Research News

Vishnu Group of Institutions, a prominent name in the educational society in the West Godavari District of Andhra Pradesh visited SRM University-AP, exploring for a collaboration across multiple domains.

The Vishnu Group of Institutions presently comprises nine constituent colleges, serving over 20,000 students across a diverse range of programs, including Engineering, Dental Studies, Pharmacy, BSc, MCA, Polytechnic, and K-12 education. The Group operates with a profound sense of purpose and unwavering commitment, dedicated to fostering engaging learning experiences in the rural regions of Andhra Pradesh and Telangana.

The event saw the presence of SRM University-AP‘s Vice Chancellor, Prof. Manoj K Arora; Pro-Vice Chancellor, Prof. Ch Satish Kumar; Dean-SEAS, Prof. C V Tomy; Dean -Research, Prof. Ranjit Thapa, among other members of the faculty and Staff, alongside the staff and faculty from Vishnu Group of Institutions.

On the occasion, Vice Chancellor Prof. Manoj K Arora outlined the university’s growth trajectory, stating that the faculty has been a key factor contributing to the varsity’s success. Our faculty has been hired from some of the most-premier institutions of the country and world, fostering a multi-cultural nucleus of learning. “We, as a university, place a lot of emphasis on faculty and their training.” He also stressed the need to advocate for knowledge dissemination.

Prof. Arora highlighted the incredible support that the SRM University Management exerts towards fortifying the research acumen, and the SEED Grant is one such instance. Speaking of the Research prowess of the varsity, Prof. Arora also stated that the research at the varsity is predominantly interdisciplinary in nature. He echoed the words of the Pro-Chancellor, who believes – “Think Big, Do Big” thereby emphasising the importance of learning over teaching.

Prof. Arora also mentioned the landmark collaboration with Carnegie Mellon University. “Our ambitions are high; we will continue to grow,” stated the Vice Chancellor. Speaking on the occasion, the Pro-Vice-Chancellor, Prof. Ch Satish Kumar, added, “At SRM University-AP, we don’t just encourage the faculty but also the students to focus on Research.” He also quoted the NEP, stating the emphasis the policy places on student-based research at all levels of learning.

Prof. Satish cited the old education system, which was based on the concepts of ‘Bhay’ and ‘Bhakti’ (fear and devotion) towards teachers. He stated, “today, the education system cannot run on this ideology. The present system requires us to mentor the students, eliminating the concepts of ‘Bhay’ (fear towards teachers) Mentoring is the key to our system of teaching and learning here at SRM University-AP.”

The event also witnessed Deans and Directors briefing the delegation from Vishnu Group of Institutions on the functioning of the various Departments and Directorates at SRM AP.

 

Continue reading →

As a significant contribution to science, Assistant Professor Dr Amit Chakroborty and his Doctoral Scholar, Arindam Basu, from the Department of Physics have published a groundbreaking paper titled, Viability of boosted light dark matter in a two-component scenario in the Physics Review D (Nature Index ) Journal. The research explores a two-component dark matter model and addresses the theoretical challenges in hopes of improving our understanding and painting a complete picture of dark matter.

Abstract

We study the boosted dark matter (BDM) scenario in a two-component model. We consider a neutrinophilic two-Higgs doublet model (ν2HDM), which consists of one extra Higgs doublet and a light right-handed neutrino. This model is extended with a light (∼ 10 MeV) singlet scalar DM ϕ3, which is stabilized under an extra dark ZDM symmetry and can only effectively annihilate through the CP even scalar H. Although the presence of a light scalar H modify the oblique parameters to put tight constraints on the model, the introduction of vectorlike leptons (VLL) can potentially salvage the issue. The vectorlike doublet N and singlet χ are also stabilized through dark ZDM symmetry. The lightest vectorlike mass eigenstate (χ1 ∼ 100 GeV) is the second DM component of the model. Individual scalar and fermionic DM candidates have Higgs/Z mediated annihilation, restricting the fermion DM in a narrow mass region while a somewhat broader mass region is allowed for the scalar DM. However, when two DM sectors are coupled, the annihilation channel χ1χ1 → ϕ3ϕ3 opens up. As a result, the fermionic relic density decreases, and paves way for broader fermionic DM mass region with under-abundant relic: a region of [30 − 65] GeV compared to a narrower [40 − 50] GeV window for the single component case. On the other hand, the light DM ϕ3 acquires significant boost from the annihilation of χ1, causing a dilution in the resonant annihilation of ϕ3. This in turn increases the scalar DM relic, allowing for a smaller mass region compared to the individual case. The exact and underabundant relic is achievable in a significant parameter space of the two-component model where the total DM relic is mainly dominated by the fermionic DM contribution. The scalar DM is found to be sub-dominant or equally dominant

