Assoc. Prof. Dr. Sagar D Patil | Composite Materials | Editorial Board Member

Associate Professor | Sharad Institute of Technology College of Engineering Yadrav | India

Assox. Prof. Dr. Sagar Dnyandev Patil is an Associate Professor of Mechanical Engineering at Sharad Institute of Technology College of Engineering, recognized for his contributions to composite materials, finite element analysis, optimization methods, and advanced manufacturing. His work integrates experimental mechanics with computational modeling to enhance material performance and structural design. With more than a decade of academic and research experience, he has extensively investigated composite structural behavior, nano-enhanced materials, hybrid composites, and the optimization of mechanical systems. He has supervised numerous student projects, collaborated with industry on practical engineering challenges, and published several impactful studies in reputed journals. His research interests span composite materials, FEA, nano-enhanced phase change materials, structural integrity assessment, and optimization techniques such as Taguchi and GRA. He has been recognized for his contributions to hybrid composite development and for advancing innovative material solutions, establishing himself as a promising researcher in mechanical engineering.

Featured Publications

Husainy, M., Patil, S. D., & Others. (2024). Heat transfer phenomenon of NEPCM incorporated in refrigeration test rig. ES Energy & Environment. (Cited by 25)

Patil, S. D., Bhalerao, Y. J., & Others. (2023). Design parameters influencing tensile strength of composite layers using Taguchi. Materials Today: Proceedings. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2020). Multi-objective optimization of carbon/glass hybrids with NDR. Multidiscipline Modeling in Materials and Structures. (Cited by 10)

Patil, S. D., & Bhalerao, Y. J. (2019). Optimization of dynamic properties of hybrid composite shaft. International Journal of Structural Integrity. (Cited by 10)

Patil, S. D., Bhalerao, Y. J., & Others. (2012). Composite torsion shaft buckling analysis using FEA. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE). (Cited by 9)

Dr. Thabang Somo | Materials Science | Best Researcher Award

Senior Researcher at University of Limpopo | South Africa

Thabang Ronny Somo is a highly skilled scientific researcher whose work intersects advanced computational chemistry, data science, and applied experimentation. Through rigorous academic training culminating in doctoral-level expertise, Thabang has developed exceptional proficiency in chemical synthesis, lab instrumentation, safe laboratory protocols, and scientific communication. His academic journey has been enriched by numerous online certifications, enhancing his competency in analytical tools and platforms and empowering a seamless blend of scientific and data-driven methodologies.

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Education Details

His educational background encompasses a comprehensive progression through physical sciences and chemistry disciplines, encompassing both theoretical foundations and hands-on laboratory work. At the graduate level, he immersed himself in rigorous coursework, experimental techniques, and data‐centric research, applying computational approaches to chemical problems. Throughout his studies, he engaged deeply with advanced analytical concepts, established scientific methods, and cultivated a strong foundation in both theoretical and applied research.

Professional Experience

As a dedicated Data and Image Scientist in both industrial and research settings, Thabang has led multifaceted projects that harness machine learning, database architecture, and image analysis. In the mining sector, he crafted predictive algorithms and integrated custom analytical tools into user-friendly interfaces to aid technical decision-making. He also engineered sophisticated image analysis systems that enhanced mineral characterization, while managing complex multi-source datasets to inform operational insights. In the fintech domain, he spearheaded end-to-end modeling for digital platform pricing, constructed forecasting frameworks to guide enterprise fee strategies, and maintained robust ETL pipelines to underpin accurate data integration and commercial pricing operations. His scientific research roles saw him design and manage interdisciplinary projects in hydrogen storage, oversee data structuring and preprocessing, implement predictive modeling, and perform simulations and modeling of green hydrogen processes.

Research Interests

His research is driven by a deep interest in applying computational and data-centric strategies to complex scientific challenges. He is passionate about designing predictive and machine learning models tailored to materials discovery and mineral analysis. Likewise, he is committed to developing visualization and algorithmic tools that translate intricate data into actionable insights across chemistry, energy materials, and industrial analytics.

Awards and Honors

While specific accolades are not listed here, Thabang’s rigorous academic achievements, progressive leadership roles, and continual upskilling through technical certifications reflect a clear recognition of his dedication, excellence, and professional growth by academic and industry peers alike.

Publication Top Notes

Theoretical hydrogen storage properties of high entropy alloys: A combined DFT and machine learning approach. Materials Today Communications.

Thermodynamic and elastic properties of laves phase AB₂-based alloys and their hydrides: A density functional theory (DFT) study. Materials Chemistry and Physics.

Highlighting the importance of characterization techniques employed in adsorption using metal–organic frameworks for water treatment. Polymers.

A comparative review of metal oxide surface coatings on three families of cathode materials for lithium ion batteries. Coatings.

Review on the effect of metal oxides as surface coatings on hydrogen storage properties of porous and non-porous materials. Chemical Papers.

Conclusions

Thabang Ronny Somo represents a synergy of scientific rigor and data science agility. He combines deep domain knowledge in chemistry and materials science with advanced analytical and modeling techniques, empowering impactful innovation across both research and commercial spheres. His blend of technical acumen, leadership ability, and cross-disciplinary fluency positions him strongly for projects at the intersection of science, data, and strategic decision-making.