Jinzhao Li | Civil Engineering | Best Researcher Award

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Dr. Jinzhao Li | Civil Engineering | Best Researcher Award

Associated Researcher at Hunan University, China

Jinzhao Li is a dynamic researcher 🧠 specializing in intelligent construction within civil engineering, currently serving as an Associate Researcher at Hunan University. With a keen focus on AI-infused methods and hydrodynamics, Li has contributed extensively to national infrastructure projects, including the Hong Kong-Zhuhai-Macao Bridge 🌉. His academic collaborations span globally renowned institutions such as TU Denmark 🇩🇰, Delft University 🇳🇱, and the University of Tokyo 🇯🇵. With over 50 research publications—including one in Nature Communications Engineering—more than 10 patents, and numerous funded projects, his work blends deep learning, computer vision, and fluid mechanics. Recognized with awards like the Silver Medal 🥈 in the Hunan Postdoctoral Innovation Competition and the China Railway Society Science and Technology Prize, Li exemplifies scientific excellence. His dedication to smart infrastructure and sustainable construction makes him a pivotal contributor to future-ready civil engineering. 🚀

Professional Profile 

Scopus

🎓 Education

Jinzhao Li’s academic journey reflects a powerful blend of engineering rigor and global exposure 🌍. He earned his Bachelor’s degree in Traffic Engineering from Shandong University of Science and Technology (2007–2011), followed by a Ph.D. in Bridge and Tunnel Engineering at Beijing Jiaotong University (2011–2017), under an integrated Master–Ph.D. program. His doctoral work, under the mentorship of Professor Meilan Qi, provided the foundation for his specialization in bridge hydrodynamics and scour processes. To further internationalize his research acumen, he completed joint training at the Technical University of Denmark 🇩🇰, working closely with Professor David R. Fuhrman—an authority in applied ocean research. These cross-continental educational experiences deeply influenced his multi-disciplinary expertise in wave mechanics, AI applications in structural monitoring, and deep-learning physics modeling. 📚 His education solidified not only technical knowledge but also his global research mindset, setting the stage for a versatile, high-impact scientific career. 📖🧠

💼 Professional Experience

Jinzhao Li’s professional path traverses academia, research institutes, and high-impact engineering projects 🏗️. Starting as an Assistant Researcher at the Tianjin Research Institute of Water Transport Engineering (2018–2019), he contributed to national megaprojects like the Shenzhen-Zhongshan Passage and floating tunnel studies. His transition to academia led to a Lecturer role at Shandong University of Science and Technology (2019–2020), where he also served as Deputy Head of Department. Since 2020, he has been advancing frontier research as a Postdoctoral Fellow and now as an Associate Researcher at Hunan University. 🚀 Under collaborative mentorship with National Youth Thousand Talents Professor Xuan Kong, Li steers projects integrating AI, computer vision, and coastal engineering. His work reflects a harmonious blend of theory and fieldwork, evident in his involvement with hydrodynamic model testing, drone-based monitoring systems, and AI-driven structural health diagnostics. 📡 His professional versatility makes him a cornerstone in smart infrastructure R&D.

🧠 Research Interests

Jinzhao Li’s research interests are a fusion of artificial intelligence and civil engineering phenomena 💡. He pioneers in AI intelligent computing, integrating physics-driven deep learning to model real-world complexities such as wave-structure interaction and scour evolution. His work in computer vision-based flow measurement enables high-fidelity monitoring of structural dynamics, especially in disaster-prone flood zones 🌊. As part of his broader vision, he also delves into intelligent fluid dynamics, bridging fluid simulation with machine learning to advance structural resilience. His studies on bridge hydrodynamics and scouring have practical implications for coastal infrastructure safety, while his exploration of flood disaster monitoring employs drones and optical flow algorithms. 📹🔍 Blending neural networks with marine physics, Li pushes the boundary of what intelligent infrastructure can achieve. His focus aligns with smart, sustainable, and responsive design systems—a true intersection of digital intelligence and environmental engineering. 🌐

🏆 Awards and Honors

Jinzhao Li has earned prestigious accolades recognizing both his innovative spirit and technical prowess 🥇. Notably, he was selected for the “Hunan Province Outstanding Postdoctoral Innovation Talent Program”, affirming his place among China’s rising scientific leaders. He secured the Silver Award 🥈 in the First Hunan Postdoctoral Innovation and Entrepreneurship Competition, spotlighting his blend of applied and entrepreneurial science. His technical contributions were further recognized through the China Railway Society Science and Technology Second Prize, a high honor in engineering innovation. Internationally active, Li has served as Guest Editor for the SCI journal Sustainability and is a regular reviewer for top-tier SCI journals. His papers—some co-authored with world-class scientists—have garnered over 900 citations and an H-index of 16, confirming his scientific impact 📊. These honors echo his exceptional integration of AI, hydrodynamics, and vision-based civil engineering.

