Research News

Dr Nilkantha Meher, an Assistant Professor in the Department of Physics at SRM University-AP, has significantly contributed to science with his research paper on using thermal light to detect objects with unmatched precision. This phenomenal work that featured in the journal Physical Review A will positively contribute to the fields of sensing, gravitational wave detection, and phase microscopy.

Abstract:

Estimation of the phase delay between interferometer arms is the core of transmission phase microscopy. Such phase estimation may exhibit an error below the standard quantum (shot-noise) limit, if the input is an entangled two-mode state, e.g., a N00N state. We show, by contrast, that such supersensitive phase estimation (SSPE) is achievable by incoherent, e.g., thermal, light that is injected into a Mach-Zehnder interferometer via a Kerr-nonlinear two-mode coupler. The phase error is shown to be reduced below, being the mean photon number, by thermal input in such interferometric setups, even for small nonlinear phase-shifts per photon pair or for significant photon loss. Remarkably, the phase accuracy achievable in such setups by thermal input surpasses that of coherent light with the same. Available mode couplers with giant Kerr nonlinearity that stems either from dipole-dipole interactions of Rydberg polaritons in cold atomic gas or from cavity-enhanced dispersive atom-field interactions may exploit such effects to substantially advance the interferometric phase microscopy using incoherent, faint light sources.

Practical Implementation:

The proposed nonlinear interferometer in this research can serve as a robust quantum sensor, making it suitable for a range of applications, including object sensing, gravitational wave detection, and phase microscopy.

Your Collaborations:

Prof. Gershon Kurizki (Weizmann Institute of Science, Israel)
Prof. Tomas Opatrny (Palacky University, Czech Republic)
Dr. Eilon Poem (Weizmann Institute of Science, Israel)
Prof. Ofer Firstenberg (Weizmann Institute of Science, Israel)

Future Research Plans:

He is currently investigating the sensing of quantum entanglement and generating highly nonclassical states using various nonlinear interferometers. This research has significant implications for distributed quantum communication and quantum information processing.

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Prof. Ranjith Thapa in collaboration with two of his research scholars, Mr E. S. Erakulan Mr Sourav Ghosh and has come up with a groundbreaking research that has resulted in the publication of a scholarly paper titled, Specific Descriptor for Oxygen Evolution Reaction Activity on Single Atom Catalysts Using QM/ML.

Abstract of the paper

Descriptors are properties or parameters of a material that is used to explain any catalytic activity both computationally and experimentally. Such descriptors aid in designing the material’s property to obtain efficient catalyst. For transition metals, d-band center is a well-known descriptor that shows Sabatier type relation for several catalytic reactions. However, it fails to explain the activity when considering same metal active site with varying local environment. To address this, density functional theory was used for single atom catalysts (SACs) embedded on armchair and zigzag graphene nanoribbons (AGNR and ZGNR). By varying the anchoring nitrogen atoms’ orientation and considering pristine and doped cases, 432 active sites were used to test the oxygen evolution reaction (OER) activity. It was observed that S and SO2 dopant helps in reducing the overpotential on Co-SAC (h = 0.28 V). Along with the d-band center, a total of 105 possible descriptors were individually tested and failed to correlate with OER activity. Further, PCA was employed to narrow down unique descriptors and machine learning algorithms (MLR, RR, SVR, RFR, BRR, LASSO, KNR and XGR) were trained on the two obtained descriptors. Among the models, SVR and RFR model showed highest performance with R2 = 0.89 and 0.88 on test data. This work shows the necessity of a multi-descriptor approach to explain OER catalytic activity on SAC and the approach would help in identifying similar descriptors for other catalytic reactions as well.

Social Implications:

Computational studies have proven to be a vital tool to predict new materials and also assess the behaviour towards various catalytic reactions. They also identify the innate properties of the material which drives the catalytic activity. It helps in designing the material with required property to improve the catalytic activity. Descriptors are such computationally obtained properties/parameters of a material that has a meaningful relation with any catalytic property of a chemical reaction. d-band center, given by Hammer and Norskov in 1995, explained the binding strength of oxygen atom on pure transition metals. The d-band center shows Sabatier type relation with chemical activity and has been widely used to explain the catalytic activity of several reactions since its formulation. The adsorbate state after interaction with delocalized s-states of the metal atom is almost constant while that resulting from d-states interaction, is split into bonding and antibonding states. Hence the s-states were not considered. It is well known that, when the dimensions of a system are lowered the states become narrow and localized. In such systems, the d-band center does not explain the catalytic activity well and it is an open research problem.