Practical Implementation/ Social Implications of the Research:

This research explores a new idea in the search for dark matter, the invisible substance that makes up most of the matter in our universe. Instead of assuming dark matter is made of just one kind of particle, this study investigates a two-component model, where a heavier dark matter particle can decay or interact to produce a lighter, faster one. These “boosted” light dark matter particles could leave detectable traces in experiments here on Earth. The study carefully examines how this model fits with current cosmological observations and what conditions are needed for it to work.

While the work is theoretical, it has strong practical implications: it can guide ongoing and future experiments in detecting dark matter more effectively. Understanding dark matter is one of the most important unsolved problems in physics, and progress here could lead to understanding more about the picture of the universe. In the broader sense, such deep-space research inspires innovation, sharpens technology, and fuels curiosity-driven science that ultimately benefits society.

Collaborations:

This work has been done in collaboration with Mr Arindam Basu, PhD Scholar, the Department of Physics, SRM University-AP.

Future Research Plans:

• Study of the Dark Matter Direct Detection prospects.
• Study of the Dark Matter Indirect Detection prospects.
• Searching new physics at energy frontier.

The link to the article

Continue reading →

While the saying – ‘School time is the best time in a child’s life,’ would have been true years ago, it no longer reflects the current scenario among school-going adolescents. Today, children in school can be under just as much stress and burden as a fully grown adult. Though factors like academic pressure, social expectations, and family dynamics mattered even earlier, today these factors have developed a new-found intensity, and when topped with the hormonal changes, can adversely affect an adolescent, making their life challenging. Dr Sandra Roshni Monterio, Assistant Professor at the Department of Psychology, analyses the situation through her paper titled Psychosocial predictors of adolescent stress: insights from a school-going cohort.

Brief abstract

This study investigates the psychosocial factors influencing stress among 1,104 school-going adolescents in Telangana, India. Utilising the Adolescence Stress Scale and various psychosocial measures, hierarchical multiple regression and serial mediation analyses revealed that emotional instability, ill health experiences, conscientiousness, and psychosocial support significantly predict adolescent stress, explaining 6% of the variance. Serial mediation models highlighted family health and emotional efficacy as key mediators. The findings underscore the complex interplay of psychosocial factors in adolescent stress and suggest targeted interventions focusing on emotional regulation and family health to mitigate stress.

Explanation of the Research in Layperson’s Terms

Adolescence is a time of big changes, and while this is true globally, Indian school-going children may experience these changes differently because of our unique cultural and social expectations. This research looks at why teenagers feel stressed and what factors contribute to it. We studied over 1,100 students from schools in Telangana, India, to understand how things like their personality, family life, and social support affect their stress levels. We found that feeling emotionally unstable, having health problems, being overly responsible, and even the kind of support they get from others can increase stress. In fact, too much social support, especially when it’s uninvited or feels controlling, can make adolescents feel even more overwhelmed. This is particularly relevant in India, where family bonds are strong but can sometimes come with pressure, judgment, or expectations. Additionally, the turn to virtual dependency may be temporarily comforting but may not always translate to meaningful connection leading to greater feelings of isolation.

Together, these factors explain a small but important part of why teens feel stressed. We also discovered that a healthy family environment and the ability to manage emotions can help reduce stress. This means that helping teens cope with their emotions and supporting strong family relationships could make them feel less stressed.

Despite growing awareness about teen mental health, most Indian studies have focused only on academic stress or used Western tools that may not capture the emotional landscape of Indian adolescents. Our study fills this gap by using tools grounded in Indian cultural realities and examining the “how” and “why” behind stress, not just “how much” stress exists.

In short, Indian children face a mix of visible and invisible pressures. To truly support them, we need to look beyond grades, listen without judging, and create spaces, both online and offline, where they feel safe, heard, and understood.