📚 Publications Top Note 

1. Physics‑preserved graph learning of differential equations for structural dynamics

Authors: (Not specified in search snippet)
Year: 2025
Citations: 0 (appears recent)
Source: Mechanical Systems and Signal Processing
Summary:
This study introduces a novel graph-based learning framework that incorporates the underlying partial differential equations (PDEs) governing structural dynamics directly into the model. By encoding displacement, velocity, and energy dissipation processes via conservation laws within a graph neural network, the model can predict structural responses while adhering to physical laws. It aims to combine data-driven flexibility with physics-based constraints for improved interpretability and generalization under dynamic loads. The approach shows promising accuracy on simulated structural dynamic scenarios.

2. Vehicle Response‑Based Bridge Modal Identification Using Different Time‑Frequency Analysis Methods

Authors: (Not specified in snippet)
Year: 2025
Citations: 5
Source: International Journal of Structural Stability and Dynamics
Summary:
This paper proposes a method leveraging a moving vehicle’s response to identify bridge modal frequencies and mode shapes. It combines Empirical Mode Decomposition (EMD) with advanced time–frequency analysis (e.g. wavelets) to isolate bridge signature from vehicle–track–bridge interactions. Field and simulation results show that this hybrid approach enhances modal identification performance, improving accuracy even amid road surface noise and vehicle dynamics.

3. Full‑field modal identification of cables based on subpixel edge detection and dual matching tracking method

Authors: Jinxin Yi, Xuan Kong, et al.
Year: 2025
Citations: 0
Source: Mechanical Systems and Signal Processing
Summary:
This research introduces a computer vision‑based framework for extracting full‑field cable modal properties in cable-stayed bridges. By applying subpixel edge detection via LSD (Line Segment Detector) on video footage, followed by a dual-matching tracking algorithm, the method captures dense dynamic displacement data. It then derives modal frequencies and employs frequency differences to compute cable tension, avoiding preset tuning parameters. Verified with laboratory and field tests, the approach is robust and accurate.

✅ Conclusion 

In summation, Jinzhao Li stands as a visionary in civil and computational engineering—a scientist bridging traditional hydrodynamics with cutting-edge artificial intelligence 🤖🌊. His career is marked by international collaborations, impactful research outputs, and real-world applications in infrastructure monitoring, disaster prediction, and intelligent design. From postdoctoral recognition in Hunan to Nature-Communications-level publications, his work exemplifies future-focused engineering with societal relevance. Whether optimizing bridge scour prediction through computer vision or leading drone-based flood warning systems, Li’s contributions embody the shift toward data-driven, smart construction ecosystems 🏗️📈. With more than 50 academic publications, 10+ patents, and a robust portfolio of funded research, he is a deserving candidate for elite research honors and fellowships. As AI and civil engineering continue to converge, Jinzhao Li is set to be a torchbearer of the next-generation engineering renaissance. 🌍🔬

Ehsan Adibnia | Engineering | Best Academic Researcher Award

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Dr. Ehsan Adibnia | Engineering | Best Academic Researcher Award

Dr. Ehsan Adibnia at University of Sistan and Baluchestan, Iran

Dr. Ehsan Adibnia 🎓 is a dedicated academic researcher in electrical engineering ⚡, specializing in cutting-edge fields such as artificial intelligence 🤖, machine learning 📊, deep learning 🧠, nanophotonics 💡, optics 🔬, and plasmonics ✨. He is proficient in Python 🐍, MATLAB 🧮, and Visual Basic, and utilizes simulation tools like Lumerical 📈, COMSOL 🧪, and RSoft 🔧 to drive innovative research. Fluent in English 🇬🇧 and Persian 🇮🇷, Dr. Adibnia contributes to academic conferences and peer-reviewed journals 📚. He is currently pursuing his Ph.D. and actively engaged in interdisciplinary scientific exploration 🌐.