Future Projects:

Density functional theory with machine learning approach could further be used and improved on similar SACs from which a predictive model equation could be constructed. Also, the proposed models are open to exploration on other catalytic reactions as well.

The authors thank SRM University-AP and National Super Computing mission for providing the computational facility.

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Dr Ravi Kant Kumar, an Assistant Professor at the Department of Computer Science and Engineering, and his research scholar, Ms Gayatri Dhara, have come up with a patent titled “A System and Method for Enhancement Of Visual Saliency Of Intended Face In Group Photography.” The patent, with Application Number 202441040020, employs pathbreaking technology to enhance security and healthcare applications, with real-time face recognition and remote diagnostics.

Abstract:

Visual saliency is a way of figuring out which parts of a scene draw our attention the most. When looking at a crowd or a group of faces, our eyes naturally focus more on certain faces than others. This happens because some faces have dominant features that stand out more. For faces that don’t naturally catch our attention, there is a need to make them more noticeable. This new method and system are designed to do just that. The system calculates scores based on various factors like skin tone, colour, contrast, position, and other visual details. These scores help identify which face needs enhancement, making it more prominent in a group of faces. The primary advantage of this invention is its potential to improve user experience in various applications, such as photo editing, social media, security systems, and more. By giving users, the control to select and enhance a specific face, it allows for a more personalised and targeted approach to face recognition and enhancement. This could be particularly beneficial in scenarios where the user wants to highlight a specific individual in a group photo or in a crowd. Overall, this invention represents a significant advancement in the field of face recognition and image enhancement, offering a novel and user-centric approach to visual saliency. It opens up new possibilities for user interaction and control in image editing and face recognition technology.

Practical and Social Implications

The practical implementation of this research lies in its ability to identify and enhance faces within a group or crowd that do not naturally draw attention. This innovative method and system address this issue by calculating saliency scores based on factors such as skin tone, colour, contrast, position, and other visual details. These scores are then used to identify faces that need enhancement to become more prominent in a group. The system’s ability to enhance specific faces has significant practical applications in several fields.

Photo Editing: Users can easily enhance specific individuals in group photos, ensuring that everyone stands out as desired. This is particularly useful for personal photos, event photography, and professional photo editing.

Social media: Enhanced face recognition and saliency can improve user experience by allowing users to highlight specific people in their posts, making photos more engaging and personalised.

Security Systems: In surveillance and security applications, the ability to enhance less prominent faces can improve the accuracy of face recognition systems, aiding in the identification of individuals in crowded or low-visibility conditions.

Collaborations:

SRM University-AP,
Dr Ravi Kant Kumar,
Mrs Gayathri Dhara.

Future Research Plans:

Future plans for this visual saliency-based face enhancement system include refining algorithms for greater accuracy and efficiency, and integrating with popular photo editing software and social media platforms for seamless user experience. The technology will be expanded into security and healthcare applications, enhancing real-time face recognition and remote diagnostics. Emphasis will be placed on reducing biases, ensuring privacy protection, and enabling user customisation. Collaborations with academic institutions will drive further research, while commercialisation efforts will focus on launching products globally.

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Dr Shekhar Singh Negi from the Department of Mathematics has published a research paper titled “A note on Sugeno exponential function with respect to distortion.” Dr Negi’s research investigates the Sugeno exponential function. This research develops new mathematical tools and rules to work with a different way of measuring things, which can be useful in various fields like economics, biology, or any area where traditional measurements don’t quite fit the problem at hand.

Abstract:

This study explores the Sugeno exponential function, which is the solution to a first order differential equation with respect to nonadditive measures, specifically distorted Lebesgue measures. We define k-distorted semigroup property of the Sugeno exponential function, introduce a new addition operation on a set of distortion functions, and discuss some related results. Furthermore, m-Bernoulli inequality, a more general inequality than the well-known Bernoulli inequality on the real line, is established for the Sugeno exponential function. Additionally, the above concept is extended to a system of differential equations with respect to the distorted Lebesgue measure which gives rise to the study of a matrix m-exponential function.

Finally, we present an appropriate m-distorted logarithm function and describe its behaviour when applied to various functions, such as the sum, product, quotient, etc., while maintaining basic algebraic structures. The results are illustrated throughout the paper with a variety of examples.

Collaborations:

Prof. Vicenc Torra, Professor at the Department of Computing Science at Umea University. His area of research include artificial intelligence, data privacy, approximate reasoning, and decision making.

