resonators (SRRs). The top surface of the proposed metamaterial is covered with a thin layer of analyte in order to examine various sensing parameters. The sensitivity and corresponding figure of merit (FoM) of the odd and even resonant modes are analyzed with respect to different thicknesses of the coated analyte film. The sensing parameters of different resonance modes are elaborated and explained with appropriate physical explanations. We have also employed a semi-analytical transmission line model in order to validate our numerically simulated observations. Such study should be very useful for the development of metamaterials based sensing devices, bio-sensors etc in near future.

More people, more cities; the energy demand increases in consequence and much of that will rely on next-generation smart materials. Zn-ferrites (ZnFe2O4) are nonconventional ceramic materials on account of their unique properties, such as chemical and thermal stability and the reduced toxicity of Zn over other metals. Furthermore, the remarkable cation inversion behavior in nanostructured ZnFe2O4 extensively cast-off in the high-density magnetic data storage, 5G mobile communication, energy storage devices like Li-ion batteries, supercapacitors, and water splitting for hydrogen production, among others. Here, we review how aforesaid properties can be easily tuned in various ZnFe2O4 nanostructures depending on the choice, amount, and oxidation state of metal ions, the specific features of cation arrangement in the crystal lattice and the processing route used for the fabrication.

Abstract Strong interaction between terahertz (THz) and matter is a topic of paramount importance, considering continuously enhanced interest in THz photonics as well as condensed matter physics, which can lead to the observation of many linear and nonlinear phenomena in the THz regime. Here, we demonstrate a unique and novel metamaterial-based technique of strong THz matter interaction towards thin film sensing, where the analyte is sensed in between the stacked metasurfaces forming the Fano cavity. Sub-wavelength structures typically overcome the diffraction limit of any optical system, which also possess very high confinement of electromagnetic energy. Fano resonance possesses a sharp asymmetric line shape, low radiation loss and large tuning capability. In addition to them, the material under test is placed in between the metasurfaces to utilize the substantial energy confinement leading to strong light matter interaction, a scheme never explored before. By intelligently exploiting the above characteristics, we have demonstrated a novel way to detect both the refractive index (dielectric constant), thickness and loss factor of the material under test when placed between the array of the meta-resonators forming the Fano metamaterials. Our study revealed that the sensitivity and figure of merit (FOM) are strikingly different for dipole and Fano modes. A maximum sensitivity of >1 THz RIU −1 (1.76 × 10 5 nm RIU −1 ) and FOM of around 14.05 are achieved at the Fano mode. Additionally, our sensor shows better performance with decreasing spacer thickness (lesser the material, more the sensitivity). Moreover, the proposed device is passive towards typical ambience temperature variation, and is highly compact because of its stacked configuration. The demonstrated device can be extremely beneficial towards realizing ultra-sensitive meta-sensors and other miniaturized THz meta-photonic devices, and bio-chemical sensing where strong light matter interaction is mandatory.

Abstract Machine learning (ML) methods enable computers to address problems by learning from existing data. Such applications are becoming commonplace in molecular sciences. Interest in applying ML techniques across chemical compound space, from predicting properties to designing molecules and materials is in the surge. Especially, ML models have started to accelerate computational chemistry, and are often as accurate as state‐of‐the‐art electronic/atomistic models. Being an integral part of the ML architecture, representation of a molecular entity, uniquely encoded, plays a crucial role to what extent an ML model would be accurately predicting the desired property. This review aims to demonstrate a hierarchy of representations which has been introduced, to capture all degrees of freedom of a molecule or an atom the best, to map the quantum mechanical properties. We discuss their diverse applications how they have been instrumental in harnessing the growing field of ML accelerated computational modeling.

We analyze plasmon induced transparency (PIT) in a planar terahertz metamaterial comprising of two C-shaped resonators and a cut-wire. The two C-shaped resonators are placed alternately on both sides of the cut-wire such that it exhibits a PIT effect when coupled with the cut wire. We have further shown that the PIT window is modulated by displacing the C-shaped resonators w.r.t. the cut-wire. A lumped element equivalent circuit model is reported to explain the numerical observations for different coupling configurations. The PIT effect is further explored in a metamaterial comprising of a cross like structure and four C-shaped resonators. For this configuration, the PIT effect is studied for the incident light polarized in both x and y directions. It is observed that such a structure exhibits equally strong PIT effects for both the incident polarizations, indicating a polarization independent response to the incident terahertz radiation. Our study could be significant in the development of slow light devices and polarization independent sensing applications.

A brain-computer interface (BCI) provides a way to develop interaction between a brain and a computer. The communication is developed as a result of neural responses generated in the brain because of motor movements or cognitive activities. The means of communication here includes muscular and non-muscular actions. These actions generate brain activities or brain waves that are directed to a hardware device to perform a specific task. BCI initially was developed as the communication device for patients suffering from neuromuscular disorders. Owing to recent advancements in BCI devices—such as passive electrodes, wireless headsets, adaptive software, and decreased costs—it is also being used for developing communication between the general public. The BCI device records brain responses using various invasive and non-invasive acquisition techniques such as electrocorticography (ECoG), electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI). In this article, a survey on these techniques has been provided. The brain response needs to be translated using machine learning and pattern recognition methods to control any application. A brief review of various existing feature extraction techniques and classification algorithms applied on data recorded from the brain has been included in this article. A significant comparative analysis of popular existing BCI techniques is presented and possible future directives are provided.