Practical Implementation or Social Implications:

The findings from this study have practical implications for schools, families, and mental health professionals. By identifying emotional instability and ill health as key stress contributors, schools can implement programs teaching emotional regulation skills to help adolescents manage stress. The significant role of family health suggests that family-based interventions, like workshops promoting positive parent-child communication, could reduce teen stress. Additionally, the findings challenge the assumption that more social support is always better, suggesting the quality of support matters more than quantity, especially in cultures undergoing social transition. There is a need for tailored support that respects adolescents’ desire for independence. These insights can inform policies in educational and community settings to foster environments that reduce stress and promote mental well-being among teenagers, particularly in high-pressure cultural contexts like India.

Collaborations:

This study was a collaborative effort between SRM University-AP, GITAM University, Hyderabad Campus, and Centre for Health Psychology, University of Hyderabad

Future Research Plans:

Building on these findings, our future research will focus on practical and achievable steps to deepen our understanding of adolescent stress. We plan to develop culturally grounded interventions to improve family health and adolescent emotional efficacy, tailored to the Indian context. To address the modest explained variance, we will explore a limited set of additional factors, such as academic pressure and peer relationships.

Continue reading →

Dr Vemula Dinesh Reddy, Assistant Professor, Department of Computer Science and Engineering, has been granted a patent for his invention “A System And A Method for Fog-Based Animal Intrusion Detection” with the Application No: 202341026013, in the Indian Patent Official Journal. The invention acts as a groundbreaking fog computing-based system designed for real-time detection of animal intrusions in sensitive areas using smart sensors for instant alerts.

Abstract

This research introduces an intelligent system using fog computing to detect animal intrusions in sensitive or protected zones such as farmlands, highways, and forest borders. The system enables real-time data processing closer to the site of intrusion, offering faster detection and reduced dependency on centralised cloud systems. Furthermore, we proposed the Quantum-Inspired optimisation technique called Quantum Evolutionary Algorithm.

Practical Implementation/ Social Implications of the Research

Through this invention, we can:

• Prevent crop destruction and reduce human-wildlife conflict.
• Enhance safety on highways where animal crossings are common.
• Support forest conservation efforts by enabling non-intrusive monitoring.
• Reduce latency and bandwidth costs by processing data locally (via fog computing).

Future Research Plans

• Integrating AI-based species classification to identify specific animals.
• Creating a scalable mesh network for larger geographic coverage.
• Enhancing energy efficiency through solar-powered edge nodes.
• Extending the system to include drone-based visual surveillance.

Continue reading →

The digital age is flooded with multimedia content ranging from articles and podcasts to videos and images, spanning multiple languages. The challenge isn’t just accessing information but understanding and summarising it efficiently. Addressing this need, a pioneering patent titled “A System and Method for Multimodal Multilingual Input Summarization Using Quantum Motivated Processors” (Application Number 202341005519) has been granted to Dr Ashu Abdul, Assistant Professor in the Department of Computer Science and Engineering, and Mr Phanidra Kumar S, PhD Scholar, as published in the Indian Patent Office Journal. This innovative system converts all kinds of media like text, images, audio, and video into descriptive text, then leverages quantum-inspired algorithms to extract and stitch together the most relevant sentences and visuals, thereby crafting a perfect summary.

Abstract

This research details a system and method for summarizing multimodal and multilingual input data by leveraging quantum-motivated processors. The system is designed to handle input documents comprising text, audio, image, and video data, potentially in multiple languages. A pre-processing engine extracts textual descriptions from all these modalities (using deep learning, CNN, VAF, Python), merging them into a unified text corpus. A quantum enabler module assigns initial probabilities and encodes sentences from this corpus into binary states (0s or 1s), reflecting a quantum measurement concept (using randint). A selection module, utilizing an objective/fitness function incorporating factors like term frequency, sentence length, pronoun presence, coverage (QCSS-based similarity), and title relevance (Sentence-to-Title QCSS), calculates a fitness score for each encoded sentence and shortlists relevant ones using a “radiant function”. This module also handles duplicate removal based on QCSS. A rearrangement module scores and reorders the shortlisted sentences based on metadata (like publishing date) and scores (like ROUGE). A summary generation module produces a textual summary. Concurrently, an image selector engine selects a relevant image from the input data, primarily based on the image’s textual description and the generated summary, often using QCSS (Quantum Cosine Similarity Score). Finally, an output engine merges the textual summary with the selected image to create a multioutput (MO) summary. The system may also include a machine translation engine to translate non-English extracted descriptions into English before summarization, if needed. The approach employs quantum measurement and adaptive quantum rotation gates within an evolutionary framework (suggesting a Quantum Genetic Algorithm approach, referred to as MSQMGA) to find optimal summary sentences, demonstrating improved performance and efficiency compared to traditional Genetic Algorithms.

