Rajasthani Clay - Solutions to Air and Water Pollution

Rakesh K Sharma

The exhaust generated by the internal combustion is a major cause for the creation of pollutants such as carbon monoxide, nitrogen dioxide, particle pollution, and sulfur dioxide. The Environment Protection Agency, USA (EPA) implemented the Clean Air Act, 1963, to define air quality standards. To maintain these standards and curb air pollution, the EPA in 1975 mandated the use of catalytic converters in vehicles. Catalytic converters are part of automobile exhaust systems and oxidize or reduce toxic gases such as nitrogen oxides, carbon monoxide, and soot into less hazardous gases such as carbon dioxide, water vapor, and nitrogen. Typically, a catalytic converter is a honeycomb-like structure containing gram levels of precious metals such as platinum, rhodium, and palladium and are therefore expensive. Metal leaching and poor performance over time have also been serious issues with conventional catalytic converters. Since conventional catalytic converters normally work best at high temperatures, they also release toxic pollutants until the vehicle warms up. While engines that work at low temperatures have been designed and developed, designing a low-cost, low-temperature catalytic convertor has led to a resurgence of active research in this area.

The Sustainable Materials and Catalysis Group at IIT Jodhpur has conducted research on clay-based heterogeneous catalysts as potential alternate technological solutions for various challenging problems in the energy and environment domain. The group attempted to address the problems associated with the conventional catalytic converter such as decline in function over time, metal leaching, and high-temperature stability. With time, the palladium particles spread over cerium undergo surface oxidation and decompose into small particles partly due to the high-temperature oxidation process. Therefore, reduction of operating temperature, replacement of palladium and cerium with non-noble metals, and re-design of the converter to prevent metal deactivation were necessary interventions. Clay has inherent properties such as large surface area, broken edge bonds, potential for ion exchange (increases the adsorption of gases). The group has engineered clay containing simple non-noble metals like iron, nickel and cobalt as well as hafnia. The Fe-Ni-Co cooperative nano-particles work as isolated nanospheres (single-site catalyst) while the hafnia-rajasthani clay function as an oxygen reservoir with controlled supply. The catalytic performance of the developed clay-based device was found to be better than the conventional catalytic converter even at high temperatures over several cycles. Further testing and prototype development are currently in progress. Devika Laishram, a Ph.D. student in the Department of Chemistry, played a major role in the development of the device.

Another focus area of the sustainable materials and catalysis group is water treatment. Though water scarcity is emerging as a major problem across the globe, water as a resource is usually taken for granted, which leads to water-insecurity. India’s situation is precarious because the country has to deal with water contamination by industrial and geogenic impurities. The underground water in India, therefore, is often polluted with harmful substances such as dyes, fluoride-rich chemicals, and other industrial discharges. The group has developed Rajasthani clay-based photocatalytic water purification technology using sunlight. In this technology, Rajasthani Clay is modified by a simple chemical process and impregnated with metal nanoparticles. The photocatalytic materials show semiconductor characteristics after incorporation of metal nanoparticles. The metal nanoparticles have multiple roles in the treatment of water such as absorbing sunlight, adsorbing pollutants, decomposition of pollutants etc. The process involves exposing a slurry made of the clay catalyst with the contaminated water followed by sunlight exposure for five hours with frequent agitation. Highly ordered long galleries in the nanoparticle impregnated clay help to absorb the sunlight. This process has been successfully used to treat waste water emerging from textile, dairy, pharmaceutical, and poultry industries.

Currently, the Sustainable Materials and Catalysis Group at IIT Jodhpur is working on a prototype to modify this technology to provide clean water to remote areas. The studies leading to the development of this technology were performed by a team including Vineet Soni, Toran Roy, Suman Dhara, Ganpat Choudhary, and Pragati Sharma.

About the author

Rakesh K Sharma

Associate Professor
Department of Chemistry

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Developing an Autopilot System for Autonomous Aerial Vehicle

C. Venkatesan

The development of unmanned aerial vehicles has been a highly active area of research in the recent decade. These aerial vehicles require proper control strategies for a stable flight to make the vehicle perform the required task in the real outdoor environment. All over the world, several organizations, educational institutions, and individuals are working on various aspects of development and application of this technology to real life scenarios. Development of an autopilot system is one of the main challenges in the realization of a reliable vehicle. In India, there are many companies/startups that use the procured foreign technology, particularly with regards to the autopilot system, to make their vehicle fly in autonomous mode. As the design of the autopilot system is critical and challenging (which depends on the dynamic behavior of the vehicle), it is important for us to develop a system indigenously so that we have maximum control and information regarding the reliability and working mechanism of the autopilot system. This will allow us to modify various modules and algorithms of the system to achieve the specified task.

To achieve the goal, the focus of our study is on the design and development of a complete autopilot system including selection of sensors, their integration, data processing, control algorithm for attitude and navigation, and communication to ground station. The developed autopilot system has been tested on-board a quadcopter flying in an outdoor environment. The results of the flight test clearly show that the indigenous autopilot system satisfies the autonomous control and way-point navigation for short range flights due to limitation in the range of wireless communication. The design procedure and the control algorithm developed in this study can now be implemented and tested in various flying vehicles with VTOL capabilities, including mini helicopters. The control architecture, quality of attitude control achieved, and the quadcopter with autopilot are shown in the figures below.

About the author

C. Venkatesan

Professor
Department of Mechanical Engineering

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Solar Thermal Systems

Prodyut Chakraborty, V. Narayanan, Hardikkumar B. Kothodia, and Shobhana Singh

Solar thermal systems have always held a cornerstone position in renewable energy applications and the share in market potential. The photovoltaic (PV) route to tap solar energy is fraught with heavy metal pollution which impacts the solar panel life and efficiency for process heating/cooling applications. Solar thermal technologies, on the contrary, are environment friendly and efficient for a wide range of applications. In order to overcome the barriers in the commercialization of solar energy based technologies, IIT Jodhpur focuses on cutting-edge research on solar thermal systems. The research encompasses not only improving the efficiency of solar thermal systems, both standalone and integrated, but also to train engineers in this field for future advancements.

An interdisciplinary team of faculty members and research scholars are working on variety of solar thermal systems and technologies including Concentrated Solar Power (CSP) systems - the parabolic dish concentrator, solar PV panels, central receiver, thermal energy storage, solar dryer, solar cooker to name a few.

A snap shot of ray traced heliostat field with receiver kept at the center.

Flux contour on the Solar Central Tower Receiver.

Experimental Set-up testing of emissivity of Solar Thermal Paints.

About the authors

Prodyut Chakraborty

Associate Professor

Department of Mechanical Engineering.

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V. Narayanan

Assistant Professor

Department of Physics.