Professional Profile:

Orcid

Scopus

Google Scholar

🔹 Education & Experience 

🎓 Ph.D. in Electrical Engineering – University of Sistan and Baluchestan, Zahedan, Iran (Expected 2025)
🎓 B.S. in Electrical Engineering – University of Sistan and Baluchestan, Zahedan, Iran (2014)
🧑‍💼 Executive Committee Member – 27th Iranian Conference on Optics and Photonics & 13th Conference on Photonic Engineering and Technology
🖋️ Assistant Editor – International Journal (Name not specified)
🔍 Researcher – Actively engaged in interdisciplinary AI & photonics research projects

🔹 Professional Development 

Dr. Ehsan Adibnia continually enhances his professional growth through active participation in conferences 🧑‍🏫, committee leadership 🗂️, and editorial work 📑. He develops algorithms and conducts simulations using advanced tools such as Lumerical 🔬, COMSOL 🧪, and RSoft 💻. His expertise in AI and photonics drives innovative research and collaboration 🌍. He also hones his programming skills in MATLAB 🧮, Python 🐍, and VBA 🧠, ensuring precision in modeling and data analysis. His hands-on knowledge in PLC systems 🤖 and industrial automation makes him versatile across both academic and applied research settings 🏭.

🔹 Research Focus 

Dr. Adibnia’s research focuses on the fusion of artificial intelligence 🤖 and photonics 💡. His work explores machine learning 📊, deep learning 🧠, nanophotonics 🔬, plasmonics ✨, optical switching 🔁, and slow light 🐢 technologies. He is particularly interested in leveraging these technologies in biosensors 🧫, metamaterials 🔷, and quantum optics ⚛️. Through simulation and algorithm development, he aims to optimize performance in optoelectronic and photonic systems 🔍. His interdisciplinary research bridges electrical engineering with physics and AI, creating advanced systems for diagnostics, sensing, and smart environments 🌐.

🔹 Awards & Honors 

🏅 Executive Committee Role – 27th Iranian Conference on Optics and Photonics
🏅 Executive Committee Role – 13th Iranian Conference on Photonic Engineering and Technology
📜 Assistant Editor – International scientific journal (name not specified)
🧠 Scopus-indexed Researcher – Scopus ID: 58485414000

Publication Top Notes

🔹 High-performance and compact photonic crystal channel drop filter using P-shaped ring resonator

  • Journal: Results in Optics

  • Date: Dec 2025

  • DOI: 10.1016/j.rio.2025.100817

  • Summary: Proposes a novel P-shaped ring resonator design for channel drop filters in photonic crystal structures. Focuses on achieving high performance in terms of compactness and spectral selectivity for integrated optical circuits.

🔹 Optimizing Few-Mode Erbium-Doped Fiber Amplifiers for high-capacity optical networks using a multi-objective optimization algorithm

  • Journal: Optical Fiber Technology

  • Date: Sep 2025

  • DOI: 10.1016/j.yofte.2025.104186

  • Summary: Introduces a multi-objective optimization approach for designing few-mode EDFAs, targeting performance improvements in next-gen high-capacity optical networks.

🔹 Inverse design of octagonal plasmonic structure for switching using deep learning

  • Journal: Results in Physics

  • Date: Apr 2025

  • DOI: 10.1016/j.rinp.2025.108197

  • Summary: Utilizes deep learning for the inverse design of an octagonal plasmonic structure used in optical switching, demonstrating enhanced precision and compact design capability.

🔹 Chirped apodized fiber Bragg gratings inverse design via deep learning

  • Journal: Optics & Laser Technology

  • Date: 2025

  • DOI: 10.1016/J.OPTLASTEC.2024.111766

  • WOS UID: WOS:001311493000001

  • Summary: Applies deep learning to the inverse design of chirped apodized fiber Bragg gratings, optimizing the spectral characteristics for filtering and sensing applications.

🔹 Inverse Design of FBG-Based Optical Filters Using Deep Learning: A Hybrid CNN-MLP Approach

  • Journal: Journal of Lightwave Technology

  • Date: 2025

  • DOI: 10.1109/JLT.2025.3534275

  • Summary: Proposes a hybrid CNN-MLP architecture to design fiber Bragg grating (FBG) optical filters, improving accuracy and speed in the inverse design process using deep learning techniques.

Conclusion

Dr. Adibnia is still in the process of completing his Ph.D., his broad technical expertise, multidisciplinary research focus, early academic leadership roles, and active participation in both national and international platforms make him a highly promising candidate for the Best Academic Researcher Award in the early-career researcher or emerging researcher category.