Future Research Plans:

To explore the aforementioned derivative and investigate results with applications in real life.

The link to the article

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The Department of Commerce, under the Paari School of Business, is proud to present the research publication of Dr Lakshamana Rao Ayyangari, Guest Faculty Dr Sankar Rao, and Research Scholar Mr Akhil Pasupuleti. Their research paper, titled “Assessing the impact of ESG scores on market performance in polluting companies: a post-COVID-19 analysis,” is featured in the Q2 journal “Discover Sustainability.” Here is an interesting abstract of their research.

Abstract:

The study aims to unravel the impact of Environmental Social Governance (ESG) scores on the firm’s market performance of polluting companies. Moreover, the study also finds out the moderating effect of green initiatives. The study’s population consisted of 67 companies that were chosen from the list of polluting companies given by the Central Pollution Control Board of India for the post-COVID-19 timeframe of 2020–2023. The results indicate that the performance of ESG will improve the financial performance of the company.

Practical Implementation:

The analysis showed that companies with higher ESG scores generally perform better in the market. This means that firms that are more responsible in terms of environmental, social, and governance practices tend to do well financially. However, the study found that green initiatives did not have a significant impact on this relationship.

These findings are important for company managers and stakeholders. Understanding the connection between ESG practices and market performance can help managers create strategies to improve their ESG scores, ultimately boosting their financial performance.

Future Research Plans:

i) Focus on the R&D investment and sustainability.

ii) Studying the relationship between green finance and sustainability

iii) Exploring the relationship of CSR in sustainability

Link to the paper

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In a significant achievement for the Department of Computer Science and Engineering, Dr Mahesh Kumar Morampudi, Assistant Professor, along with B.Tech. student Ms. Nunna Lakshmi Manasa, has been granted a patent for their groundbreaking invention titled “System and method for generating synthetic data based on variational autoencoder.” The patent, with Application Number: 202241049545, was officially recognised in the Indian Patent Office.

This innovative system leverages the capabilities of variational autoencoders to generate synthetic data, which has vast applications in various fields, including machine learning, data privacy, and simulation. The ability to create high-quality synthetic datasets can significantly enhance research and development processes, providing researchers and practitioners with valuable tools for analysis and experimentation.

The recognition of this patent not only highlights the innovative spirit within the department but also underscores the collaborative efforts between faculty and students in advancing technology and contributing to the field of computer science.

Abstract of the Research

Diabetic retinopathy (DR) is a diabetes-related eye condition that occurs when high blood sugar levels cause damage to the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Over time, these damaged vessels can leak blood or other fluids, leading to vision impairment.

DR typically progresses through stages, starting with mild non-proliferative retinopathy, where small bulges form in the blood vessels, to proliferative retinopathy, the most severe stage, where new abnormal blood vessels grow on the retina and in the vitreous humor, potentially leading to blindness. Early detection and management are crucial to prevent significant vision loss, often involving regular eye exams, blood sugar control, and treatments like laser therapy or surgery.

Synthetic data generation for DR is an emerging approach to augment limited clinical datasets, enhancing the training of machine learning models for diagnosis and prognosis. The present disclosure envisages a system for generating synthetic data based on a variational autoencoder (VAE). This work explores the use of a VAE combined with deep learning for the detection and classification of DR. VAEs, known for their ability to learn compact and meaningful representations of complex data, are employed to generate latent features from retinal images, effectively capturing the subtle variations and anomalies indicative of DR. These latent features are then fed into a deep learning classifier, which is trained to categorise the severity of DR into various stages, ranging from no DR to proliferative DR.

Research in Layperson’s Terms

Imagine your eye is like a camera, and the retina at the back of your eye is the film that captures the pictures you see. DR is a condition that affects this “film” when someone has diabetes for a long time. High blood sugar levels can damage the tiny blood vessels in the retina, leading to vision problems and, in severe cases, blindness.

Our research is about creating a computer program that can help doctors detect and classify this eye condition more accurately. We use a special kind of technology called a VAE, which is like a smart artist that learns to understand and recreate detailed images of the retina. This “artist” can pick up on the tiny changes and patterns in the retina that might be missed by the human eye. Once the VAE has learned these details, it passes them on to another program, which is really good at sorting things into categories. This second program, a deep learning classifier, uses the information from the VAE to decide how severe diabetic retinopathy is—whether it’s mild, moderate, or severe.

By combining these two technologies, our system can help doctors detect diabetic retinopathy earlier and more accurately, which is crucial for preventing vision loss in people with diabetes.

