Amjad Ali | Experimental Physics | Best Researcher Award

Prof. Amjad Ali | Experimental Physics | Best Researcher Award  

Prof. Amjad Ali | School of Materials Science and Engineering, Jiangsu University | China

Prof. Amjad Ali is an accomplished chemist specializing in polymer science, catalysis, and sustainable materials. He earned his Ph.D. from Zhejiang University, China, where his research focused on the kinetics and mechanisms of olefin polymerizations using advanced zirconocene catalysts. With expertise in organic synthesis, polymer engineering, and biodegradable materials, Prof. Ali has published widely, served as a guest editor for international journals, and actively reviews for leading scientific publications. His global collaborations span institutions in Pakistan, China, Poland, and Saudi Arabia, reflecting his commitment to interdisciplinary research in environmental sustainability, energy materials, and advanced polymer systems.

Author Profiles

Scopus | Orcid | Google Scholar

Early Academic Pursuits

Prof. Amjad Ali began his academic career with a B.Sc. in Chemistry and Biology from the University of Karachi in 2010, followed by an M.Sc. in Organic Chemistry from FUUAST University in 2013. His passion for scientific discovery led him to one of the world’s top-ranked institutions, Zhejiang University in China, where he earned his Ph.D. in Chemistry in July 2020. His doctoral research focused on “Kinetic and Mechanistic Studies on Olefin Homo- and Copolymerizations Catalyzed with ansa-Zirconocene/Borate/-Alkylaluminum” a project rooted in Experimental Physics, polymer science, and catalysis. This solid academic foundation laid the groundwork for his future contributions in materials science, sustainable polymers, and catalysis.

Professional Endeavors

Prof. Amjad Ali’s professional experience spans across Pakistan, China, Poland, and the Middle East, where he has served as a researcher, assistant professor, and associate professor. His expertise includes organic synthesis, polymer engineering, reaction kinetics, and advanced analytical techniques, all deeply rooted in the Experimental Physics of chemical reactions and materials. His international research activities, particularly during his Ph.D. and postdoctoral phases, include major collaborative projects with prominent institutions like Zhejiang University (China), the University of Silesia (Poland), King Saud University (Saudi Arabia), and McMaster University (Canada). His leadership in laboratory management, teaching at both undergraduate and graduate levels, and participation in multiple global workshops has made him an influential educator and experimental researcher.

Contributions and Research Focus

Prof. Ali’s research is primarily focused on the design and synthesis of novel catalysts, development of biodegradable and sustainable polymers, and the fabrication of composite materials incorporating MOFs, COFs, and biopolymers. His studies on chemical modification of bio-based materials aim to enhance their electrochemical, adhesive, and environmental properties. A key element of his work is rooted in the Experimental Physics of polymerization reactions elucidating kinetic behavior, molecular interactions, and reaction mechanisms to optimize catalyst performance and material properties. He also addresses environmental challenges related to plastic waste through innovative material science solutions.

Impact and Influence

Prof. Amjad Ali’s work has garnered international recognition for its interdisciplinary scope and environmental relevance. He has served as a Guest Editor for leading journals including Polymers, Processes, and Sustainability, curating special issues on nanomaterials, smart polymers, and sustainable waste management. His influence is reflected in his extensive reviewer role for high-impact journals like ACS Omega, Chemosphere, Scientific Reports, and RSC Advances. His ability to merge chemistry with Experimental Physics principles has significantly advanced both theoretical understanding and practical applications in polymer science and environmental engineering.

Academic Cites

Prof. Amjad Ali’s publications are widely cited across scientific disciplines, particularly in polymer chemistry, catalysis, and materials science. His work contributes to the evolving fields of sustainable chemistry and Experimental Physics, especially in relation to catalyst design and kinetic studies. His role in global academia is further strengthened by his active collaborations with over a dozen international researchers and institutions, ensuring that his scientific contributions reach a broad and interdisciplinary audience.

Legacy and Future Contributions

As a prolific academic with a deep understanding of materials science, Prof. Amjad Ali is poised to leave a lasting legacy in the areas of sustainable polymers, catalyst innovation, and interdisciplinary research. His future work is expected to further bridge Experimental Physics with green chemistry, particularly in designing next-generation biodegradable materials and advanced composites for energy and environmental applications. His mentorship of students, leadership in international collaborations, and dedication to scientific outreach position him as a global thought leader in experimental materials science.

Experimental Physics

Prof. Amjad Ali's research excellence lies in the interface of Experimental Physics and chemical sciences. His methodical approach to kinetic modeling, reaction optimization, and polymer structure analysis showcases how Experimental Physics principles can drive innovation in catalyst performance and sustainability. His laboratory work emphasizes precise control over variables such as temperature, reaction time, and molecular interactions hallmarks of rigorous experimental methodology. As he continues to push the frontiers of material synthesis, Prof. Ali’s integration of Experimental Physics into chemistry makes him a central figure in the global movement toward eco-friendly and performance-optimized materials.