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Hardikkumar B. Kothodia

Assistant Professor

Department of Mechanical Engineering.

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Shobhana Singh

Assistant Professor

Department of Mechanical Engineering.

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Exploring Inflammation, Immunity and Tumour Biology of Deadly Brain Tumours in India

Sushmita Jha

Our brains consist of neurons and glial cells or glia. Glia that were thought of as mere brain glue for almost a century are now appreciated for their myriad roles. They regulate development, maintain homeostasis, repair injuries and fight infections. Gliomas are tumours arising from glial cells. Gliomas are classified based on their pathology and molecular characteristics into four grades. Glioblastomas, the most common and aggressive primary brain tumours in adults, remains one of the least treatable cancers. Current standard of care—combining surgical resection, radiation, and alkylating chemotherapy—results in a median survival of less than 15 months. Despite decades of research, most anti-glioma therapies fail to translate into effective therapies. It is widely appreciated that inter- and intra-tumour heterogeneity in cellular populations leads to poor outcomes and ineffective therapies. Dysregulated inflammation and aberrant blood vessel formation (angiogenesis) are central to glioblastoma pathology.

We sought to understand tumour heterogeneity in cellular and molecular expression of genes involved in inflammation and immunity1. With funding from the young scientist grant from SERB, DST (2014-2017) and using The Cancer Genome Atlas (TCGA) data the importance of innate immune pathways in cell lines2 was established. Next, a tripartite MoU was setup between Indian Institute of Technology Jodhpur, Tata Memorial Centre [consisting of TMH - Tata Memorial Hospital and ACTREC - Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai] & AIIMS Jodhpur to understand the cellular and molecular landscape of gliomas for the identification of novel therapeutic targets especially in context of the Indian subcontinent. With funding from the department of biotechnology (DBT 2017-2020) key prognostic markers for glioblastomas were identified 2. More recently, the contribution of the tumor microenvironment especially the aberrantly low oxygen levels (hypoxia) within the tumour core on the tumour pathology are being examined 3. With funding from the Ministry of electronics and information technology (2020-2023) the team is working to develop an artificial intelligence based model for tumour growth using multimodal data from patient derived tumour organoids (Figure). This multi-pronged approach should help us identify key biomarkers for future therapeutic interventions to delay the progression and/or modify the outcome of this deadly tumour.

About the author

Sushmita Jha

Associate Professor
Department of Bio-Science and Bio-Engineering

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Sensors for Environment, Health and Water monitoring applications

Mahesh Kumar

Room-temperature gas sensors have aroused great attention in current health and environment monitoring technology because of the deemed demand for cheap, low power consuming and portable sensors for rapidly growing Internet of Things (IoT) applications. As an important approach, light illumination has been exploited for room temperature operation with improving gas sensor’s attributes including sensitivity, speed, and selectivity. Our current research is focused on the utilization of photo-activated nanomaterials in gas sensing field. We have fabricated room temperature hydrogen gas sensors using hybrid gold nanoparticles with metal oxide semiconductors under light illumination [1]. Recently, we have demonstrated excellent gas sensing performance of emerging two-dimensional (2D) materials based sensors under light illumination [2]. Originated impressive features from the interaction of photons with sensing materials are elucidated in the context of modulating sensing characteristics. Our research group addressed the key and constructive insights into current and future perspectives in the light-activated nanomaterials for optoelectronic gas sensor applications [3]. Bad odours coming from exhaled breath (halitosis) consists of volatile molecules which are mainly sulfur compounds such as H2S, CH3SH and CH3SCH3. We are fabricating sensors to detect extremely low concentration of the volatile sulfuric compounds using metal oxides for halitosis sensors.

Heavy metals are ubiquitous in water and soil. Among them, trace amounts of Fe, Co, Mn, Zn, etc. are necessary for living organisms while others such as Hg, Pb, Ni, As, Cd, Sn, Cr, etc. are not only toxic but can also cause cancer and neuro-degenerative diseases. The sources of these toxic heavy metals are paints, automobile exhaust, wastes from mining, coal, and other industries. We have demonstrated a novel AlGaN/GaN high electron mobility transistor (HEMT) based low cost heavy metal ion sensors. The sensing selectivity of ions depends on the functionalization materials and Cd and Pb ion sensors have been developed [4-5]. Recently our group has also developed a sensor for highly sensitive, selective, and rapid determination of the trace amount of toxic Hg2+ ions in water using MoS2 functionalized AlGaN/GaN HEMT [6]. Currently, we are working on the fabrication of a single chip for sensing multiple metal ions in water.

About the author

Mahesh Kumar

Associate Professor
Department of Electrical Engineering

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Singularities opening up new possibilities for devices like optical isolators

Somnath Ghosh

Isolators are indispensable in almost all optical systems. For example, protection of a high-power laser from back-reflection, reduction of multipath interference in a communication system, and optical signal processing. Non-reciprocity (NR) is the fundamental requirement to realize an optical isolation process, for which a device blocks light in a particular direction and allows it to pass in the opposite direction. To this end, NR has commonly been achieved via the magneto-optical Faraday rotation effect. However, on chip-scale integrated photonics, it remains elusive due to the unavailability of necessary materials to achieve a sufficient Faraday rotation effect. Thus, there is a usual challenge in the translation/realization of all-optical isolators on a chip-scale device footprint. In this work, the concept of an exceptional point (EP) has been utilized to overcome this challenge. EPs are the branch point singularities, usually appearing in the open quantum systems that once seemed purely mathematical. EPs appear as the topological defects, where two eigenvalues and the corresponding eigenvectors simultaneously coalesce, and accordingly, the Hamiltonian of the underlying system becomes defective. Recent advanced technologies to implement EPs in various optical systems have boosted the device performance in the context of a wide range of technological applications, including asymmetric waveguiding, lasing and anti-lasing, ultra-sensitive sensing, and also an enhancement in nonreciprocal transmission.