Practical Implementation of the Research

The system’s design outlines a modular architecture suitable for software or hardware implementation, involving distinct processing engines (Input, Pre-processing, Quantum Enabler, Selection, Rearrangement, Summary Generation, Image Selector, Output, and potentially Machine Translation). Key technical details include:

• Pre-processing: Use of Python, deep learning models (VAF, CNN) for extracting textual descriptions from audio/video/image data.
• Quantum Enabler/Selection: Assignment of initial probability (1/√2), encoding via a randomized quantum measurement model (randint(0,1) <= alpha_i), fitness function incorporating multiple weighted factors (fs = [0.75 * ((w1) * C * + w2 * pn * Ts) + 0.25 * S1] * Tf), QCSS for similarity checks (summary-to-document, sentence-to-title, intra-sentence, image selection), shortlisting via a radiant function, duplicate removal via QCSS.
• Rearrangement: Sorting shortlisted sentences based on metadata like publishing date and ROUGE score.
• Image Selection: Deep learning models like QTL-based CNN-LSTM, thresholding (e.g., 0.85).
• Multilingual Handling: Explicit mention of a Machine Translation Engine (122) to translate non-English extracted text into English
• Performance: Claims of achieving ROUGE-1 scores (e.g., 0.78) and QCSS scores (e.g., 92% for image ID), and being “quite faster” compared to traditional GA approaches.
• Datasets: Evaluation conducted using DUC 2005, DUC 2007, Indian Express datasets for text summarization, and Flickr 8k, Flickr 30k, Indian Express datasets for image description (ID).

These specifics suggest practical implementation could involve developing software modules that utilize libraries for deep learning (e.g., TensorFlow, PyTorch with CNN, LSTM components), natural language processing (e.g., NLTK, spacy for tokenization, POS, lemmatization), and potentially frameworks for simulating or interfacing with quantum-inspired algorithms. The “real-time applications” aspect implies design considerations for efficiency and processing speed. Potential deployments include news aggregation platforms, content management systems, competitive intelligence dashboards, cross-cultural communication tools, or applications for analysing vast archives of mixed-media data.

Social Impact

Beyond basic information access, this technology has the potential to foster greater understanding and reduce bias by providing summarized content across linguistic and cultural divides. It could empower individuals and organisations to consume and analyse global information landscapes more effectively. For educators, it could facilitate the creation of multimodal learning materials from diverse sources. For researchers, it could accelerate literature review across different fields and languages. However, it also raises potential implications related to the source and neutrality of the summarisation models themselves – whose perspective is encoded, and how might summaries differ based on training data or algorithmic biases? Ethical considerations around information representation and potential manipulation of summaries would be important as such technologies become more widely adopted.

Future Research Plans

Although the patent doesn’t explicitly list a roadmap, the detailed description and stated advantages imply several potential future research directions and refinements based on the current work:

1. Algorithmic Refinement: Further optimizing the “quantum-motivated” genetic algorithm (MSQMGA) framework, including the fitness function weights (w1, w2 are mentioned as trainable parameters), the “radiant function” for shortlisting, and the quantum measurement mapping.
2. Modality Integration: Enhancing the pre-processing and integration of information from different modalities, potentially exploring more sophisticated methods for cross-modal semantic understanding beyond extracting textual descriptions.
3. Cross-Lingual Capabilities: Improving the multilingual summarization accuracy, potentially integrating more advanced machine translation techniques directly within the summarization process or extending the quantum-motivated selection mechanism to handle multi-language sentence comparisons natively.
4. Quantum Hardware Exploration: Investigating the feasibility and performance benefits of implementing parts of the system, particularly the quantum enabler and selection modules, on actual quantum computing hardware as it matures, moving beyond the current “quantum-motivated” (inspired/simulated) approach.
5. Scalability and Real-time Performance: Further developing the system to handle even larger volumes of multimodal, multilingual data efficiently for true real-time applications.
6. Evaluation and Benchmarking: Expanding testing on a wider range of diverse datasets and benchmarking against more varied state-of-the-art multimodal and multilingual summarization techniques.
7. Summarization Quality: Focusing on subjective quality metrics of the generated summaries, such as coherence, readability, and conciseness, in addition to objective metrics like ROUGE
8. Image Selection Enhancement: Refining the image selection process, potentially considering factors beyond just textual description and summary similarity, such as image quality, saliency, and contextual relevance within the broader multimodal input.

