Rodouan Touti | Computational Modeling | Research Excellence Award

Prof. Dr. Rodouan Touti | Computational Modeling | Research Excellence Award

Faculty of sciences Dhar El Mahraz, University Sidi Mohamed Ben Abdellah | Morocco

Touti Rodouan is a physicist whose research spans radiation protection, medical physics, and computational materials science. His work focuses on dosimetry and assessment of radiation doses resulting from ingestion, inhalation, and topical application of radioactive substances, using solid-state nuclear track detectors such as CR-39 and LR-115. In parallel, he applies density functional theory (DFT) to investigate the structural, electronic, elastic, and optical properties of advanced materials, particularly lead-free perovskites for energy storage, optoelectronic, and photovoltaic applications. His research integrates experimental radiation measurements with first-principles modeling to address health, environmental, and sustainable energy challenges.

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Featured Publications

Pratik Sarkar | Computational Chemistry | Research Excellence Award

Mr. Pratik Sarkar | Computational Chemistry | Research Excellence Award

IIT Kharagpur | India

Pratik Sarkar is a Ph.D. research scholar in Computational Chemistry at the Indian Institute of Technology Kharagpur, focusing on theoretical modeling of molecular and nanoscale systems using advanced quantum chemical methods. His research employs density functional theory (DFT) and high-level post–Hartree–Fock approaches, including CCSD(T) and DLPNO-CCSD(T), to investigate electronic structure, bonding, stability, and reactivity. A major theme of his work is the computational design of platinum-free nanocluster and nanoalloy catalysts based on boron, aluminum, and gallium for the oxygen reduction reaction, contributing to sustainable electrocatalysis. He also studies lithium–sulfur clusters to elucidate polysulfide intermediates and lithiation mechanisms relevant to next-generation battery technologies. In parallel, his research explores unconventional carbon chemistry, including planar pentacoordinate and hypercoordinate carbon motifs. By applying bonding and aromaticity analyses such as QTAIM, NICS, and AdNDP, he provides deep insights into multicenter bonding and electron delocalization. His work is published in leading physical chemistry journals and presented at national and international conferences.

Citation Metrics (Scopus)

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Sudipta Dash | Computational Chemistry | Best Researcher Award

Mr. Sudipta Dash | Computational Chemistry | Best Researcher Award

Kalinga Institute of Social sciences | India

Mr. Sudipta Dash is a scholar and academic leader specializing in Applied Physics, with research emphases on quantum optoelectronic materials, carbon‑based nanostructures, functional perovskites, and intelligent instrumentation using IoT and AI technologies. After completing his M.Sc. from Ravenshaw University, he pursued teacher education at Acharya Nagarjuna University, earning his B.Ed and M.Ed. Professionally, Mr. Dash’s career spans roles in higher education and administration: he served as Principal at Gayatri Degree College, Lecturer and then Assistant Professor at Kalinga Institute of Social Sciences, and as of 2024, he is Head of the Department there. He has been recognized with several honours, including CSIR‑NET (2019), GATE (2018), and multiple Best Poster Awards. His inventive work is evidenced by patents in areas like anti‑dandruff/anti‑ripening shampoos; carbon quantum dots; AI‑based digital education methods; and outcome‑based assessment aligned with NEP 2020. His publication record includes studies on perovskite band gap engineering, optoelectronic properties of lead‑free compounds, toxicity assessment of nanomaterials, among others.

Profiles : Scopus | Google Scholar

Featured Publications

  • Dash, S., Behera, D., Mohanty, S. K., Palai, G., & Mohanty, I. (2024). Unveiling the potential of lead‑free KInBr₃ and RbInBr₃ perovskites: A breakthrough in optoelectronic and photovoltaic performance through DFT (HSE hybrid functional) and SCAPS‑1D simulations. Phase Transitions, 97(11‑12), 826–845.

  • Dash, S., Mohanty, S., & Palai, G. (2025). First‑Principles Insights into Structural, Electronic, Elastic, and Optical Behavior of AlGeX₃ (X = Cl, Br) Perovskites. Russian Journal of Inorganic Chemistry, 1–9.

  • Dash, S., Behera, D., Mohanty, S., Panda, J., & Palai, G. (2025). Comprehensive investigations on the optoelectronic properties of lead‑free K₂InSbCl₆ compound. Next Research, Article 100607.