Here, a gain-loss assisted dual-mode planar step-index optical waveguide, hosting an EP, has been investigated to implement a chirality driven asymmetric mode conversion scheme, which can enable a mode selective optical isolation process in the presence of saturable nonlinearities. Due to dynamical (length-dependent) closed variation of gain-loss enclosing the EP in the absence of nonlinearity, the proposed waveguide hosts an asymmetric mode conversion scheme, where depending on the direction of light propagation (in terms of device chirality), the light is converted into a particular dominating mode irrespective of the choice of input modes. Here, the waveguide delivers the dominating fundamental mode (ψ_0) in the forward direction and the dominating first-higher order mode (ψ_1) in the backward direction. Such a chirality driven asymmetric mode conversion scheme is reciprocal with a symmetric scattering matrix. Now, this reciprocity has been broken by introducing saturable nonlinearities in the optical medium, and for a certain amount of nonlinearity, exceeding a particular threshold, the waveguide enables a nonreciprocal transmission with an asymmetric scattering matrix. Here, the proposed waveguide is active in the forward direction for 3.75% nonlinearity and allows light to pass in the forward direction, but blocks in the backward direction. In this situation, owing to the EP-aided asymmetric mode conversion scheme, the waveguide delivers only ψ_1 only in the forward direction. Now, while increasing the nonlinearity amount to 8.75%, it has been observed that the waveguide changes its active direction, i.e., allows light to pass in the backward direction, whereas blocks in the forward direction. Here, the EP-aided asymmetric mode conversion scheme allows only ψ_0 to propagate along the backward direction through the waveguide. Thus, based on the amount of nonlinearities, the proposed waveguide can isolate a particular mode in two different directions as per the device requirement. Here, optical nonreciprocity based on the nonlinear dynamics around an EP offers a unique possibility to control light flow in the waveguide structure. Besides a strong impact in fundamental physics, with suitable scalability and using state-of-the-art techniques, our scheme should open up an extensive platform to realize a new class of all-optical isolators for chip-scale/integrated device applications in future communication circuits and computing.

About the author

Somnath Ghosh

Assistant Professor
Department of Physics

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Physical Layer Security in Wireless Communications

Aashish Mathur

The future 5G wireless communication technology focuses on providing ultra-high data rates, ultra-low latency, ultra-wide radio coverage, and massive device connectivity. Nowadays, end users rely heavily on wireless networks for transmission of confidential information for several applications. Thus, security is very critical for future 5G wireless networks. Today, security relies heavily on cryptographic techniques and algorithms that are applied at various levels of data processing stack. However, due to their drawbacks, it is necessary to develop security algorithms at the physical layer itself, known as physical layer security (PLS). PLS is particularly advantageous for 5G scenarios compared to cryptographic techniques as PLS techniques are independent of computational complexity implying that the security will not be compromised even if the eavesdropper in 5G networks has powerful computational capabilities. Further, due to the decentralized nature of the 5G networks, devices will be connected to or disconnected from the network at random time instants. Thus, cryptographic key distribution and management will be a very challenging task. In such a situation, PLS techniques can be used to perform secure data transmission directly. Some key contributions by IIT Jodhpur in this area are mentioned as follows.

On physical layer security of α-η-κ-μ fading channels:

This work provides a detailed analysis of PLS over generalized wireless fading channels that could be used to model 5G communication environments. It is shown through analysis that the secrecy diversity order depends only on the channel non-linearity and the number of multipath clusters. The obtained results are instrumental in studying the secrecy performance over generalized fading scenarios applicable to typical 5G application scenarios.

Secrecy performance of correlated α-μ fading channels

This study analyses the impact of correlation effects between the main channel and eavesdropper channel on the secrecy performance of wireless communication scenarios. It is revealed that while the secrecy diversity order is independent of the correlation coefficient, correlation dependent power penalty is observed.

This paper appears in the list of popular articles of IEEE Communications Letters for August 2019.

Secrecy enhancement of RF backhaul system with parallel FSO communication link

High data rate requirement in 5G and beyond communications requires backhaul links with much higher capacity and reliability relative to previous systems, especially in the context of network densification that makes wired backhaul an expensive solution and of the fact that integrated access backhaul solution is officially adopted in 5G standard of 3GPP recently. In this work, we combine the advantages of RF communication (in terms of its robustness to atmospheric and weather effects) and free space optical (FSO) communication (in terms of secure transmission with high data rate) and propose a parallel setup of FSO and RF communication systems which serves as a more reliable candidate solution for backhaul network for 5G systems.

Currently, we are also working on the development of secure transmission schemes for FSO backhauling for 5G where the transmitter will generate artificial noise and add it to the information bearing signal such that it does not affect the intended receiver channel but degrades the quality of the eavesdropper’s channel, thereby ensuring security. This work is being carried out in collaboration with IIT Delhi in the project titled “Building end to end 5G Test Bed”.

About the author

Aashish Mathur

Assistant Professor
Department of Electrical Engineering

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Dependable Face Recognition

Richa Singh and Mayank Vatsa

Face is one of the most commonly used and widely explored biometric modalities for person authentication. Recent advancements in machine learning, especially deep learning, coupled with the availability of sophisticated hardware and abundant data, have led to the development of several face recognition algorithms achieving superlative performance and the systems are getting used in several real world scenarios ranging from photo tagging in social media, photo organization in mobile devices to critical law enforcement applications of missing persons search, and suspect identification. However, recent events regarding the inclusivity of people of all ethnicity and gender, and sensitivity to adversarial attacks have raised questions about the dependability of existing systems. With the motivation to make the systems more robust, inclusive and explainable, the research group at IIT Jodhpur (http://iab-rubric.org/) is working on multiple facets of dependable face recognition: (i) bias, (ii) robustness to adversarial attacks and fake images, and (iii) privacy preservation.

“Bias” can be defined as the action of supporting or opposing a particular person or thing in an unfair way, because of allowing personal opinions to influence your judgment. In terms of algorithmic bias, personal opinions can be alluded to the knowledge or the experience gained from the training data. Amazon’s face recognition software, Rekognition made an erroneous prediction for 28 members of the US Congress and confused them with images of publicly available mugshots. The inability of the recognition model to perform well for a particular subset of the population has caused significant concern in the community. The team is designing automated methods and metrics to evaluate whether the system is biased or not, followed by algorithms to mitigate the effect of bias on the network without affecting the performance. The team has also received the Ethics in AI award from Facebook for “Mitigating Bias in Face Recognition For Vast Regional Diversity in India”.

Another problem that the group is working on is making the systems robust to adversarial attacks, which can be both digital and physical. With growing technology, the methods of attacking are also becoming more and more sophisticated. For instance, silicone masks and disguise accessories can be used to impersonate someone’s identity while in the digital domain, images can be perturbed by adding a minute noise to fool the machine learning models. Similarly, driven by the innovation in Generative Adversarial Networks (GANs), recent techniques are capable of producing highly photorealistic Deepfakes which can easily fool both humans and automated algorithms. The research team is designing unified algorithms to detect and mitigate different kinds of attacks on face recognition systems. The proposed attack detection and mitigation approaches are utilizing deep learning and handcrafted features and have shown highly encouraging results on benchmark databases. The research is supported by the Ministry of Electronics and Information Technology of the Government of India.

With the help of advanced data analysis approaches, the scope of information profiling from the photo collections available at the social media websites has proliferated. There are approaches which provide not only the health profile of a person by looking at the images but also can infer sexual orientation. Under the dependable face recognition research, the team is also developing privacy preserving techniques that can help the users to avoid being profiled using their images available on the social media sites. The team is developing measures to preserve the privacy of face images while ensuring that the face recognition performance is not affected.