Continue reading →

In a commendable stride toward advancing edge AI technology, Dr Swagata Samanta, Assistant Professor in the Department of Electronics and Communication Engineering along with B.Tech students Amrit Kumar Singha and Arnov Paul, have successfully filed and published a patent titled “A System for FPGA-based Acceleration of Support Vector Machine (SVM) Computations, and a Method Thereof” in Patent Office Journal.

The patented system introduces a novel approach to speeding up machine learning algorithms specifically Support Vector Machines by implementing them on Field-Programmable Gate Array (FPGA)-based embedded systems. By harnessing the capabilities of Xilinx’s Vitis High-Level Synthesis (HLS), the team was able to develop a hardware-accelerated solution that dramatically enhances computational efficiency while simplifying the design process through C++-based abstraction.

Abstract:

By utilising the power and flexibility of FPGAs, the aim is to enhance the performance and efficiency of these compute-intensive tasks without delving into the intricate low-level hardware details. The approach involves implementing the fundamental concepts of SVM algorithms using the Vitis HLS design flow provided by Xilinx. Vitis HLS allows us to describe these algorithms at a higher level of abstraction using C++, enabling faster development and easier optimisation compared to traditional HDL- based designs.

 

By leveraging the capabilities of Xilinx Zynq-based embedded systems, we can efficiently accelerate these algorithms and improve overall system performance. GDS2 is a standard file format used for representing integrated circuit layouts, playing a crucial role in the physical design and fabrication of FPGAs by capturing the geometric and connectivity information of components such as logic blocks, interconnects, and I/O pads.

Proper GDS2 layout design is essential for ensuring manufacturability, optimising performance, maximising area utilisation, and maintaining signal integrity within an FPGA, taking into account physical constraints and design rules imposed by the fabrication process to minimise signal propagation delays, reduce power consumption, optimise timing, achieve higher density, minimise wasted space, and employ proper routing and shielding techniques to minimise crosstalk, signal reflections, and other signal integrity issues. By combining the power of HLS using Vitis with the Cadence GDS2 layout design, this project aims to accelerate SVM algorithms on FPGA-based embedded systems.

The use of Vitis HLS simplifies the development process and enhances productivity, while the GDS2 layout design ensures manufacturability, performance optimisation, efficient area utilisation, and signal integrity. This work showcases the potential of using FPGAs for hardware acceleration of machine learning algorithms, opening up new possibilities for embedded systems in various domains such as computer vision, natural language processing, and data analytics.

 

Implementation and Impact:

This work advances machine learning on FPGAs by optimising SVM algorithms for speed via parallel processing, supporting multiple ML models, and using Vitis HLS for efficient hardware-software co-design, while reducing power consumption and enabling scalability through multi-FPGA or hybrid systems; we’ll test in real-world IoT, automotive, and medical applications, compress models with pruning and quantisation, transition to ASICs for mass production, and develop standardised interfaces and on-device learning to enhance privacy and adaptability.

These advancements could make AI more accessible for low-cost medical diagnostics or smart devices in underserved areas, reduce carbon footprints through energy efficiency, boost economic growth through job creation, and improve safety in self-driving cars and smart homes. However, ethical design is crucial to prevent bias or misuse and ensure equitable benefits across society.

Future Directions:

Building on this foundation, the team plans to expand their architecture to support additional ML models, deepen hardware-software co-design efforts, and implement on-device learning for adaptive, privacy-preserving intelligence. Long-term goals also include transitioning to custom ASIC implementations for mass production and developing standardised interfaces to enhance system interoperability.

Continue reading →

Have you ever questioned whether the food on your plate is truly fresh—or where it was grown?

