Oluwaseun Duntoye | Energy Technologies | Research Excellence Award

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Mr. Oluwaseun Duntoye | Energy Technologies | Research Excellence Award

Research Assistant | Seoul National University of Science | South Korea

Mr. Seun Duntoye is a power systems and AI-based control researcher specializing in intelligent modeling, optimization, and data-driven control of modern energy networks. His research spans smart grids, renewable energy integration, HVDC systems, energy storage, electric vehicles, and power electronics, with a strong emphasis on deep learning–based forecasting and hardware-efficient control strategies for resilient and sustainable power systems. His scholarly outputs include journal articles, conference papers, and technical studies indexed across Google Scholar and Scopus databases, demonstrating measurable research impact through documented citations, growing publication counts, and an emerging h-index. His work reflects strong potential for high-impact doctoral research and recognition in energy systems and AI-driven power engineering award categories.

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                     🟦 Citations    🟥 i10-index    🟩 h-index

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Featured Publications

Dr. Fan Zhang | Energy Technologies | Best Researcher Award

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Dr. Fan Zhang | Energy Technologies | Best Researcher Award

Research Associate | Queensland University of Technology | Australia

Dr. Fan Zhang is a distinguished researcher at the Queensland University of Technology whose work focuses on the advancement of next-generation aqueous zinc-ion batteries and sustainable energy storage technologies. Their research integrates bioinspired materials design, electrolyte optimization, and interfacial engineering to address key challenges such as dendrite formation, hydrogen evolution, and low reversibility in Zn-based systems. With significant contributions to materials science and electrochemistry, Dr. Zhang has established a strong reputation for innovative approaches that enhance the safety, energy density, and long-term stability of aqueous batteries. Their studies combine experimental synthesis with advanced characterization techniques, leading to impactful findings published in high-impact journals such as Advanced Materials, Journal of the American Chemical Society, National Science Review, and Nano Energy. Dr. Zhang’s scholarly influence is evidenced by a Scopus citation count of 370 (h-index: 12, 17 documents) and a Google Scholar citation count of 352 (h-index: 11, i10-index: 11). Their research continues to drive progress in electrochemical energy storage, contributing to the global shift toward sustainable and environmentally friendly power solutions.

Profile

Scopus | Google Scholar

Featured Publications

Zhang, F., Liao, T., Liu, C., Peng, H., Luo, W., Yang, H., Yan, C., & Sun, Z. (2022). Biomineralization-inspired dendrite-free Zn-electrode for long-term stable aqueous Zn-ion battery. Nano Energy, 103, 107830.

Zhang, F., Liao, T., Peng, H., Xi, S., Qi, D. C., Micallef, A., Yan, C., Jiang, L., & Sun, Z. (2024). Outer sphere electron transfer enabling high-voltage aqueous electrolytes. Journal of the American Chemical Society, 146(15), 10812–10821.

Zhang, F., Liao, T., Qi, D. C., Wang, T., Xu, Y., Luo, W., Yan, C., Jiang, L., & Sun, Z. (2024). Zn-ion ultrafluidity via bioinspired ion channel for ultralong lifespan Zn-ion battery. National Science Review, 11(8), nwae199.

Zhang, F., Liao, T., Yan, C., & Sun, Z. (2024). Bioinspired designs in active metal-based batteries. Nano Research, 17(2), 587–601.

Zhang, F., Liao, T., Zhou, Q., Bai, J., Li, X., & Sun, Z. (2025). Advancements in ion regulation strategies for enhancing the performance of aqueous Zn-ion batteries. Materials Science and Engineering: R: Reports, 165, 101012.

Aymen saad | Energy Technologies | Best Researcher Award

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Mr. Aymen saad | Energy Technologies | Best Researcher Award

Lecturer | University of Technology Malaysia | Iraq

Mr. Aymen Saad is a dedicated academic and researcher in the field of computer and microelectronic systems engineering. He has established himself as an experienced lecturer at Al-Furat Al-Awsat Technical University, Kufa Management Technical College, where he has been contributing to education and research for many years. His work bridges theory and practice, with a strong interest in artificial intelligence and advanced computing systems. Alongside his teaching responsibilities, he has developed a reputation for impactful research, particularly in deep learning, machine learning, and biomedical image analysis, while maintaining a strong presence in international research communities.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Mr. Aymen Saad began his academic journey by earning a bachelor’s degree in computer science from the Islamic University of Iraq. He later advanced his knowledge through a master’s degree in computer and microelectronics systems engineering at University of Technology Malaysia. Building on this foundation, he is currently pursuing his doctoral studies at the same institution, focusing on advanced applications of artificial intelligence and deep learning in computer vision and signal processing. His academic progression reflects a clear commitment to developing both technical expertise and research excellence in applied computer science and engineering fields.