Richa Singh

Professor
Department of Computer Science and Engineering

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Mayank Vatsa

Professor
Department of Computer Science and Engineering

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Neural networks for automated image tagging

Yashaswi Verma

Pictures have become an integral part of our lives - they are everywhere. In today’s digital era, there has been an explosion of photographs on the internet as well as in personal collections. This has made it necessary to develop new computational systems that can help in understanding pictures based on their visual content with minimal human intervention. One way to understand pictures is using text. If we have access to an accompanying text (such as labels/keywords, phrases, captions, or paragraphs) describing what is there in a picture, it becomes quite easy to manage (archive/search/retrieve) them and also simplifies the interaction of common people with this vast ocean of visual data, as is made evident by the online image search engines.

In order to automatically describe images using natural text an automatic visual system becomes necessary. However, there is a fundamental difference between how a computer perceives an image versus how humans do. Therefore, developing accurate computational techniques to automatically predict a piece of text for a given image that would describe its visual semantics has been widely acknowledged as an important area of research. The simplest form of text that we can associate with an image is a set of labels that conveys the content of the image, and is popularly referred to as “image annotation” or “image tagging”. Since image tagging deals with multiple labels, it becomes crucial to model relationships among labels. For example, if we consider a picture of a “beach”, it is quite likely that it would also contain “sea”, “sky”, and “people”.

With the advent of deep learning (deep neural networks), there has been a significant advancement in the development of practically useful visual recognition systems. Two such techniques that are widely used are convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs). While CNNs have shown a compelling capacity to learn powerful features for regular grid-based data such as images, RNNs are found to be effective in learning combinatorial inter-label relationships in textual data while keeping the computational complexity tractable. Thus, the combination of CNN and RNN provides a promising avenue to learn an advanced computational model for image annotation.

Recent research, led by a multi-institute team including members from IIT Jodhpur, Target Corporation (India) and IIIT Hyderabad, has led to the development of a novel algorithm toward using the CNN+RNN framework for image annotation. While RNNs have been seen as possible solutions for many applications, this is the first report of an algorithm that enables the RNN to learn multiple inter-label relationships, and achieves state-of-the-art accuracies on large-scale image annotation data. One fundamental challenge in using RNN for label-set prediction is that at the time of training, it requires the labels to be fed in a sequence. To address this constraint, researchers have explored frequency based orderings (e.g., low-to-high frequency) in the past. However, imposing such an ordering does not naturally align with the image annotation task, since the original data contains label-sets and not label-sequences. The unique approach developed by the multi-institute team eliminates this constraint and also enables the RNN to learn multiple inter-label dependencies on its own. To harness the inherent sequence-learning and sequence-prediction capabilities of RNN, this work proposes a two-step process, in which the first step allows the RNN to predict a sequence of confidence scores corresponding to all the labels given an image representation computed using a CNN, and the second step makes use of these confidence scores for each label across the sequence and assigns the maximum score.

The outcome of this research, titled “Recurrent Image Annotation with Explicit Inter-label Dependencies”, will be published in the proceedings of the European Conference on Computer Vision (ECCV) 2020. Developing a practical image annotation system is a complex task, requiring capabilities from multiple disciplines including image processing, computer vision, machine learning, natural language understanding, information retrieval and knowledge-based reasoning. The team would like to acknowledge the financial and computational support provided by Target Corporation (India), Department of Computer Science and Engineering at IIT Jodhpur, Kohli Center on Intelligent Systems at IIIT Hyderabad, and the Department of Science and Technology (India).

About the author

Yashaswi Verma

Assistant Professor
Department of Computer Science and Engineering

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Sketching to find objects in images

Anand Mishra

A sketch-guided object localization framework has been developed by Anand Mishra, a faculty member at the computer science and engineering department at IIT Jodhpur, in collaboration with researchers from the Indian Institute of Science Bangalore. This work will appear at the European conference on computer vision (ECCV 2020), one of the prestigious conferences in the field of computer vision as a spotlight presentation (top-5% paper). This work is considered to be a significant step towards open-world object localization.

Object localization is one of the fundamental problems in computer vision. Most successful localization methods rely on large-scale annotated training data, and are often limited to a finite number of closed-world object categories. Other lines of work either use image or class labels as a query to localize objects in the target image. However, in many cases, images or class labels are not available due to copyright or privacy issues or user convenience. For example, users may prefer to draw sketches of an object. In such cases, sketch-guided object localization illustrated in the above figure, becomes an important task. This problem, despite being practically useful, has not been explored. Mishra et al., were the first to introduce this problem and have proposed a novel cross-modal attention scheme to address it. They have achieved encouraging results (both for seen and unseen categories) on public benchmarks.

About the author

Anand Mishra

Assistant Professor
Department of Computer Science and Engineering

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Vehicular Ad-hoc Networks

Debasis Das and Arun K. Singh

Advancement in modern transportation technologies led to a connected and autonomous vehicles world. With the advancement in technology, there is a rapid increase in auxiliary factors such as limited road space, infrastructure, high accidental rate, and bad driving habits. In a report, the world health organization (WHO) affirms that road traffic accidents touched 1.35 million deaths per year. Increased road accidents, traffic congestion, long delay, inability to find parking spots need a more efficient and intelligent transportation system which is named Vehicular Ad-hoc Network (VANET). Vehicular Ad-hoc Network (VANET) has been attracting a lot of attention in wireless communication and automobile industries in the recent years. VANET as a new wireless technology use wireless access devices such as IEEE 802.11p to establish connectivity between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure(V2I) (Shown in Figure 1). This novelty in wireless communication assures hugely improved road safety, increase efficiency and prove sustainable transport system in the nearest future via Intelligent Transportation system (ITS) development. Hence, there are many ongoing research projects on several areas of VANET among government organizations, automobile industries and academia on establishment of standards for VANETs. The deployment of VANET applications such as collision alerting system, routing, security and traffic awareness dissemination system have made VANET an area of great research interest when it comes to wireless communication. Due to their unique characteristics such as high dynamic topology and predictable mobility, VANETs attract so much attention of both academia and industry. Vehicular ad hoc network (VANET) applications have emerged as new opportunities for the automobile industry to provide advanced services for connecting vehicles and their users. Most of these services, such as safety, information, and entertainment systems, require a variety of digital content to be delivered to and from vehicles.

The primary objective of VANET was to reduce potential accidents and reduce congestion and delay due to road traffic. Still, with the improvement in the technology, VANET provides a range of services like safety message dissemination, traffic management, infotainment services, entertainment services, parking assistance, traffic flow regulation, multipath mitigation technique, and collision avoidance. The vehicular network consists of entities like Road Side Units (RSUs), On-Board Units (OBUs), Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Vehicle (I2V), and Infrastructure-to-Infrastructure (I2I) communications (Shown in Figure 1). Various wireless technologies such as 5th Generation networks (5G), Long-Term Evolution (LTE), Wi-Fi, and Dedicated Short Range Communication (DSRC) radios are required in VANET.