The efficiency of our food supply chain is often undermined by challenges such as limited traceability, poor communication, and rising labor costs. These disruptions can lead to food safety concerns, spoilage, and increased costs. Hence there’s a growing need for better tracking and transparency throughout the entire supply chain.

Addressing these issues, P Naga Sravanthi, Assistant Professor in the    Department of Computer Science and Engineering,   published a chapter titled “ Multi-Criteria Decision Making Methods and Sustainable Applications in the Digital Age” in the book Blockchain-based Supply Chain Management for Sustainable Food Production Using the Analytic Hierarchy Process (AHP) by IGI Global Publishers (US).

The chapter explores how blockchain can be incorporated into the food supply chain and used to track every step of the food journey. The Analytical Hierarchy Process (AHP) helps assess critical metrics like quality, cost, or environmental impact. AHP provides a transparent platform to improve food systems and make sustainable choices for all stakeholders, including farmers, sellers, and consumers.

Abstract:

This chapter discusses how blockchain technology can build transparent and trustworthy food supply chains. It combines blockchain technology with the Analytic Hierarchy Process (AHP), a structured method to rank and prioritise sustainability goals, including reducing waste, ensuring freshness, and supporting local farmers. This approach guides food producers in making smarter, eco-conscious decisions, helping them align with global sustainability goals while improving production and distribution efficiency.

Societal Impact:

This research can make a massive difference in the real-world scenario. Farmers can trace their products, suppliers can avoid fraud, and customers can get safe and fresh food. It also supports small-scale producers by giving them a voice in the supply chain. Governments and companies can use this system to ensure sustainability, reduce food loss, and build public trust in food safety and fair trade.

Future Plans :

Looking ahead, the goal is to test this blockchain-AHP model in farm-to-fork supply chains, especially in rural or emerging markets. Collaborations with agricultural cooperatives and food tech startups are in progress. Machine learning will also be integrated to improve real-time decision-making. Future research will focus on policy frameworks and community-based blockchain adoption to expand its impact on sustainable food production.

Continue reading →

In a significant breakthrough for sustainable construction, a new and improved version of traditional cement mortar has been developed by Dr Geeta Devi, Assistant Professor, Department of Civil Engineering along with Dr Mohanraj Rajendran, Assistant Professor, and Mr Lokeshwaran Murugan, M.Tech Scholar have filed and published a patent titled “A Mortar Composition and a Process for its Preparation”.

This enhanced mortar becomes stronger and more durable while setting faster to reduce construction time. The improved formula absorbs less water, increasing its resistance to moisture and weather damage. It also provides better workability, making it easier to apply on construction sites. This innovation saves both time and cost in construction projects while delivering longer-lasting structural integrity, making it a valuable advancement for the building industry.

Abstract
The research presents an innovative mortar composition and a simple, scalable process for its preparation, designed to improve construction quality and efficiency. This innovative formulation incorporates polyester fibers (Recron 3s). It employs water with controlled Total Dissolved Solids (TDS) levels, resulting in remarkable improvements in compressive and flexural strength, faster setting times, and reduced water absorption. This novel formulation demonstrates up to 21.5% improvement in strength and offers a practical, eco-friendly solution for modern construction needs.

A new and improved version of traditional cement mortar has been developed by adding Recron 3s polyester fibers to the mix and controlling the mixing water quality through Total Dissolved Solids (TDS) level adjustments. This enhanced mortar becomes stronger and more durable while setting faster to reduce construction time. The improved formula absorbs less water, increasing its resistance to moisture and weather damage, while also providing better workability that makes it easier to apply on construction sites. This innovation saves both time and cost in construction projects while delivering longer-lasting structural integrity, making it a valuable advancement for the building industry.

Practical Implementation & Social Impact

This new mortar composition is particularly tailored for practical use in real-world construction, including residential buildings, educational institutions like schools and colleges and infrastructure projects like bridges, pavements and stable structures for harsh environments. Some of the key performance improvements are the enhanced durability giving long-term strength, Faster setting time aiding quicker construction and better water resistance, minimising damage and deterioration.

Using this mortar composition in construction could reduce maintenance and repair costs by extending the lifespan of structures. It also supports sustainable practices by utilising recyclable polyester fibres (Recron 3s). The ability to utilise non-potable water with controlled Total Dissolved Solids (TDS) levels makes the process both eco-friendly and cost-effective in regions with limited access to clean water resources. This innovation contributes meaningfully to Sustainable Development Goals (SDGs) in infrastructure and housing, particularly in resource-limited or climate-sensitive areas.

