Prof. Dr. Jinju Sun | Computational Methods | Best Researcher Award

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Prof. Dr. Jinju Sun | Computational Methods | Best Researcher Award

Professor | Xi'an Jiaotong University | China

Prof. Dr. Jinju Sun is a distinguished scholar in the School of Energy and Power Engineering at Xi’an Jiaotong University, renowned for her pioneering contributions to fluid mechanics, turbomachinery, and multiphase flow systems through advanced Computational Methods. Her educational journey spans cryogenic engineering to a PhD in turbomachinery and engineering mechanics, which laid the foundation for her expertise in Computational Methods applied to turbomachinery optimization, Lattice Boltzmann modeling, and Vortex Method simulations. Throughout her professional career, she has served as a researcher, lecturer, and professor, advancing research through numerous national and international collaborations emphasizing Computational Methods in fluid dynamics and green energy system design. She has received prestigious honors, including the Donald Julius Groen Prize and the Arthur Charles Main Award from the Institution of Mechanical Engineers (UK), in recognition of her outstanding achievements utilizing Computational Methods for energy system modeling and flow optimization. Her research interests include cryogenic liquid turbines, compressor instabilities, and innovative Computational Methods for fluid-structure interaction and multiphase flow behavior. She has authored numerous high-impact publications and holds multiple international patents that demonstrate her excellence in Computational Methods-based innovation. Prof. Dr. Jinju Sun’s research skills encompass CFD modeling, LBM, topology optimization, and Computational Methods-driven analysis for turbomachinery and green energy systems. In conclusion, her dedication to advancing Computational Methods in engineering has positioned her as a global leader driving innovation, sustainability, and scientific excellence in modern energy and power engineering.

Profile: ORCID

Featured Publications

1. Qu, Y., Sun, J., Song, P., & Wang, J. (2025). Enhancing efficiency and economic viability in Rectisol system with cryogenic liquid expander. Asia-Pacific Journal of Chemical Engineering.

2. Ge, Y., Peng, J., Chen, F., Liu, L., Zhang, W., Liu, W., & Sun, J. (2023). Performance analysis of a novel small-scale radial turbine with adjustable nozzle for ocean thermal energy conversion. AIP Advances.

3. Fu, X., & Sun, J. (2023). Three-dimensional color-gradient lattice Boltzmann model for simulating droplet ringlike migration under an omnidirectional thermal gradient. International Journal of Thermal Sciences.

4. Song, P., Sun, J., Wang, S., & Wang, X. (2022). Multipoint design optimization of a radial-outflow turbine for Kalina cycle system considering flexible operating conditions and variable ammonia-water mass fraction. Energies.

5. Song, P., Wang, S., & Sun, J. (2022). Numerical investigation and performance enhancement by means of geometric sensitivity analysis and parametric tuning of a radial-outflow high-pressure oil–gas turbine. Energies.

Sathya Arumugam Thirumalai | Computational Methods | Young Scientist Award

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Mr. Sathya Arumugam Thirumalai | Computational Methods | Young Scientist Award

Mr. Sathya Arumugam Thirumalai | Indian Institute of Technology Roorkee | India

Mr. Sathya Arumugam Thirumalai is a highly motivated researcher whose work integrates Computational Methods with experimental nanomaterial science, emphasizing sustainability, environmental protection, and advanced detection technologies. His academic journey, from IIT Roorkee to TU Dresden, reflects an enduring commitment to merging experimental nanotechnology with Computational Methods for the synthesis and characterization of perovskite, MXene, and 2D materials. Mr. Sathya’s professional experience spans renowned institutions like IISc Bengaluru, BARC Mumbai, and IIT Roorkee, where he utilized Computational Methods in density functional theory (DFT) simulations, material modeling, and radiation detector design. His research, grounded in Computational Methods, has contributed to multiple journal publications addressing gas sensing, field emission, and radiation detection. He applies Computational Methods to optimize nanomaterial performance, enhance photonic properties, and improve the efficiency of radiation detectors. Recognized with several awards and fellowships, including the National Talent Search Fellowship and the Saxon Student Mobility Grant, he has demonstrated excellence in both theoretical and practical domains. His technical mastery extends to Python, MATLAB, COMSOL, and VASP, emphasizing his strength in applying Computational Methods across interdisciplinary fields. Mr. Sathya’s skill in Computational Methods enables him to bridge theoretical simulations with experimental validation, ensuring scientific precision and innovation. His collaborative engagements with global research groups highlight his leadership and cross-disciplinary adaptability. In conclusion, Mr. Sathya exemplifies how Computational Methods can revolutionize material science, fostering technological advancements that align with sustainability and human welfare.