Vehicular Ad Hoc Networks (VANET) have been envisioned to play an important role in the future wireless communication service market for safety communications as well as for information and entertainment applications. The ongoing research mainly focuses on using underlying structure and techniques to provide accidental aware vehicular network and intelligent transportation services which can benefit in real-world scenarios.

Our research focuses on four main areas of VANETs. In the first work, we are working towards safety applications for collision avoidance, fast warning message dissemination, lane changing assistance, and lower latency networking solutions. These applications are generally supported by optimized routing techniques and efficient traffic modeling by utilizing traffic dynamics. For providing optimized routing, we are exploring the domain of vehicle clustering (shown in Figure 2) with orientational information of a vehicle. The traffic modeling is done through Spatio-Temporal propagation of the vehicle and treating traffic flow as the flow of continuous fluid. A recent research, published in 19th IEEE International Federation for Information Processing (IFIP) Networking 2020 Conference (NETWORKING 2020), presents a cosine similarity based selective broadcast routing protocol, also known as CSBR, which leverages non-linear cluster formation ability using cosine similarity index.

The second work towards cooperative traffic solutions using machine learning methods which improve route discovery and provides Quality of Service (QoS) is shown in Figure 3. In this phase, reinforcement learning techniques and deep learning networks are exploited to enhance routing solutions and autonomy of the vehicular network. The vehicular network does not have a specific model due to the dynamicity of the vehicles. Through machine learning techniques vehicles can learn from the environment by interaction with the network nodes such as other vehicles and RSUs and does not require correct knowledge of the input and output parameters. This helps to achieve satisfactory routing standards and provide an efficient approach for optimal path discovery and rapid message dissemination process.

The third work pertains to Reliable Comprehensive Communication System for the Internet of Vehicles using lightweight cryptography. By 2025, around 25 billion “things” will be connected to the Internet for a better society using different technological systems. Vehicle users have a better experience by collaborating with the Internet of Things (IoT) and vehicular ad-hoc network (VANET) architectures, and this emerging field is called the Internet of vehicles (IoV) (shown in Figure 4). Therefore, IoV architecture will play an important role in the industry, research organization, and academics for various public and commercial applications. However, the IoV structure should ensure secure and efficient performance for vehicular communications; otherwise, an attacker may interfere. In this work, we proposed protected comprehensive data dissemination protocols (say IoVCom) based on the one-way hash function and elliptic curve cryptography (ECC) for the IoV structure. Next, we analyze the security strengths of the IoVCom against various security attacks and discuss performance results in terms of communication overhead, computation time, storage cost, and energy consumption. The recent research is published in IEEE Transactions on Dependable and Secure Computing (TDSC) and IEEE Transactions on Vehicular Technology(TVT).

In the last work, we are working towards a Multipath Mitigation Technique for GNSS Signals in Urban Scenarios for Automated Vehicular Systems. In Automated Vehicular Systems, enabling high accuracy ubiquitous positioning is essential for implementing more efficient and safer automated train and road transport systems. However, positioning solutions are particularly vulnerable in the urban environment because of the presence of multipaths in the urban canyons. There are several approaches to multipath estimation and mitigation, all such previously known techniques fall short of the requirements of an automated transport management system. One of the theme of vehicular network research at IIT Jodhpur is to provide solutions to the aforementioned positioning issues. A recent research, published in IEEE Transactions on Vehicular Technology, presents a novel multipath mitigation technique that is able to accurately estimate Global Navigation Satellite System (GNSS) signal ranges with a very high probability (>95%) even in the presence of multipaths caused by urban canyons. The presented technique provides an improved accuracy as compared to the previously best known multipath mitigation techniques like Narrow Correlator (NC) and High Resolution Correlator (HRC) or double delta correlator. The performance guarantees for the presented technique hold for the carrier to noise ratio (CNR) of as low as 20 dB, for the typical land vehicle speeds up to 360 Kmph, and for different multipath scenarios including urban, sub-urban, metropolitan environments.

Debasis Das

Assistant Professor
Department of Computer Science and Engineering

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Arun K. Singh

Associate Professor
Department of Electrical Engineering

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DC Microgrid and Applications

Deepak Fulwani

A typical DC microgrid consists of an integrated network of renewable sources, energy storage units and loads. It has parallelly connected interfacing dc-dc converters connected to a common dc bus. The microgrid further consists of the hierarchical primary, secondary and tertiary controls which are collectively used to achieve the objective of the dc bus voltage regulation irrespective of load variations, proportional load sharing among the sources and optimal power flow among the cluster of nodes. To achieve these objectives, the controller must be designed to be robust against the modelling uncertainties and parametric variations. Hence, a sliding mode control (SMC) based primary control and integral sliding mode control (ISMC) based secondary control has been used so as to facilitate the proportional load sharing during the plug-and-play operations in presence of communication uncertainties. The ISMC is incorporated to mitigate the matched uncertainties in the microgrid, without amplifying the unmatched uncertainties. The ISMC generates the voltage reference for the primary SMC. The proposed control combinations are incorporated to maintain desired voltage level, and load sharing in presence of communication and operation uncertainties during plug-in and out of nodes from the microgrid network.

Further, the second order ripples are reflected at the dc side of the dc-ac inverter when connected to the dc bus. Due to this, the low frequency second order ripple currents (SRCs) propagate to the sources and lead to a significant impact on the life span of the components. To reduce the second order ripples, the bulky electrolytic capacitors can be used but it leads to an increase in size and cost of the system. The electrolytic capacitors have lower lifespan compared to other components; this makes the system less reliable and prone to failures. To address this, a virtual impedance based Adaptive Sliding Mode Control (ASMC) methodology has been proposed, so as to increase the output impedance of the interfacing converter.

The adaptive surface consists of the dc bus voltage and current error terms such that, when the bus voltage is within the desired range, the current error is dominant and when it is out of the desired range, the current error is negligible. As a result, by regulating the proportions of error constants, the output impedance can be regulated. The impedance can be regulated at different nodes so as to achieve the second order ripple sharing. An instance of output impedance is shown in Fig.1. The Xc and XL are the impedance of the dc bus capacitor and interfacing converter’s inductance respectively. The output impedance Zo3>Zo2>Zo1 and the SRCs are distributed as SRC1>SRC2>SRC3. Hence, proposed control is used to propagate the ripple currents at some desired node which consists of the ripple filter. It helps in improving the energy density of the ripple filters. The primary ASMC control is integrated with the dynamic consensus secondary control (DCSC) as shown in Fig.2. The DCSC estimates the average loading of the microgrid, based on the communicated data from each node, and regulates the droop constant value to generate adequate voltage reference for the proportional load sharing. The objective of SRC control and dc component sharing is achieved simultaneously. A graph theoretical analysis is used to analyze the per unit load sharing among all the interfaced nodes. The stability of the proposed controller is analyzed considering multiple source nodes using Lyapunov's approach.