Future Research Plans

• Scaling the mortar production for industry use.
• Exploring nano-materials and industrial by-products to further improve mortar performance and reduce environmental impact.
• Testing the composition under extreme climate conditions and integrating it with 3D printing in construction.
• Investigating automated mixing systems and AI-based optimisation of mix design for site-specific applications.

Research Team

Dr Geeta Devi – Assistant Professor, SRM University-AP
Dr Mohanraj Rajendran – Assistant Professor, SRM University, Delhi NCR
Mr Lokeshwaran Murugan – M.Tech Scholar, SRM University, Delhi NCR

Continue reading →

The paper titled “Nanosecond Laser-Induced Conversion of Leaf-Like Co-MOF to Nanoscale Co@N-gCarbon for Enhanced Multifunctional Electrocatalytic Performance” by Dr Narayanamoorthy Bhuvanendran, Assistant Professor, Department of Environmental Science and Engineering, was published in the ChemSusChem journal with a Q1 rating with an impact factor of 7.5. The study presents a breakthrough in clean energy research with an innovative nanosecond laser-based technique that transforms metal–organic frameworks into high-performance electrocatalysts faster, more energy-efficiently, and eco-friendly.

Abstract :

Conversion of metal–organic frameworks (MOFs) into metal-nitrogen-doped carbon (M–N–C) catalysts requires a high-temperature process and longer processing time under a protective atmosphere. This study utilises a low-energy nanosecond laser processing (LP) technique to convert aqueous synthesised 2D leaf-like Co-MOF (L-Co-MOF) into nanoscale cobalt metal encapsulated within a nitrogen-doped graphitic carbon matrix (Co@N-gC, Co-LP) in a shorter period under air atmosphere.

The laser-induced process results in the formation of Co@N-gC with smaller Co particle size, uniform distribution, and better interaction with the carbon support compared to the conventional pyrolysis process (CP). LP catalysts result in enhanced multifunctional electrocatalytic activity over CP (Co-CP) catalysts owing to the tunable metal–support interaction, higher charge transfer, and presence of multiactive sites.

Under optimised conditions (laser fluence: 5.76 mJ cm−2 and scan speed: 10 mm s−1), the Co-LP-5 catalyst exhibits better ORR performance, with onset and half-wave potentials of 0.92 and 0.76 V, respectively. Additionally, Co-LP-5 delivers excellent water-splitting performance, with OER and HER overpotentials of 380 and 280 mV, respectively, achieving an overall energy efficiency of 77.85%. Furthermore, Co-LP-5 demonstrates exceptional durability over 48 h of real-time testing, outperforming the Co-CP, and the proposed low-energy LP is viable for fabricating multifunctional catalysts.

The research focuses on developing new materials for more efficient clean energy production, specifically advanced catalysts that accelerate chemical reactions. Traditionally, creating an effective M–N–C (metal–nitrogen–carbon) catalyst requires heating metal-organic frameworks (MOFs) to high temperatures in oxygen-free environments, which is time-consuming and energy-intensive.

This study introduces a simpler, faster, and energy-saving approach using nanosecond laser pulses to transform cobalt-containing MOFs into a new material called Co@N-gC. This laser method operates in normal air, significantly reducing time and energy consumption. The resulting catalyst features smaller, evenly distributed cobalt particles that enhance interaction with the carbon support, leading to improved activity and efficiency in key energy reactions. Our laser-made catalyst, Co-LP-5, exhibited excellent performance over 48 hours, outperforming traditional methods. This breakthrough demonstrates that low-energy laser techniques can create powerful, multifunctional catalysts for clean energy more quickly, cheaply, and sustainably.

Practical implementation of the research :

We are working on developing new materials that help produce clean energy in a faster, cheaper, and more eco-friendly way. Usually, scientists use a high-heat process to convert materials called metal-organic frameworks (MOFs) into something called catalysts, which are substances that help speed up important chemical reactions, such as splitting water to produce hydrogen fuel or helping batteries and fuel cells work better.