  • Dash, S., Mohanty, S., Behera, D., Mohanty, S. K., & Palai, G. (2025). Band gap engineering and optical response of SrSiO₃ perovskite for high‑efficiency photonic applications. MRS Advances, 1–8.

 

Arunmozhi Bharathi Achudhan | Computational Chemistry | Best Scholar Award

Mr. Arunmozhi Bharathi Achudhan | Computational Chemistry |  Best Scholar Award

SRM Institute of Science and Technology, India

👨‍🎓Profiles

🧬 Academic and Research Background

Mr. Arunmozhi Bharathi Achudhan is a dynamic early-career researcher specializing in environmental genomics and microbiome analysis, currently pursuing his Ph.D. at SRM Institute of Science and Technology, Chennai, with his thesis submitted for review. His doctoral research, titled “Structural and Functional Genomic Analysis of Coal Microbiome and Machine Learning-Based Recovery of Novel Metagenome-Assembled Genomes (MAGs),” integrates next-generation sequencing (NGS) data analysis with computational biology and machine learning. Originally trained in microbiology, he has independently mastered advanced genomics techniques and computational pipelines, using over 150 workflows to deeply explore microbial communities in extreme environments.

🧪 Research Focus and Innovations

His research focuses on unculturable microbial genomes, recovering novel MAGs from coal microbiomes and evaluating their ecological and industrial significance. By analyzing structural diversity and mining functional genes, he has identified and validated novel enzymes like amidase and nitrilase through comparative structure analysis with protein crystal structures available in the Protein Data Bank. These findings carry high biotechnological potential for industrial applications. He has also developed a read-specific binning strategy for improved MAG reconstruction, now filed as a patent, which addresses limitations in existing metagenomics workflows.

💻 Technical and Computational Skills

Mr. Arunmozhi is adept at working in Linux environments and cloud platforms like AWS. He is highly skilled in coding with Python, R, and Perl, and proficient in setting up automated workflows using Snakemake and Conda. His experience includes using powerful metagenomic and proteomic analysis pipelines such as metaWRAP, MuDoGer, SqueezeMeta, Traitar, GTDB-tk, Prokka, Prodigal, AlphaFold, and Gromacs. He also utilizes AutoDockVina, PyMOL, and Schrödinger Suite for molecular docking and structure analysis. His strong command over bioinformatics tools bridges the gap between raw sequencing data and functional biological insights.

🧫 Laboratory Training and Teaching

In addition to his computational expertise, he has received formal training in Microbiology and Molecular Biology techniques, including GLP, and has instructed undergraduate laboratory courses in these areas. He also taught Computational Biology and delivered academic training in research methods, APA formatting, SPSS, and Sigma Plot, equipping students with both theoretical knowledge and practical scientific skills.

🎓 Education and Early Scientific Engagement

Mr. Arunmozhi holds a Master’s degree in Applied Microbiology and a Bachelor’s degree in Microbiology from Madras Christian College, where he also completed a Postgraduate Diploma in Medical Laboratory Technology. His postgraduate research included the molecular characterization of β-agarase from Sphingomonas paucimobilis, and he actively participated in international and national conferences, presenting research on larvicidal activity, microbial virulence, and antimicrobial agents.

🌍 Broader Contributions to Microbiome Science

His independent project on the Indian healthy human gut microbiome involved shotgun metagenomic analysis of 110 samples, where he employed language-based machine learning to identify hidden probiotic genomes. This study provided new insights into unculturable probiotic microbes and showcased how integrating metagenomics with machine learning can revolutionize microbiome research and gut health diagnostics.

🏅 Awards and Recognition

His academic contributions have been recognized through multiple oral presentation awards, including the Best Oral Presentation at the International Conference on Advances and Applications of Biotechnology (2024), and the Young Scientist Award at the International Conference on Innovation in Science and Technology for Sustainable Development (2023). He has also presented his work at several national and international platforms, including the National Conference on Structural Biology and Drug Discovery, and New Horizons in Bioengineering, among others.

🧠 Innovation and Intellectual Property

Mr. Arunmozhi is an innovator in the domain of metagenomic genome reconstruction. His patented approach, A Read-Specific Binning Strategy for the Recovery of Unculturable MAGs, improves the reliability and efficiency of genome assembly from complex microbial communities. This contribution represents a significant advancement in bioinformatics methodology, with applications across environmental microbiology, health, and industrial biotechnology.