Mohsen Khatibinia | Structural Control | Best Researcher Award

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Assoc. Prof. Dr. Mohsen Khatibinia | Structural Control | Best Researcher Award

Civil Engineering at University of Birjand, Iran

Mohsen Khatibinia, Ph.D., is an Associate Professor of Civil Engineering at Shahid Bahonar University of Kerman, Iran. With a robust academic background, he has contributed significantly to the field of structural engineering, particularly in optimizing structures for earthquake resilience. His research integrates computational intelligence methods with advanced structural analysis, focusing on performance-based design and soil-structure interaction. Dr. Khatibinia is an experienced educator and a skilled user of advanced engineering software.

Professional Profile

Education

  • Ph.D. in Civil Engineering (Structural Engineering)
    Shahid Bahonar University of Kerman, Iran (2006–2013)
    Dissertation focused on reliability-based optimization of reinforced concrete structures with soil-structure interaction.
  • M.Sc. in Civil Engineering (Structural Engineering)
    Shahid Bahonar University of Kerman, Iran (2002–2004)
    Thesis: Optimal design of space structures using genetic algorithms.
  • B.Sc. in Civil Engineering
    University of Sistan and Baluchestan, Zahedan, Iran (1998–2002).

Professional Experience

Associate Professor, Shahid Bahonar University of Kerman, Iran
Mohsen Khatibinia has extensive academic experience, specializing in teaching undergraduate and graduate courses such as Structural Analysis, Theory of Elasticity, Advanced Engineering Mathematics, and Stability of Structures.

  • Expertise in computational tools including FORTRAN, MATLAB, SAP, ETABS, SAFE, Ansys, and OpenSees.
  • Proficient in developing optimization algorithms and conducting numerical simulations for structural engineering applications.

Research Interest

  • Structural optimization for earthquake-resistant design.
  • Soil-structure interaction and performance-based design.
  • Application of soft computing methods (e.g., genetic algorithms, particle swarm optimization, gravitational search algorithms, hybrid optimization algorithms, fuzzy logic, and neural networks) in structural and earthquake engineering.
  • Seismic reliability assessment and optimization of reinforced concrete and steel structures.

Author Metric

  • Published 12 peer-reviewed journal articles in prestigious journals such as Reliability Engineering & System Safety, Journal of Sound and Vibration, and Engineering Optimization.
  • Presented research at 16 international and national conferences, highlighting advancements in structural and earthquake engineering.
  • Expertise in developing hybrid computational models, with numerous citations in domains of structural optimization and earthquake engineering.

Publications Top Noted

1. Truss Optimization on Shape and Sizing with Frequency Constraints Based on Orthogonal Multi-Gravitational Search Algorithm

  • Authors: m. khatibinia, s.s. naseralavi
  • Journal: Journal of Sound and Vibration, Volume 333, Issue 24, Pages 6349-6369, 2014
  • Citations: 112
  • Abstract:
    This paper introduces an Orthogonal Multi-Gravitational Search Algorithm (OMGSA) for optimizing truss structures in terms of shape and size while satisfying frequency constraints. The approach integrates orthogonal learning and multi-agent search to enhance solution accuracy and convergence speed. The method’s effectiveness is demonstrated through benchmark problems and comparison with existing algorithms.

2. A Hybrid Approach Based on an Improved Gravitational Search Algorithm and Orthogonal Crossover for Optimal Shape Design of Concrete Gravity Dams

  • Authors: m. khatibinia, s. khosravi
  • Journal: Applied Soft Computing, Volume 16, Pages 223-233, 2014
  • Citations: 99
  • Abstract:
    This research presents a hybrid optimization technique combining an Improved Gravitational Search Algorithm (IGSA) with Orthogonal Crossover for the shape optimization of concrete gravity dams. The hybrid method addresses computational challenges in design optimization, yielding high-quality solutions with improved convergence efficiency.

3. RETRACTED: Shear Behaviour of Concrete Beams with Recycled Aggregate and Steel Fibres

  • Authors: h.r. chaboki, m. ghalehnovi, a. karimipour, j. de brito, m. khatibinia
  • Journal: Construction and Building Materials, Volume 204, Pages 809-827, 2019
  • Citations: 95
  • Abstract:
    (Retracted) The study initially examined the shear behavior of concrete beams incorporating recycled aggregates and steel fibers, aiming to explore their structural and sustainability benefits. However, the paper has been retracted, and further details are unavailable or unreliable.