However, the traditional method requires a lot of energy, time, and special conditions to work. In the study, we found a much simpler and faster way to make these useful catalysts. Instead of heating the material for a long time, we used a laser to quickly transform the MOF into a new material. We did this in normal air using short pulses of light from a laser, and within seconds, the material changed into a highly active form containing tiny cobalt particles surrounded by nitrogen-rich carbon. This new material works more efficiently and lasts longer than the one made by traditional heating.

Our method is not only quicker and more energy-efficient, but also easier to scale up for larger use. This laser technique can be used to create advanced materials for fuel cells, batteries, and systems that produce hydrogen from water. These technologies are crucial for clean energy and can help reduce pollution and dependence on fossil fuels.

The real-world impact of this research is significant. It can make clean energy technologies more affordable and accessible, especially in developing regions with limited energy access. It also supports the shift toward a greener economy by promoting sustainable methods and creating new opportunities in clean energy industries. In the long term, this work contributes to fighting climate change and protecting the environment by helping the world move toward cleaner, safer energy solutions.

Future Research Plans:

• Explore using different metal-based MOFs to develop a broader range of catalysts for clean energy applications.
• Optimise laser processing conditions such as energy, speed, and environment to improve the quality and performance of the final materials.
• Study the detailed mechanism of how the laser converts MOFs into active catalysts to better understand and control the process.
• Test the laser-made catalysts in actual energy devices like fuel cells and water-splitting systems to evaluate their real-world performance.
• Investigate methods to scale up the laser processing technique for larger production while keeping it cost-effective and energy efficient.
• Expand the application of these materials to other areas such as carbon dioxide reduction, hydrogen storage, or environmental sensing.

 

Collaborations:

Prof. Sae Youn Lee, Dongguk University, Republic of Korea.
Dr. Srinivasan Arthanari, Chungnam National University, Republic of Korea.

 

Continue reading →

In the quest for next-generation organic electronic materials, researchers have drawn inspiration from nature’s intricate designs. A groundbreaking study titled “Bulk Assembly of Intrachain Folded Aromatic Polyamides Facilitating Through-Space Charge Transport Phenomenon” led by Dr Sabyasachi Mukhopadhyay, Associate Professor in the Department of Physics, introduces a novel class of polymers that mimic the secondary structures of biomolecules. Published in the high-impact Q1 journal SMALL with an Impact Factor of 13.0, this research unveils the potential of intrachain folded aromatic polyamides in facilitating efficient through-space charge transport.

Abstract :
This study presents the design and synthesis of periodically grafted aromatic Polyamides capable of intrachain folding, mimicking secondary structures seen in biomolecules. Leveraging the immiscibility between aromatic backbones and Polyethylene glycol (PEG) side chains, the polymers self-assemble into lamellar, phase-separated domains with ordered π-stacking.

The structural order is further enhanced by incorporating aromatic guest molecules, enabling efficient through-space charge transport. Structural and morphological investigations via SAXS, WAXS, AFM, and TEM confirm the formation of highly ordered π-domains. Charge transport measurements reveal vertical current densities as high as 10⁻⁴ A/cm² in annealed host–guest complexes, comparable to conventional conjugated polymers, demonstrating the potential of these materials for stable, anisotropic organic electronics.

Practical implementation :

This research provides a new strategy for designing flexible, stable, and efficient organic electronic materials without the need for traditional conjugated polymers. The ability to precisely control the orientation and spacing of conductive regions at

The nanoscale opens doors for:

• Wearable and stretchable electronics
• Flexible sensors and low-power devices
• Organic transistors and memory devices with tunable directionality
• Environmentally stable devices, useful in humid or high-temperature conditions
• These innovations can lower manufacturing costs, enhance sustainability, and enable novel applications in healthcare, IoT, and smart textiles.

This research was a collaborative effort between multiple departments and institutions including Department of Chemical Sciences, IISER Mohali, Department of Physical Sciences, IISER Mohali and Department of Physics, SRM University – AP (Ramkumar K, Dr Sabyasachi Mukhopadhyay) and was supported by Department of Science and Technology – Science and Engineering Research Board (DST-SERB)

Future research plans:

Dr Sabyasachi Mukhopadhyay is  working towards “Integrated Center for Organic Electronics” – a multidisciplinary innovation hub focused on designing the next generation of flexible, sustainable, and high-performance electronic materials and devices.

Continue reading →