Profiles: Google Scholar | ORCID

Featured Publications

1. Sathya, A. T., Jethawa, U., Sarkar, S. G., & Chakraborty, B. (2025). Pd-decorated MoSi₂N₄ monolayer: Enhanced nitrobenzene sensing through DFT perspective. Journal of Molecular Liquids, 427, 127310.

2. Sathya, A. T., Kandasamy, M., & Chakraborty, B. (2024). Strain induced nitrobenzene sensing performance of MoSi₂N₄ monolayer: Investigation from density functional theory. Surfaces and Interfaces, 55, 105386.

3. Sanyal, G., Vaidyanathan, A., Sathya, A. T., & Chakraborty, B. (2025). Efficient catechol sensing in newly synthesized 2D material Ti₂B MBene: Insights from density functional theory simulations. Langmuir, 41(33), 22525–22534.

4. Sathya, A. T., Sarkar, S. G., Bakhtsingh, R. I., & Mondal, J. (2024). Suppression of shielding effect of large area field emitter cathode in radio frequency gun environment. Physica Scripta, 99(12), 125301.

Dr. Yu Peng | Artificial Neural Networks | Best Researcher Award

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Dr. Yu Peng | Artificial Neural Networks | Best Researcher Award

Associate Research Fellow | East China University of Science and Technology | China

Dr. Yu Peng, Associate Research Fellow at the School of Materials Science and Engineering, East China University of Science and Technology, has built an impressive academic and research career with a strong focus on materials chemistry and energy-related applications, where Artificial Neural Networks play a pivotal role in his innovative work. With a B.Sc. in Pharmacy from Nanchang University and a Ph.D. in Physical Chemistry under joint supervision at ShanghaiTech University and the Chinese Academy of Sciences, he deepened his expertise before advancing as a Post-doctor and then securing his current academic position. Dr. Yu Peng’s professional experience demonstrates his dedication to exploring polar and ferroelectric materials, two-dimensional photoelectric materials, photocatalytic hydrogen production, and photocatalytic biomass conversion, where Artificial Neural Networks are consistently applied to enhance predictive modeling, structural optimization, and performance analysis. His research has earned prestigious awards such as the Carbon Future Young Investigator Honorable Mention Award and multiple national scholarships, reflecting his outstanding contributions to science. Widely published in leading journals, his work bridges experimental material synthesis with Artificial Neural Networks modeling to design high-performance perovskites and hybrid semiconductors. His research skills encompass advanced materials characterization, computational simulations, and integration of Artificial Neural Networks in analyzing photoelectric and catalytic behaviors. Dr. Yu Peng’s career reflects a fusion of theoretical insight and practical applications, and his achievements in Artificial Neural Networks demonstrate his commitment to driving forward sustainable energy materials, innovative photocatalysts, and advanced optoelectronic devices, making him a recognized contributor to the global research community.

Profile: ORCID

Featured Publications

1. Zhang, J., Zhang, Y., Peng, Y., Wang, M. M., Zhu, Y., Wang, X., Tang, Y. Y., Ding, P. C., Liu, P. F., & Yang, H. G. (2025). Template-free synthesis of single-crystal SrTiO₃ nanocages for photocatalytic overall water splitting. Chemical Communications.

2. Peng, Y., Li, L., Xu, Y., Wang, X., & Hou, Y. (2025). Two-dimensional multilayered ferroelectric with polarization-boosted photocatalytic hydrogen evolution. Catalysts.

3. Peng, Y., Zhang, Y., Wang, X., Sui, X. Y., Lin, M. Y., Zhu, Y., Jing, C., Yuan, H. Y., Yang, S., Liu, P. F., et al. (2024). Polar aromatic two-dimensional Dion–Jacobson halide perovskites for efficient photocatalytic H₂ evolution. Angewandte Chemie.

4. Peng, Y., Zhang, Y., Wang, X., Sui, X. Y., Lin, M. Y., Zhu, Y., Jing, C., Yuan, H. Y., Yang, S., Liu, P. F., et al. (2024). Polar aromatic two-dimensional Dion–Jacobson halide perovskites for efficient photocatalytic H₂ evolution. Angewandte Chemie International Edition.

5. Liu, D., Zheng, Y., Sui, X. Y., Wu, X. F., Zou, C., Peng, Y., Liu, X., Lin, M., Wei, Z., Zhou, H., et al. (2024). Universal growth of perovskite thin monocrystals from high solute flux for sensitive self-driven X-ray detection. Nature Communications.