Title of the Patent in the Citation Format
Inventor Name(s):
Dr Mahesh Kumar Morampudi
Nunna Lakshmi Manasa
“System and method for generating synthetic data based on variational auto encoder” with Application Number: ” 202241049545.” Date of Patent Grant. 29/07/2024

Practical Implementation:
The practical implementation of our research involves integrating the VAE and deep learning classifier into a software tool that can be used by eye care professionals. Here’s how it might work in a real-world setting:

1. Data Collection: Retinal images are collected from patients during routine eye exams. These images are fed into the system, which has been trained using a large dataset of retinal images, including those showing different stages of DR.
2. Feature Extraction: The VAE processes these images, learning to capture and condense the important features that indicate the presence and severity of DR. This step is crucial because it allows the system to focus on the subtle details that might signify early stages of the disease.
3. Classification: The deep learning classifier then takes these features and classifies the severity of DR. It provides a diagnosis, categorising the condition as no DR, mild, moderate, or severe. The system could also flag cases that need urgent attention, helping prioritise patient care.
4. Clinical Decision Support: The results are presented to the healthcare provider through an intuitive interface. This tool could be used in clinics, especially in areas with limited access to specialised eye care, allowing general practitioners or technicians to screen for DR and refer patients for further treatment if needed.

Social Implications:

1. Increased Access to Early Detection: By automating the detection of diabetic retinopathy, this technology can be deployed in remote or underserved areas where access to specialists is limited. Early detection is key to preventing vision loss, and this tool can make screening more accessible, especially in communities with high rates of diabetes.
2. Reduced Healthcare Costs: Early detection and treatment of diabetic retinopathy can prevent the progression to more severe stages, reducing the need for expensive treatments like surgery or long-term care for blindness. This could lower healthcare costs for both patients and the healthcare system.
3. Empowering Healthcare Providers: The tool can support general healthcare providers in making more accurate diagnoses, reducing the burden on specialists and allowing them to focus on more complex cases. This democratizes eye care, making it possible for more people to get the care they need without long delays.
4. Improved Patient Outcomes: With more accurate and timely diagnosis, patients can receive treatment sooner, leading to better health outcomes and preserving vision. This can significantly enhance the quality of life for individuals with diabetes, who might otherwise suffer from preventable blindness.
5. Ethical Considerations: While technology offers many benefits, it also raises ethical questions about the use of AI in healthcare, such as ensuring that the system is fair, transparent, and does not introduce biases. Continuous monitoring and updates would be necessary to ensure the system remains accurate and equitable.

Future Research Plans

1. Improving Model Robustness and Generalization: Enhance the VAE and deep learning classifier’s ability to generalize across diverse populations and imaging conditions.
2. Incorporating Multimodal Data: Integrate additional data sources, such as patient medical history, blood sugar levels, and genetic information, to improve diagnostic accuracy.
3. Real-Time Implementation and Mobile Integration: Adapt the system for real-time analysis and deploy it on mobile devices.
4. Collaboration with Clinical Trials: Validate the AI system’s effectiveness through collaboration with clinical trials.

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In a significant contribution to the field of food marketing and consumption, Ms P Naga Sravanthi, an Assistant Professor Ad hoc in the Department of Computer Science and Engineering, has recently published a book chapter titled “Blockchain-Based Food Influencer Verification.” The chapter is part of the book “Innovative Trends Shaping Food Marketing and Consumption,” shedding light on the emerging use of blockchain technology in authenticating food influencers.

In the rapidly evolving world of food marketing, trust and authenticity have become more critical than ever. The book chapter delves into how blockchain technology can revolutionise the way food influencers are verified and trusted by their audiences. Blockchain technology, known for its transparency and security features, has been making waves across various industries, and its application in the food sector is no exception.

Ms Naga Sravanthi’s work focuses on utilising blockchain to verify the credibility and authenticity of food influencers, a vital aspect in today’s digital era, where influencer marketing plays a significant role in consumer decision-making. The book chapter explores how blockchain can address issues of trust and reliability in food marketing by providing a secure and immutable record of influencers’ endorsements and partnerships. This innovative approach not only benefits consumers by ensuring they receive genuine recommendations but also helps food brands collaborate with legitimate and reputable influencers.

The publication of this book chapter underscores SRM University-AP’s commitment to fostering cutting-edge research and innovation in diverse fields, positioning its faculty members as thought leaders in their respective domains. Ms Naga Sravanthi’s work serves as a testament to the university’s dedication to academic excellence and pushing the boundaries of knowledge in emerging technologies.