🔮 Future Contributions and Vision

With a multidisciplinary skill set spanning microbiology, computational genomics, structural biology, and AI-driven analysis, Mr. Arunmozhi Bharathi Achudhan is poised to make impactful contributions to sustainable biotechnology, precision microbiome therapeutics, and enzyme discovery. He envisions continuing his journey in postdoctoral research, focused on unraveling complex microbial systems and translating genomic discoveries into real-world applications that benefit health and the environment.

📖Notable Publications

  1. A review on applications of β-glucosidase in food, brewery, pharmaceutical and cosmetic industries
    Contributors: P. Kannan, A.B. Achudhan, A. Gupta, L.M. Saleena
    Journal: Carbohydrate Research, 530, 108855
    Year: 2023

  2. Functional metagenomics uncovers nitrile-hydrolysing enzymes in a coal metagenome
    Contributors: A.B. Achudhan, P. Kannan, L.M. Saleena
    Journal: Frontiers in Molecular Biosciences, 10, 1123902
    Year: 2023

  3. A Review of web-based metagenomics platforms for analysing next-generation sequence data
    Contributors: A.B. Achudhan, P. Kannan, A. Gupta, L.M. Saleena
    Journal: Biochemical Genetics, 62(2), 621–632
    Year: 2024

  4. CRISPR detection in metagenome-assembled genomes (MAGs) of coal mine
    Contributors: A.B. Achudhan, P. Kannan, L.M. Saleena
    Journal: Functional & Integrative Genomics, 23(2), 122
    Year: 2023

Md Ibrahim Shikder Mahin | Computational Modeling | Best Researcher Award

Mr. Md Ibrahim Shikder Mahin | Computational Modeling | Best Researcher Award

Bangladesh University of Business and Technology (BUBT), Bangladesh

👨‍🎓Profiles

🎓 Academic Background

Md Ibrahim Shikder Mahin is a Bachelor of Science (B.S.) graduate in Electrical and Electronics Engineering from Bangladesh University of Business and Technology (BUBT), where he maintained a CGPA of 3.41/4.0 (2019-2024). His earlier academic journey includes:  Higher Secondary School Certificate (HSC) – Science from Government Shaheed Suhrawardy College (2017-2019), with a GPA of 3.75/5.0. Secondary School Certificate (SSC) – Science from Gandaria High School (2005-2017).

🚀 Passion for Technology & Innovation

As a passionate technophile, Mr. Mahin specializes in:
✔️ Computational Modeling & Image Processing 🖥️ – Applying advanced algorithms for visual data analysis.
✔️ Machine Learning & Deep Learning 🧠 – Developing intelligent systems for automation and decision-making.
✔️ Robotics & AI 🤖 – Exploring automation, smart systems, and industrial robotics.
✔️ Blockchain Technology 🔗 – Investigating decentralized applications and cryptographic security.

His strong foundation in Python programming allows him to implement innovative AI and image processing solutions, contributing to cutting-edge research and real-world applications.

📊 Technical Expertise

Mr. Mahin has hands-on experience in various AI and computational technologies, including:
✔️ Programming Languages: Python, MATLAB
✔️ Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
✔️ Image Processing: OpenCV, Computer Vision Techniques
✔️ Robotics & AI: Embedded Systems, IoT Integration
✔️ Blockchain & Cryptography: Smart Contracts, Decentralized Systems

🎯 Research & Career Aspirations

Mr. Mahin is committed to driving innovation and fostering collaboration in the tech community. His future goals include:
🔹 Developing intelligent automation systems using AI & robotics.
🔹 Advancing deep learning applications for medical and industrial imaging.
🔹 Exploring the intersection of blockchain and AI for secure, decentralized solutions.
🔹 Contributing to open-source projects and global research communities.

🏆 Conclusion

Md Ibrahim Shikder Mahin is a highly motivated researcher and engineer in the fields of AI, deep learning, and computational modeling. His passion for technology, robotics, and blockchain continues to shape his journey toward innovation and impactful contributions in the digital era.

📖Notable Publication

Real-Time Rapid Accident Detection for Optimizing Road Safety in Bangladesh

Authors: Md Shamsul Arefin, Md Ibrahim Shikder Mahin, Farzana Akter Mily

Journal: Heliyon

Year: 2025