Professional Experience

Mr. Aymen saad has served as a lecturer at Al-Furat Al-Awsat Technical University in Iraq, teaching within the Department of Information Technology Management. His professional career extends beyond teaching, as he actively engages in academic research and publications in reputed international outlets. With significant contributions in artificial intelligence applications, he has collaborated with researchers worldwide, presenting in conferences and publishing in peer-reviewed journals. He has also developed practical frameworks for disease detection, image enhancement, and pattern recognition, demonstrating the applied relevance of his work in solving modern engineering and healthcare challenges.

Awards and Honors

Throughout his academic journey, Mr. Aymen Saad has been recognized for his research contributions and teaching excellence. His growing h-index reflects the impact of his work in artificial intelligence and computer vision. His involvement in international conferences has earned him scholarly visibility and recognition, while his consistent publishing record in leading indexed journals highlights his dedication to advancing research in his field. Additionally, his professional profiles across platforms such as Google Scholar, ResearchGate, and Scopus emphasize his active participation and acknowledgment within the global academic community.

Research Focus

Mr. Aymen saad ’s research focuses on artificial intelligence, deep learning, and computer vision with applications across healthcare, security, and engineering systems. His studies span image and video processing, pattern recognition, optical character recognition, and medical image classification. He has contributed significantly to the development of robust models for cancer detection, COVID diagnostics, and brain tumor classification, as well as innovations in license plate recognition and fire detection. His current and future work aims to explore hybrid intelligent systems, bio-inspired algorithms, and advanced deep learning frameworks for solving real-world problems with greater efficiency and accuracy.

Publication Top Notes

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model
Published Year: 2021
Citation: 56

Classification of Bird Sound Using High-and Low-Complexity Convolutional Neural Networks
Published Year: 2022
Citation: 42

An Optimized Deep Learning Approach for Robust Image Quality Classification
Published Year: 2023
Citation: 38

A Novel Deep Learning Approach for Brain Tumors Classification Using MRI Images
Published Year: 2023
Citation: 35

Automatic Vehicle License Plate Recognition Using Lightweight Deep Learning Approach
Published Year: 2023
Citation: 29

Conclusion

Mr. Aymen Saad is a skilled computer and microelectronic systems engineer, academic, and researcher with a strong background in artificial intelligence. His education, professional teaching experience, and extensive research portfolio reflect his dedication to both learning and sharing knowledge. With numerous publications, conference presentations, and ongoing projects, he continues to advance innovative solutions in medical diagnostics, intelligent systems, and computational modeling. His future research aspirations highlight his determination to contribute further to global knowledge in AI, ensuring that his work remains impactful in both academic and practical domains.

Dr. Qian Guo | Energy technologies | Best Researcher Award

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Dr. Qian Guo | Energy technologies | Best Researcher Award

Research Associate, University of Manchester, United Kingdom

Dr. Qian Guo is a dynamic researcher in the field of materials science and condensed matter physics, currently serving as a Postdoctoral Research Associate in the Department of Physics and Astronomy at the University of Manchester, UK. With a rich academic and professional background, she has made substantial contributions to solar-to-hydrogen conversion, 2D material ion intercalation, and energy devices. Her interdisciplinary work across physics, materials engineering, and sustainable energy technologies has earned her recognition at international levels. 🔬⚛️

Professional Profile

Google Scholar

ORCID

Scopus

🎓 Education Background

Dr. Guo holds a Ph.D. in Materials Science (2022) from Queen Mary University of London, where she specialized in photoelectrocatalysis for solar-to-hydrogen conversion under the supervision of Prof. Ana Sobrido and Prof. Magda Titirici. She obtained her Master of Engineering in Materials Engineering (2016) from the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, with a GPA of 90.9/100. Her academic journey reflects a strong foundation in both theoretical and applied materials science. 📘🎓

💼 Professional Experience

Since September 2022, Dr. Guo has been contributing to cutting-edge research on rechargeable ion batteries at the University of Manchester under Prof. Irina Grigorieva. Prior to that, she was a visiting Ph.D. student at Imperial College London, investigating biomass-derived carbon materials. Her earlier research includes innovative work on nanocomposites for electronic packaging during her master’s studies. Her collaborations span leading UK, European, and Chinese institutions, showcasing her strong international profile. 🌍🔧

🏅 Awards and Honors

Dr. Guo has received numerous prestigious awards, including the RSC Researcher Development Grant (2022), SuperSolar Conference Fund Award (2022), and SEMS Postgraduate Research Excellence Award (2021). She also earned the National Scholarship from the Ministry of Education of China (2015) and was recognized as an Outstanding Student for three consecutive years by the University of Chinese Academy of Sciences. Her accolades reflect her academic excellence and research innovation. 🥇📜

🔍 Research Focus

Her research centers on advanced functional materials for energy applications, particularly in photoelectrocatalysis, energy storage, and interfacial engineering of nanomaterials. Her expertise includes the design of novel materials for solar water splitting, carbon-based electrocatalysts, and graphene-based batteries. Dr. Guo’s interdisciplinary approach bridges material chemistry, physics, and engineering, driving forward next-generation clean energy technologies. ⚡🧫

📌 Conclusion

Dr. Qian Guo is an emerging leader in sustainable energy and materials research, known for her scientific rigor, innovation, and international collaborations. Her continued work in the field of rechargeable batteries and photoelectrocatalysis marks her as a vital contributor to the advancement of green energy technologies. 🌱🔋

📚 Top Notable Publications

  1. Single atom Ir on hematite photoanodes for solar water oxidation: catalyst or spectator?
    Journal of the American Chemical Society, 2023, 145(3), 1686–1695.
    ➤ Cited by 35+ articles.