4. Optimizing Parameters of Tuned Mass Damper Subjected to Critical Earthquake

  • Authors: r. kamgar, p. samea, m. khatibinia
  • Journal: The Structural Design of Tall and Special Buildings, Volume 27, Issue 7, Article e1460, 2018
  • Citations: 92
  • Abstract:
    This paper focuses on optimizing the parameters of Tuned Mass Dampers (TMDs) to enhance their performance under critical seismic events. The study utilizes advanced optimization techniques to achieve an optimal balance between cost and efficiency, improving the structural stability of tall buildings.

5. Seismic Reliability Assessment of RC Structures Including Soil–Structure Interaction Using Wavelet Weighted Least Squares Support Vector Machine

  • Authors: m. khatibinia, m.j. fadaee, j. salajegheh, e. salajegheh
  • Journal: Reliability Engineering & System Safety, Volume 110, Pages 22-33, 2013
  • Citations: 92
  • Abstract:
    This study proposes a novel approach using the Wavelet Weighted Least Squares Support Vector Machine (WLS-SVM) model for seismic reliability analysis of reinforced concrete (RC) structures, incorporating the effects of soil-structure interaction (SSI). The method offers a computationally efficient way to assess structural reliability under seismic loading conditions.

Conclusion

Dr. Mohsen Khatibinia is a strong candidate for the Best Researcher Award due to his impactful contributions to structural engineering, particularly in earthquake-resilient design and optimization. His innovative application of computational intelligence and hybrid optimization techniques highlights his expertise and commitment to advancing the field. While addressing the retraction and expanding his collaborative and publication scope could enhance his profile, his achievements make him a deserving nominee for this prestigious recognition.

 

jinghua Li | Vibration Signal Analysis | Young Scientist Award

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Mr. jinghua Li | Vibration Signal Analysis | Young Scientist Award

Orcid Profile

Educational Details:

Li Jinghua completed his undergraduate studies in Mechanical Design, Manufacturing, and Automation at Shenyang Ligong University from September 2018 to July 2022. Following this, he pursued a Master’s degree in Mechanical and Electronic Engineering at the same university, with his studies spanning from September 2022 to March 2025. Throughout his academic journey, Li has developed a strong foundation in mechanical engineering principles, focusing on the integration of mechanical systems with electronic components, which equips him with the skills necessary for innovative design and problem-solving in the field.

Undergraduate

During his academic journey, Li Jinghua has gained comprehensive knowledge in both undergraduate and master’s degree programs. His undergraduate coursework included foundational subjects such as Basics of Mechanical Design and Manufacturing, Mechanical Principles, Mechanical Design, Basics of Control Engineering, Hydraulic Transmission, Material Mechanics, and Theoretical Mechanics. These courses provided him with a solid understanding of mechanical systems and their applications. In his master’s program, he expanded his expertise to encompass advanced topics such as Signal Analysis and Processing, Digital Image Processing and Machine Vision, Artificial Intelligence and Machine Learning, Mechanical Engineering Testing, and Modern Control Theory. This blend of traditional mechanical engineering principles and cutting-edge technological applications positions Li to contribute significantly to the field of mechanical and electronic engineering.

Textbooks

Li Jinghua serves as the Deputy Editor-in-Chief for the publication “Engineering Application and Practice of Intelligent Computing Power,” published by People’s Posts and Telecommunications Press. In this role, he contributes to the editorial direction and quality of the work, focusing on the practical applications of intelligent computing in engineering. His expertise in mechanical and electronic engineering, combined with his understanding of advanced computing technologies, allows him to guide the inclusion of cutting-edge research and innovative practices. Through this publication, Li aims to promote knowledge sharing and collaboration among professionals in the field, enhancing the integration of intelligent computing solutions in engineering practices.

Software works

Li Jinghua has been involved in several innovative projects that showcase his expertise in image processing and machine learning. One of his notable projects is the Video Target Tracking and Area Detection System based on YOLOv8, which enhances real-time tracking and analysis of moving objects in video streams. Additionally, he has developed a Picture Style Migration System utilizing VGG19, enabling the transformation of images to mimic various artistic styles. Another significant project is the GAN-based Character Avatar Generation System, which employs Generative Adversarial Networks to create unique character avatars for use in gaming and virtual environments. Li has also contributed to a Rice Disease Detection System based on ResNet, facilitating early identification of diseases in rice crops to improve agricultural productivity. Furthermore, he has worked on an All-Round Visual Analysis System that integrates image classification, target detection, semantic segmentation, and instance segmentation, providing comprehensive analytical capabilities for various visual data applications. Through these projects, Li has demonstrated his commitment to advancing intelligent computing solutions across diverse fields.