About the author

Deepak Fulwani

Associate Professor
Department of Electrical Engineering

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Smart Manufacturing initiatives at IIT Jodhpur

Kaushal Desai

Manufacturing industries are experiencing significant transformations in recent times due to the onset of Industry 4.0 concepts. The newer set of technological solutions necessitate real-time monitoring of manufacturing processes using sensors followed by data analytics to evaluate the status and adjustment of parameters. It will be necessary to have appropriate process knowledge embedded into the decision-making system for the adjustment of settings. The primary objective of Smart Manufacturing activities at IIT Jodhpur is to conduct interdisciplinary and translational research relevant to the next generation shop floors. The overall focus is on improving the practicing engineer’s ability to produce highly accurate and precise components on time. The focus is on strong analytical/numerical modelling capabilities coupled with fundamentals accompanied by experimental techniques and data analysis skills, including physics-based machine learning. Some of the ongoing research efforts include development of physics-guided data-driven models to predict the performance of metal cutting operations, vision-based smart machining platform for in-situ inspection and monitoring and exploring solutions for leapfrogging from Industry 2.0 to Industry 4.0.

Cutting force is the primary source of multiple disturbances contributing to the deterioration of component accuracy significantly during metal removal operations. The prediction, monitoring, and control of cutting force are imperative to avoid or minimize faults such as tool breakage, tool wear, selection of cutting parameters, fixture errors, etc. As cutting force is linked with multiple process faults during metal removal operations, it is essential to have a reliable predictive model to assist in the decision making related to process faults. The group is attempting to develop reliable process models for end milling operation, which is commonly employed in most of the manufacturing industries to fabricate complex shapes in a variety of materials at higher accuracy and productivity. One of the recent works attempted to combine physics-based approaches with machine learning to enhance the prediction accuracy of computational models. It has been observed that the developed hybrid model can predict cutting forces accurately over a wide range of cutting conditions. It is planned to strengthen the present models subsequently by using new generation networks for better realization of relationships.

About the author

Kaushal Desai

Associate Professor
Department of Mechanical Engineering

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Space Robotics

Suril V. Shah

One of the major areas in space science that demands for immediate attention and commercial drive is the On-Orbit Services (OSS), e.g., orbital detritus management, refurbishment and refueling of orbiting satellites, construction in space, etc. The increasing number of satellites as well as growing interest in ‘on-orbit services’ makes it necessary to have robots that can perform these operations autonomously. Hence, autonomous on-orbit servicing operations using a robotic arm mounted on micro servicing satellites will be one of the important components of future space missions. Space robots increase reliability, safety and ease of execution of space operations, but pose a novel challenge to the design and modelling of space robots due to microgravity space environments. At IIT Jodhpur research activities have been undertaken in reactionless manipulation, capture and post-capture control, vision-based control, dynamic identification and earth-based prototype development as shown in Fig. 1. A brief overview of key contributions in these areas is highlighted next.

Capture of Debris

The rapid advancement in space exploration missions caused a growing number of disabled or broken satellites that can damage operational spacecraft and active satellites in case of a collision. Most of these satellites are distributed in Low Earth Orbits (LEOs) and travel at over 7 km/s. At these hyper-velocities, collisions may fragment large objects, thus further aggravating the space debris problem. This necessitates proper strategies for space debris removal and disposal. Efforts are laid down in developing a comprehensive framework for modelling impact dynamics, post-capture stabilization and target manoeuvring of a multi-arm robotic system mounted on a servicing satellite while capturing orbiting objects. An adaptive reactionless control strategy has also been devised for capturing objects with unknown parameters.

Reactionless Manipulation

The dynamics of robots in space differ from that of an earth-based robot. The coupling of the arms and the base of a space robot creates reaction forces and moments on the base whenever the arms execute a manoeuvre, causing the base to rotate and translate in accordance with the laws of conservation of linear and angular momenta. However, it is generally desirable to keep the attitude of the base fixed relative to the sun and the earth (or other bodies) for navigation and communication purposes or to maintain the target in the field of view of the sensors. In this work, a motion planning framework that can be used to capture the target at the specified time while avoiding algorithmic as well as Jacobian singularities with minimum attitude disturbances has been proposed. The framework also allows additional constraints such as limits on angles, acceleration and jerk to be satisfied while avoiding collisions. This method could alternatively be used to find a suitable initial configuration for a given desired motion state.

Vision-Based Control

It is highly desired that the closing over manoeuvres of the robot is carried out autonomously due to communication time delay between the service satellite and ground station. This calls for a control technique which makes use of the on-board machine vision facility for successfully and autonomously completing the OOS. In this work, a generic reactionless visual servo controller for a satellite-based multi-arm space robot was proposed. The controller was designed to complete the task of visually servoing the robot's end-effectors to the desired pose while maintaining minimum attitude disturbance on the base-satellite. Task function approach was utilized to coordinate the visual servoing and attitude of the base satellite. Subsequently, a framework for visual servoing of a space robot towards an uncooperative tumbling object was developed. The framework minimizes the feature error directly in image space by observing that the feature points on the tumbling object follow a circular path around the axis of rotation and their projection creates an elliptical track in the image plane. A novel controller was proposed that minimizes the error between elliptical track and the desired features, such that at the desired pose the features lie on the circumference of the ellipse.

Dynamic Identification

Accurate information of inertial parameters is critical to motion planning and control of space robots. After the launch, on-orbit operations substantially alter the value of inertial parameters. In this work, a new momentum model-based method was proposed for identifying the minimal parameters of a space robot while on orbit. Minimal parameters are combinations of the inertial parameters of the links and uniquely define the momentum and dynamic models. The key to the proposed framework is the unique formulation of the momentum model in the linear form of minimal parameters. Further, to estimate the minimal parameters, a novel joint trajectory planning and optimization technique based on direction combinations of joints’ velocity have been proposed.

These activities have also led to active ongoing collaboration with ISRO on motion planning and control of space robots.

About the author

Suril V. Shah

Associate Professor
Department of Mechanical Engineering

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Superhydrophobic Self-Cleaning Coating

Ravi, K. R.