Brief Description of the Book Chapter:
This chapter explores the integration of blockchain technology into the verification process of food influencers, providing a transparent and tamper-proof method to authenticate their influence and endorsements. By leveraging blockchain, the chapter outlines how the food industry can ensure that influencers are genuine, their endorsements are credible, and their impact is accurately measured. The chapter also delves into case studies and practical applications, demonstrating how this technology can reshape the landscape of influencer marketing in the food sector.

Significance of the Book Chapter
The significance of this chapter lies in its exploration of a cutting-edge application of blockchain technology in a field that deeply influences consumer behaviour in food marketing. As the digital space becomes more saturated with influencers, the need for a reliable verification system becomes paramount. This chapter is particularly meaningful because it addresses the growing concern of authenticity in influencer marketing, a crucial factor in maintaining consumer trust and brand integrity. For those passionate about the intersection of technology, marketing, and food, this chapter offers insights into how blockchain can be a game-changer in ensuring that the voices consumers trust is, indeed, trustworthy.

Target Audience

The book is primarily targeted at professionals in the food industry, including marketers, brand managers, and digital strategists, who are keen on understanding the latest trends shaping consumer behaviour and marketing practices. Additionally, this chapter will be valuable to academics and students in the fields of marketing, food science, and technology, as well as tech enthusiasts interested in the practical applications of blockchain.

Startups and entrepreneurs looking to innovate within the food sector will also find this book to be a vital resource, providing both theoretical insights and practical examples of how technology can redefine industry standards.In essence, “Innovative Trends Shaping Food Marketing and Consumption” and its chapter on blockchain-based influencer verification is an essential read for anyone looking to stay ahead of the curve in the dynamic world of food marketing.

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Dr Vineeth Thomas, an Assistant Professor in the Department of Political Science, has recently published a thought-provoking paper titled “Why Modi’s Third Term as India’s Prime Minister Will Be Tough” in the distinguished journal Asian Affairs. In his comprehensive analysis, Dr Vineeth delves into the political landscape of India and the various challenges Prime Minister Narendra Modi may face if he seeks re-election for a third consecutive term in 2024. Drawing on an array of political theories and current socio-economic indicators, the paper examines the underlying factors shaping Indian politics and public sentiment.

Dr Thomas argues that while Modi’s leadership has been marked by significant economic reforms and a robust foreign policy, the landscape is shifting in a manner that could complicate his re-election bid. Key issues explored in the paper include rising economic disparities, increasing urban unrest, and the challenge of maintaining communal harmony in a diversely populated nation.

The findings of this research are particularly pertinent in light of ongoing debates surrounding economic policy, agrarian distress, and the role of civil liberties – a backdrop that could significantly influence electoral dynamics heading into 2024.

The publication of this paper not only highlights Dr Thomas’s expertise in political science but also underscores the vital role academic discourse plays in contemporary political analysis. His insights contribute to a deeper understanding of India’s complex interplay between governance and public perception.

Abstract of the Research

On 9 June 2024, Narendra Modi became India’s Prime Minister for a third consecutive term, a feat previously accomplished only by Jawaharlal Nehru. However, this historic victory also began one of the most uncertain periods of his prime ministership. The fractured outcome of India’s 18th general election has created a fundamentally different political landscape. Modi’s Bharatiya Janata Party (BJP) secured just 240 seats, a significant drop from its 2019 tally, and was therefore short of an absolute majority. Although the BJP-led National Democratic Alliance (NDA) did win a majority, Modi will now be dependent upon coalition partners, necessitating a shift towards a more collaborative governance style.

Research in Layperson’s Terms

In the recent election, Modi’s party, the BJP, won only 240 seats, much fewer than in the last election and not enough for a clear majority. Although the broader alliance led by the BJP still has a majority, Modi now needs to rely more on other parties in the alliance to govern. This means he will have to work more closely with his coalition partners and be more collaborative in his leadership style.

Research Paper in the Citation Format

Vineeth Thomas, Agney GK & Arsha V Sathyan (2024), Why Modi’s Third Term As India’s Prime Minister Will Be Tough, Asian Affairs, ISSN: 0306-8374
(Routledge, SCOPUS/WoS Indexed)

Practical implementation or the social implications associated with Research

The practical implementation of my research lies in shaping policies that promote gender equality in political representation. By identifying barriers and proposing solutions, it aims to enhance women’s participation in politics, leading to more inclusive governance. The social implications include fostering a more equitable society with diverse perspectives in decision-making.