Top Notable Publications

 

Conclusion

 

 

Takayuki Isii | Crystal Chemistry | Best Researcher Award

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Assoc Prof Dr. Takayuki Isii | Crystal chemistry | Best Researcher Award

Orcid Profile

Educational Details:

Dr. Takayuki Isii completed his academic training at Gakushuin University, where he earned his Ph.D. in Science from the Graduate School of Natural Science in 2015. Prior to his doctoral studies, he received his Master of Science in 2012, also from the Graduate School of Natural Science at Gakushuin University. His academic foundation began with a Bachelor of Science degree in Chemistry from the Faculty of Science, Gakushuin University, which he obtained in 2010. His educational journey provided a robust background in the natural sciences, laying the groundwork for his subsequent research in high-pressure geophysics and mineral physics.

Professional Experience

Dr. Isii is currently an Associate Professor at Okayama University (April 2023 – present), where he focuses on high-pressure planetary materials. Prior to this, he was a tenure-tracked staff scientist at the Center for High Pressure Science & Technology Advanced Research (April 2021 – March 2023). His earlier roles include serving as the Principal Investigator of a German Research Foundation project (April 2019 – March 2021), Humboldt Research Fellow (April 2017 – March 2019), and Overseas Research Fellow supported by the Japan Society for the Promotion of Science (JSPS) (April 2016 – March 2017). Dr. Isii also held postdoctoral fellowships under JSPS, focusing on the high-pressure properties of minerals relevant to Earth’s mantle.

Research Interest

Dr. Isii’s research focuses on high-pressure mineral physics, geophysics, and materials science. His primary interest lies in investigating phase transitions, chemical heterogeneities, and physical properties of Earth’s deep interior materials under extreme conditions. He also explores hydrous mineral stability and their implications for planetary processes. Dr. Isii actively contributes to advancing understanding in high-pressure science, geoscience, and planetary materials through experimental research and collaboration with international scientific communities.

Top Notable Publications

Takayuki Ishii (2024). “Synthesis and Crystal Structure of Ilmenite-Type Silicate with Pyrope Composition.” Solids, 5(3). DOI: 10.3390/solids5030026.

Takayuki Ishii (2024). “Hydrogen partitioning between stishovite and hydrous phase δ: Implications for water cycle and distribution in the lower mantle.” Progress in Earth and Planetary Science. DOI: 10.1186/s40645-024-00615-0.

Takayuki Ishii (2023). “Buoyancy of slabs and plumes enhanced by curved post-garnet phase boundary.” Nature Geoscience. DOI: 10.1038/s41561-023-01244-w.

Takayuki Ishii (2023). “Iron and aluminum substitution mechanism in the perovskite phase in the system MgSiO3-FeAlO3-MgO.” American Mineralogist, 108(4). DOI: 10.2138/am-2022-8457.

Takayuki Ishii (2023). “Equation of State and Spin Crossover of (Al, Fe)‐Phase H.” Journal of Geophysical Research: Solid Earth. DOI: 10.1029/2022JB026291.

Takayuki Ishii (2023). “Ferric Iron Substitution Mechanism in Bridgmanite under SiO2-Saturated Conditions at 27 GPa.” ACS Earth and Space Chemistry, 7(2). DOI: 10.1021/acsearthspacechem.2c00326.

Takayuki Ishii (2023). “Stability of Fe5O6 and its relation to other Fe-Mg-oxides at high pressures and temperatures.” American Mineralogist, 108(1). DOI: 10.2138/am-2022-8370.

Takayuki Ishii (2023). “Synthesis and structural analysis of CaFe2O4-type single crystals in the NaAlSiO4-MgAl2O4-Fe3O4 system.” American Mineralogist, 108(1). DOI: 10.2138/am-2022-8748.

Takayuki Ishii (2023). “The influence of Al2O3 on the structural properties of MgSiO3 akimotoite.” American Mineralogist, 108(1). DOI: 10.2138/am-2022-8257.

Takayuki Ishii (2022). “Superhydrous aluminous silica phases as major water hosts in high-temperature lower mantle.” Proceedings of the National Academy of Sciences, 119(46). DOI: 10.1073/pnas.2211243119.

Conclusion

Dr. Takayuki Isii’s research portfolio reflects a dedication to scientific innovation with a profound community impact. His interdisciplinary and international research efforts, alongside his focus on critical global challenges, make him a strong contender for the Research for Community Impact Award. His work not only advances scientific knowledge but also contributes to solving real-world problems that affect global communities.