Verwey transition (VT) of Fe 3 O 4 has been extensively investigated as this results in sharp changes in its physical properties. Exploitation of VT for potential applications in spin/charge transport, multiferroicity, exchange bias, and spin Seebeck effect-based devices has attracted researchers recently. Although hundreds of reports have been published, the origin of VT is still debatable. Besides, not only the size effects have a significant impact on VT in Fe 3 O 4 , even the conditions of synthesis of Fe 3 O 4 nanostructures mostly affect the changes in VT. Here, we review not only the effects of scaling but also the growth conditions of the Fe 3 O 4 nanostructures on the VT and their novel applications in spintronics and nanotechnology.

Cu(0) nanoparticles were deposited on a nanoporous polymer to develop a novel nanocatalyst (Cu-B) for carrying out Ullmann coupling of aryl halides with amines in water. Non-aqueous polymerization of a mixture of divinylbenzene and acrylic acid under hydrothermal conditions followed by the deposition of Cu(0) nanoparticles were adopted to afford the Cu-B nanocatalyst. In order to compare the catalytic activity of the Cu-B nanocatalyst in the Ullmann coupling reactions, another nanocatalyst, Cu(0) nanoparticle-loaded porous carbon (Cu-A), was also prepared. All the newly developed Cu(0) nanoparticle-based nanocatalysts were thoroughly characterized using several characterization techniques. The Ullmann coupling reactions were carried out in water only with 1.35 mol% loading of Cu as catalytically active sites in Cu-B. The Cu-B nanocatalyst exhibited higher catalytic activity as compared with Cu-A, and also showed a good catalytic recyclability with a high consistence in the catalytic activity. No Cu leaching from the nanocatalyst surface and the smooth nanocatalyst recovery confirm the true heterogeneity in these catalytic reactions.

Abstract Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.

FPGAs offer high performance coupled with energy efficiency, making them extremely attractive computational resources within a cloud ecosystem. However, to achieve this integration and make them easy to program, we first need to enable users with varying expertise to easily develop cloud applications that leverage FPGAs. With the growing size of FPGAs, allocating them monolithically to users can be wasteful due to potentially low device utilization. Hence, we also need to be able to dynamically share FPGAs among multiple users. To address these concerns, we propose a methodology and a runtime system that together simplify the FPGA application development process by providing: 1) a clean abstraction with high-level APIs for easy application development; 2) a simple execution model that supports both hardware and software execution; and 3) a shared memory-model which is convenient to use for the programmers. Akin to an operating system on a computer, our lightweight runtime system enables the simultaneous execution of multiple applications by virtualizing computational resources, i.e., FPGA resources and on-board memory, and offers protection facilities to isolate applications from each other. In this paper, we illustrate how these features can be developed in a lightweight manner and quantitatively evaluate the performance overhead they introduce on a small set of applications running on our proof of concept prototype. Our results demonstrate that these features only introduce marginal performance overheads. More importantly, by sharing resources for simultaneous execution of multiple user applications, our platform improves FPGA utilization and delivers higher aggregate throughput compared to accessing the device in a time-shared manner.

Distorted octahedral complexes of Sn(<sc>iv</sc>) with thio- and seleno-ether ligands have been used as single source precursors in low pressure CVD experiments under various conditions to deposit tin mono and dichalcogenide thin films.

Abstract In this article, we experimentally and numerically investigated a metamaterial (MM) geometry capable of exhibiting polarization independent double-band electromagnetic induced transparency (EIT) effect. The meta-molecule unit of the proposed MM configuration composed of a strip and two asymmetric split ring resonators (SRRs). For polarization independent double-band EIT effect, the existing meta-molecule unit is converted into a cross-like structure adorned with four SRRs. Terahertz transmission response is analyzed for two orthogonal polarization directions of the incident light to confirm the polarization independent response. In order to understand and explain our numerical findings more elaborately we have employed four-level tripod atomic system based analytical model. The transmission response is also analyzed for different angle of incidence of the two orthogonal polarizations. In order to demonstrate the practical applicability of our study, we have studied the effect of transmission with the change of refractive index of analyte of thickness 10 μ m coated on the top of the MM resonators. The calculated sensitivities for the 1st, 2nd and 3rd dips are 121 GHz/RIU, 138 GHz/RIU and 135 GHz/RIU (RIU, refractive index unit) respectively. Our study can also play an important role in the advancement of slow light devices, modulators and filters.

Abstract The increase in the numbers of electric vehicles (EVs) is seen as an upgrading of the existing vehicles for various reasons. This calls for an in‐depth analysis of the heart of these vehicles—the motor. A motor in an electric vehicle propulsion system is a crucial component that has the ability to affect the efficiency, weight, cost, reliability, power output and performance. Hence a detailed comparative study, that compares the existing types and topologies of various motors, is the need of the hour. The various motors that can be used in electric traction, namely DC, induction, switched reluctance, permanent magnet brushless AC motors and permanent magnet brushless DC motors, are reviewed in view of their capabilities with respect to EV propulsion. A detailed review is presented of existing motors and the application of power electronic techniques to EVs, and recommendations for some new designs of brushless DC motors. These include permanent magnet hybrid motors, permanent magnet spoke motors and permanent magnet inset motors.

Alzheimer's disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly.

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security.