Your collaborations

Electoral Politics

Your Future Research Plans

Indian govt and politics

The Link to the Article

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Dr Debajyoti Kundu, Assistant Professor, Department of Environmental Science and Engineering, has conducted an impactful study on developing polyhydroxyalkanoates (PHA), an eco-friendly solution that can help reduce plastic waste and make food packaging more sustainable. His recent paper “Advancements in microbial production of polyhydroxyalkanoates (PHA) from wastes for sustainable active food packaging: An eclectic review”, published in the Q1 journal Biocatalysis and Agricultural Biotechnology, Dr Debajyoti investigates how microorganisms can convert waste into a special type of plastic called PHA, which can be used for food packaging.

Unlike regular plastics, PHA is biodegradable and safe for both the environment and human health. The study reviews recent innovations in making PHA stronger and more effective for packaging, including its ability to prevent food spoilage and improve food safety.

Abstract

This study explores advancements in microbial production of polyhydroxyalkanoates (PHA) from waste resources for sustainable active food packaging. It highlights the eco-friendly nature of PHAs as bioplastics and their potential to replace synthetic plastics in food packaging. The paper discusses recent technological improvements in PHA production and formulations, focusing on enhancing material properties to make PHA a viable alternative. It also examines trends in active packaging, including antimicrobial, antioxidant properties, and spoilage indicators, which can significantly improve food safety and quality.

Practical Implementation/ Social Implications of the Research

The practical implementation of this research involves using PHA-based materials for food packaging to replace conventional plastics. This can lead to reduced environmental pollution due to PHA’s biodegradability and lower reliance on fossil fuels. Social implications include improved food safety through active packaging features like antimicrobial and antioxidant properties, potentially reducing foodborne illnesses and extending shelf life. Additionally, using waste to produce PHA promotes waste recycling and resource efficiency.

Collaborations

This research is a collaborative effort among various prestigious institutions including St. Joseph’s University, SRM Institute of Science and Technology, Gurudas College, and SRM University-AP.

Moving forward, Dr Debajyoti will continue to work on improving the production processes and formulations of PHA to enhance its mechanical properties and cost-effectiveness. Additionally, he will explore new waste sources for PHA production and develop advanced active packaging technologies, such as smart packaging with sensors for real-time monitoring of food quality. Collaborations with industry partners to scale up production and test real-world applications of PHA-based packaging are another key focus.

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Dr Debajyoti Kundu, Assistant Professor in the Department of Environmental Science and Engineering, has published a research paper titled “Synthesis, delineation and technological advancements of algae biochar for sustainable remediation of the emerging pollutants from wastewater – A Review” in the esteemed Q1 journal “Environmental Research”, which has an impact factor of 8.3.

Dr Debajyoti’s research focuses on using algae biochar, which is particularly effective at cleaning polluted water. The study reviews how this biochar is made and improved and how it can effectively remove harmful substances from wastewater. This process is sustainable and environmentally friendly, offering a promising solution to water pollution.

Abstract

The study examines the synthesis, technological advancements, and applications of algae biochar for the sustainable remediation of emerging pollutants from wastewater. It highlights the unique properties of algae biochar, including its high surface area, pore volume, and adsorption capacity, which make it an effective medium for removing inorganic and organic contaminants from wastewater. The paper discusses various methods for producing algae biochar, such as pyrolysis, gasification, and torrefaction, and explores chemical and structural modifications to enhance its pollutant removal efficiency.

Practical Implementation /Social Implications of the Research

The practical implementation of this research involves using algae-derived biochar in wastewater treatment plants to remove harmful pollutants. This can lead to cleaner water, reduced environmental pollution, and improved public health. The process is also sustainable and cost-effective, contributing to environmental conservation and resource efficiency.

Collaborations

This research is a collaborative effort among various experts from institutions such as the University of Calcutta, Babasaheb Bhimrao Ambedkar University, Koneru Lakshmaiah Education Foundation, Graphic Era Deemed to be University, Indian Institute of Technology Kharagpur, Ranchi University, King Abdulaziz University, Incheon National University, Korea Aerospace University, and B.S. Abdur Rahman Crescent Institute of Science and Technology.

Future Research Plans

Dr Debajyoti’s future research projects include exploring advanced modification techniques for algae biochar to further enhance its pollutant removal capabilities, investigating its application in different types of wastewater, and developing large-scale production methods. Additionally, there is an interest in studying the long-term environmental impacts and economic viability of using algae biochar in wastewater treatment.

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