Self-cleaning based on superhydrophobic coatings is gaining interest because of its potential applications, such as in self-cleaning windows, smart microfluidic devices, anti-bioadhesion, anti-freezing and oil–water separation. These studies are inspired by many natural organisms, including lotus leaves, rose petals and water striders’ legs. Traditionally, superhydrophobic surfaces are fabricated by two main strategies that involve modifying a rough surface with low surface energy compounds and roughening the surface of hydrophobic materials.

Our research team has already developed water-repellent and blood-repellent coating on glass, solar panels, fabrics, and metal surfaces at the laboratory level (Figure 1). The group coated this self-cleaning coating technology on tempered cover glass (34.5 cm x 28.5 cm) and made solar panels with the help of the local industry. Coated solar panels exhibited excellent self- cleaning efficiency (Figure 2). Currently this approach has been extended to N95 face masks and other personal protective equipment (PPEs) to improve protection against respiratory droplets containing the COVID 19 virus.

About the author

Ravi, K. R.

Associate Professor
Department of Metallurgical and Material Engineering

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Bioremediation & Bioenergy generation using Microbial Fuel Cells

Meenu Chhabra

Nuclear wastes contain high nitrate concentration along with radionuclides like U (VI). Also, several reports suggest the presence of U (VI) and nitrate as co-contaminants in groundwater. A microbial fuel cell (MFC) based process was developed for the treatment of these wastes. The denitrifying bacterial consortia at MFC cathode produced phosphatase enzyme, which catalyzed the controlled release of phosphate from glycerol-3-phosphate. The inorganic phosphate combined with U (VI) resulting in insoluble uranyl phosphate. Ninety percent of initial U (VI) added in the biocathode could be recovered as Uranyl phosphate. Nitrate acted as an electron acceptor at the cathode, enabling completion of the MFC circuit and simultaneous nitrate and U (VI) removal. The remediation was accompanied by power output at 2.91 Wm−3 and nitrate removal rate of 0.130 kg NO3−-N m−3 d−1 was achieved. The 16S rDNA based microbial community analysis revealed a high abundance of Pseudomonas species in the biocathode. The process was successfully tested at the Process Development Division, Nuclear recycle group, BARC using nuclear fuel reprocessing wastewater.

Besides, low-cost microbial carbon capture (MCC) cells for power generation and algae cultivation have been developed. The system generates algal biomass and electrical energy with zero input energy. The MFC (10 L) consisted of low-cost materials like rock phosphate blended clayware & low-density polyethylene bags as anodic & cathodic chambers respectively. Algal biomass after lipid extraction at 2 g/l served as electron donor at the anode. Chlorella vulgaris at cathode provided oxygen as electron acceptor and served as lipid source.

The MFCs performed well in all aspects namely energy recovery, algae productivity, and cost of operation. The 5% RP-MFCs gave 0.307 kg/m3d algal productivity, 0.09 kg/m3d lipid productivity, and 11.5318 kWh/m3 of net energy recovery (NER). Rock phosphate served as a slow and constant source of phosphorus supporting algae growth. Proteobacteria (45.14%) were the dominant phyla while Alicyliphilus (5.46%) and Dechloromonas (4.74%) were the dominant genera at the anode. The estimated cost of the system was only $11.22.

About the author

Meenu Chhabra

Associate Professor
Department of Bio-Science and Bio-Engineering

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How neurons can survive during imperfect ageing and neurodegeneration?

Amit Mishra

Presence of abnormal protein aggregates within and outside of neuronal cells is the clinical hallmark of many neurodegenerative diseases. Formation of aberrant aggregates implies various imperfect molecular neurobiological mechanisms such as depleted chaperone capacity and insufficient clearance of old or defective neuronal proteins. Till date, it is unknown how these accumulated misfolded proteins can be removed to achieve normal homeostasis. The distribution, localization, and size of aggregates in neurons may be linked with the neuronal-toxicity, which may lead to neuronal dysfunction and neurodegeneration. The cellular and molecular neurobiology unit aims to understand the molecular neurobiological mechanisms of neurodegeneration and the development of preventative therapeutic strategies. Identification and characterization of molecular factors that can actively recognize and clear the accumulated erroneous proteins is still a challenge.

A substantial proportion of early-onset cases of neurodegenerative diseases are associated with mutations that result in the loss of cellular quality control mechanisms. E3 ubiquitin ligases play major roles in several neuronal pathways and rejuvenate their normal functions by removing pathological proteins leading to their designation as quality control E3 ubiquitin ligases. The presence of these neuronal guardians acts as a first or early line of defense against altered proteostasis. It is an established fact that family history of imperfect aging is a critical risk factor for neurodegenerative disorders. However, detailed insights into the defective mechanisms of several age-associated neurodegeneration remains to be fully understood. A better understanding of molecular neurobiological mechanisms that can specifically target and degrade aberrant proteins will be helpful to design new promising possible therapeutic strategies against neuronal functional loss and impaired-proteostasis disorders. The cellular and molecular neurobiology unit has established a new significant concept on selective E3 ubiquitin ligases. Since they perform key quality control functions E3 ubiquitin ligases are important in the context of neurodegenerative diseases and imperfect aging. The findings of the cellular and molecular neurobiology unit have led to the development of an innovative concept to modulate proteasomal functions, which in turn can induce autophagy pathways and serve to restore affected cellular proteostasis. Such an approach has the potential to develop new lines of therapeutic targets for neurodegeneration and defective ageing.

About the author

Amit Mishra

Associate Professor
Department of Bio-Science and Bio-Engineering

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Decoding Recent Anomalies in Particle Physics

Ashutosh K. Alok

Particle physics aims to understand the universe at the fundamental level by unraveling the most profound mysteries in nature, such as how did the universe begin? What is the origin of space and time? What will the ultimate fate of the universe be? Do we live in a multiverse? The current understanding of our universe is embedded in a theory known as the Standard Model (SM) of particle physics. More than a quarter of the Nobel Prizes in physics are either related to the inputs or to the results of this amazing theory. The last ingredient of SM, the Higgs Boson (popularly known as the God particle) which resolved the mystery of the origin of mass, was discovered in 2012 by the ATLAS and CMS collaboration at CERN, Geneva.

Although SM successfully survived stringent tests of their efficacy in several high precision experiments, it cannot be considered the quintessential theory of nature. This is mainly because SM fails to account for the origin and nature of dark matter and dark energy, which constitute more than 95% of our Universe. Therefore the quest has now shifted towards probing physics beyond SM. The experiments at Belle-Japan, BaBar-Stanford and CERN have already provided several tantalizing hints of new physics. Most of these signatures are related to the charged and neutral semi-leptonic decays of B (beauty) mesons.