  2. In-plane staging in lithium-ion intercalation of bilayer graphene
    Nature Communications, 2024, 15, 6933.
    ➤ Cited by 10+ articles.

  3. The role of carbon dots – derived underlayer in hematite photoanodes
    Nanoscale, 2020, 12, 20220–20229.
    ➤ Cited by 60+ articles.

  4. Carbon Dots in Solar to Hydrogen Conversion
    Trends in Chemistry, 2020, 2(7), 620–637.
    ➤ Cited by 85+ articles.

  5. Porous carbon nanosheets from biological nucleobase precursor as efficient pH-independent oxygen reduction electrocatalyst
    Carbon, 2020, 156, 179–186.
    ➤ Cited by 45+ articles.

  6. Study on the effects of interfacial interaction on the rheological and thermal performance of silica nanoparticles reinforced epoxy nanocomposites
    Composites Part B: Engineering, 2022, 245, 110214.
    ➤ Cited by 25+ articles.

  7. Photoelectrochemical imaging system with high spatiotemporal resolution for visualizing dynamic cellular responses
    Biosensors and Bioelectronics, 2021, 180, 113121.
    ➤ Cited by 40+ articles.

  8. High concentration Ti3+ in porous carbon‐doped TiO2 nanosheets for photocatalytic ammonia synthesis
    Advanced Materials, 2021, 33, 2008180.
    ➤ Cited by 50+ articles.

  9. Photoelectrochemical detection of calcium ions based on hematite nanorod sensors
    ACS Applied Nano Materials, 2022, 5, 17087–17094.
    ➤ Cited by 20+ articles.

  10. Label‑free imaging of cell apoptosis by a light‑addressable electrochemical sensor
    Analytical Chemistry, 2023, 95(23), 8898‑8905.
    ➤ Cited by 15+ articles.

 

Dr. Caixin Yan | Power System Optimization | Best Researcher Award

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Dr. Caixin Yan | Power System Optimization | Best Researcher Award

PhD Student, Central South University, China

Dr. Caixin Yan is a distinguished researcher at the National Engineering Research Centre of Advanced Energy Storage Materials in Changsha, China. With a deep passion for energy systems and artificial intelligence applications in power grids, Dr. Yan has contributed significantly to the field of energy optimization and power market strategies. His expertise in reinforcement learning and grid stability has made him a prominent figure in the domain of advanced energy storage and smart grid technologies.

Publication Profile

ORCID

🎓 Education:

Dr. Yan pursued his higher education in automation and electrical engineering, focusing on intelligent power grid management and optimization. His academic journey has equipped him with extensive knowledge in multi-energy systems, deep reinforcement learning, and industrial load flexibility.

💼 Experience:

Currently associated with the National Engineering Research Centre of Advanced Energy Storage Materials, Dr. Yan has also collaborated with institutions like the School of Automation at Central South University and the Hunan Xiangjiang Artificial Intelligence Academy. His research focuses on optimizing power systems through artificial intelligence and developing cutting-edge solutions for market-based power regulation.

🏆 Awards and Honors:

While specific awards and honors are not listed, Dr. Yan’s impactful contributions to energy storage, power market strategies, and reinforcement learning applications have been recognized through his publications and collaborations. His research is gaining traction, as evidenced by his growing citation count.

🔍 Research Focus:

Dr. Yan’s research revolves around power grid optimization, energy storage integration, and AI-driven solutions for smart grids. His work on hierarchical reinforcement learning for power grid topology regulation and multi-energy systems operation strategies has been instrumental in advancing the field of intelligent energy management.

🔚 Conclusion:

Dr. Caixin Yan is a rising expert in energy storage and AI-driven power grid optimization. His contributions to power market strategies, reinforcement learning applications, and energy system integration are paving the way for a smarter and more efficient electricity landscape. With growing recognition and impactful research, he continues to make significant strides in the field of intelligent energy solutions. 🚀

📚 Publications :

Review of Power Market Optimization Strategies Based on Industrial Load Flexibility – Analyzing the role of industrial flexibility in power markets.

Power Grid Topology Regulation Method Based on Hierarchical Reinforcement Learning – Exploring AI-driven strategies for grid topology adjustments.

Deep Reinforcement Learning for Strategic Bidding in Incomplete Information Market – Applying AI to strategic bidding in uncertain energy markets.

Optimal Operation Strategies of Multi-Energy Systems Integrated with Liquid Air Energy Storage Using Information Gap Decision Theory – Investigating operational strategies for multi-energy systems.

Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers – Developing control mechanisms for PV-integrated power systems.