In particular, unexpected deviations from the lepton-flavour universality, which is deeply embedded in the symmetry structure of the SM, have been found in the decays of B mesons either to strange or to charm mesons along with an electron or a muon pair. In a series of collaborative works with IIT Bombay, TIFR Mumbai, Montreal U., NCNR Poland, Mississippi U. and Wayne State U., we were able to decode the Lorentz structure of new physics responsible for these anomalous measurements. These works have received more than 400 citations and have received global recognitions including LHCb & Belle collaborations.

It was shown that the measurement of polarization of charmed meson in the charged current B decays could rule out or confirm new physics in the form of tensor interactions. On 19th September 2018, the Belle collaboration announced this measurement in the “10th International Workshop on the CKM Unitarity Triangle” in the University of Heidelberg, Germany. This measurement literally ruled out tensor as a possible new physics interaction, confirming our results!

Our collaboration was the first to identify the Lorentz structures of new physics in the neutral current B decays after the updated measurements announced by the LHCb collaboration in March 2019. The nature of this new physics was probed in a model-independent way using the language of effective field theory. By including all related global data, we identified parsimonious scenarios that would explain these measurements. The LHCb Collaboration acknowledged this work while presenting their results at CERN. We also presented this work at the CERN Theory Seminar on 5th July 2019.

In the coming years, the Belle-II along with the high-luminosity LHC experiments are expected to provide a wealth of data that can lead to discoveries of physics beyond the current paradigm and hence reveal the ultimate secrets of the Universe.In the coming years, the Belle-II along with the high-luminosity LHC experiments are expected to provide a wealth of data that can lead to discoveries of physics beyond the current paradigm and hence reveal the ultimate secrets of the Universe.

About the author

Ashutosh K. Alok

Associate Professor
Department of Physics

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New Generation Energy Storage

Shahab Ahmad

While energy demands are increasing worldwide, fossil fuels fulfil more than 80% of global energy consumption. Alternatives such as wind, hydro and solar power are rapidly growing sources of sustainable electricity to meet the ever growing energy demands, but their intermittency makes energy storage a contemporary challenge. Most of these renewable energy systems need to be synchronized with the energy storage systems, such as batteries, to store the produced electrical energy in the form of chemical energy, so that the electrical energy can be supplied for the desired application at a later stage. The extraordinary advances in battery technology over the past 20 years have opened up vast opportunities for market growth. With limited and expensive oil resources, and the need to reduce CO2 emissions and other greenhouse gases, the requirement for increased energy efficiency afforded by lithium batteries, particularly in the transportation and stationary energy storage sectors, is expanding. The necessity for improved electrochemical energy storage is now clearly receiving the urgent attention it failed to get earlier.[1]

With several recent advancements in rechargeable battery technology, the new generation energy storage systems comprised electrodes with multi-functionalities for unconventional device applications. For instance the electrodes of lithium ion batteries have been made flexible and shape conformal to power fully flexible consumer electronic devices such as wearable electronics, flexible displays etc. The design of highly flexible batteries requires judicious engineering of the electrodes to mitigate stress concentration and crack formation. We demonstrated flexible batteries by designing carbon nanotube (CNT) based cone-shaped microstructures, loaded with nanocrystals for energy storage, which decouple the stress induced during bending in the collector electrode from stress in the energy-storage material (Fe2O3 anodes and LNCO cathodes).[2] These unique lightweight electrodes not only alleviate stress, but also bring the active particles outside of the binder, which dramatically enhances the performance of the conversion reactions. We found that the battery architecture imparts excellent flexibility (bending radius ≈ 300 μm), rate (20 A/g) and cycling stability (over 500 cycles at 1 C with capacity retention over 70%). (Figure 1a)

Emerging autonomous electronic devices require increasingly compact energy generation and storage solutions. Merging these two functionalities in a single device would significantly increase their volumetric performance. However, this is challenging due to material and manufacturing incompatibilities between energy harvesting and storage materials. We recently reported highly versatile and hybrid photo-rechargeable energy storage devices where the functionality of the rechargeable battery and the solar cell are merged together in a single device termed photo-battery to avail the advantages of both technologies simultaneously. [3] Photo-battery is a device that harvests solar energy and stores it in the form of chemical energy. This photobattery relies on highly photoactive lead halide perovskites to simultaneously achieve photocharging and Li-ion storage in a single material. Integrating these functionalities provides simple autonomous power solutions while retaining capacities of up to 100 mAh/g (Figure 1b).

About the author

Shahab Ahmad

Assistant Professor
Department of Physics

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Multi-messenger study of TeV-PeV IceCube neutrino events

Reetanjali Moharana

High Energy Astrophysics (HEA) has been one of the integral parts of particle physics and astrophysics since the discovery of cosmic rays (CRs, the highest energy CRs observed are ultra-high energy CRs, UHECRs >1018 eV) from space and X-rays from astrophysical objects. The origin and propagation of such high energy particles are still in debate. With more significant discoveries it has been confirmed that HEA has imprints even in other wavelengths, radio (1.4 GHz), optical and gamma-rays (> keV). The first discovery of Gravitational Waves (GWs) from neutron star- neutron star (NS-NS) mergers as a transient in other electromagnetic (EM) wavelengths, as well as the recent multi-messenger observation of ~ 290 TeV neutrino event from one of the highest energy astrophysical environment, blazar, could only become possible, because of the combined observations at different wavelengths with correlation in time and space. Our research group at IITJ is dedicated to understanding the various aspects of HEA from a phenomenological and theoretical perspective.

Accelerated high energy cosmic rays from blazars, a class of AGN with their relativistic jets pointed towards us, are likely candidates for the TeV-PeV neutrino events detected by IceCube neutrino telescope at the South Pole. High energy neutrinos being weakly interacting and pointing directly to the direction of their sources, considered as smoking-gun evidence of hadronic accelerators. On the 22 September 2017 IceCube detected neutrino track event (IceCube-170922A) with energy > 290 TeV, which was coincident in direction and time with a high gamma-ray state from a blazar TXS 0506+056. Subsequently, a multi-wavelength campaign was followed involving telescopes across the globe.

Motivated by this, we look for further variable blazar sources as possible candidates of muon track events from the northern hemisphere. The result of our study suggests a spatial correlation of two neutrino events IceCube-100608A and IceCube-111216 correlated with variable blazars, 4FGL J2255.2+2411, and 4FGL J0224.9+1843 respectively. Importantly blazar 4FGL J2255.2+2411 has an additional association of flaring state in gamma-rays observed by Fermi-LAT, with the neutrino event. This study strengthens the possibility of variable blazars may be producing TeV-PeV neutrino events.

Also, the HEA group at IITJ is a member of the Indian cosmic ray detector Gamma-Ray Astronomy PeV EnergieS phase-3 (GRAPES-3), and Ph.D. student Bhanu Pant is now actively involved in the data analysis of the observations.

About the author

Ritanjali Maharana

Assistant Professor
Department of Physics

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