Notable Publications 

A review on the modification of cellulose and its applications

  • Authors: T. Aziz, A. Farid, F. Haq, M. Kiran, A. Ullah, K. Zhang, C. Li, S. Ghazanfar, ...
    Journal: Polymers
    Year: 2022
    Citations: 345

Recent progress in silane coupling agent with its emerging applications

  • Authors: T. Aziz, A. Ullah, H. Fan, M.I. Jamil, F.U. Khan, R. Ullah, M. Iqbal, A. Ali, B. Ullah
    Journal: Journal of Polymers and the Environment
    Year: 2021
    Citations: 194

Wearable and flexible multifunctional sensor based on laser-induced graphene for the sports monitoring system

  • Authors: T. Raza, M.K. Tufail, A. Ali, A. Boakye, X. Qi, Y. Ma, A. Ali, L. Qu, M. Tian
    Journal: ACS Applied Materials & Interfaces
    Year: 2022
    Citations: 104

Manufactures of bio‐degradable and bio‐based polymers for bio‐materials in the pharmaceutical field

  • Authors: T. Aziz, A. Ullah, A. Ali, M. Shabeer, M.N. Shah, F. Haq, M. Iqbal, R. Ullah, ...
    Journal: Journal of Applied Polymer Science
    Year: 2022
    Citations: 90

Advances and applications of cellulose bio-composites in biodegradable materials

  • Authors: Z. Chen, T. Aziz, H. Sun, A. Ullah, A. Ali, L. Cheng, R. Ullah, F.U. Khan
    Journal: Journal of Polymers and the Environment
    Year: 2023
    Citations: 73

Revisiting recent and traditional strategies for surface protection of Zn metal anode

  • Authors: A. Naveed, A. Ali, T. Rasheed, X. Wang, P. Ye, X. Li, Y. Zhou, S. Mingru, Y. Liu
    Journal: Journal of Power Sources
    Year: 2022
    Citations: 67

Ion chromatography coupled with fluorescence/UV detector: A comprehensive review of its applications in pesticides and pharmaceutical drug analysis

  • Authors: N. Muhammad, M. Zia-ul-Haq, A. Ali, S. Naeem, A. Intisar, D. Han, H. Cui, ...
    Journal: Arabian Journal of Chemistry
    Year: 2021
    Citations: 67

Farajollah Tahernezhad-Javazm | Experimental Physics | Best Researcher Award 

Dr. Farajollah Tahernezhad-Javazm | Experimental Physics | Best Researcher Award 

Dr. Farajollah Tahernezhad-Javazm | University of York | United Kingdom

Dr. Farajollah Tahernezhad-Javazm is a researcher in computer science and mechatronics with expertise in reinforcement learning, brain-machine interfaces, and evolutionary algorithms. He earned his Ph.D. from Ulster University, UK, focusing on transforming multiobjective evolutionary algorithms using hybrid structures and reinforcement learning. He currently serves as a Research Associate at the University of York, contributing to bio-inspired engineering design through Graph Neural Networks and Cartesian Genetic Programming. His previous roles include data analytics research at the University of Lincoln, a visiting Ph.D. position at Auburn University, and neuroimaging research at Ulster’s MEG lab. His interdisciplinary work spans AI, optimization, and neural signal processing.

Author Profiles

GOOGLE SCHOLAR | ORCID | SCOPUS

Early Academic Pursuits

Dr. Farajollah Tahernezhad-Javazm’s academic foundation is rooted in electronics and mechatronics engineering, disciplines that enabled him to engage early with complex systems and intelligent interfaces. He earned his BSc in Electronics Engineering from Yazd University in 2013, where he designed a programmable Building Management System (BMS). He progressed to an MSc in Mechatronics Engineering from the University of Tabriz in 2016, focusing on real-time brain-machine interface (BMI) systems using combinatorial classification methods. His academic trajectory culminated with a PhD in Computer Science from Ulster University, Northern Ireland, in May 2024. His doctoral thesis, “Reinforcement Learning and Hybrid Structures: Transforming Multiobjective Evolutionary Algorithms,” under the supervision of Prof. Damien Coyle and Dr. Debbie Rankin, integrated reinforcement learning with evolutionary computation, setting a strong theoretical and practical foundation for his future contributions to Experimental Physics and intelligent systems.

Professional Endeavors

Dr. Tahernezhad-Javazm has held numerous interdisciplinary roles across academia and research institutions. As a Research Associate at the University of York since November 2024, he contributes to the Re-Imagining Engineering Design (RIED) project, advancing bio-inspired methodologies using Graph Neural Networks (GNN), Cartesian Genetic Programming (CGP), and Reinforcement Learning (RL). Previously, at the University of Lincoln (2023–2024), he applied machine learning for data analytics in the food supply chain sector. His international research engagement includes time as a Visiting PhD Researcher at Auburn University in the U.S., working on multiobjective algorithms and reinforcement learning, and as a Research Assistant at Ulster University’s Magnetoencephalography (MEG) Laboratory, where he played a crucial role in managing high-sensitivity brain imaging systems  contributing directly to Experimental Physics through real-time neuroimaging calibration, testing, and maintenance.

Contributions and Research Focus

Dr. Tahernezhad-Javazm’s research blends machine learning, neuroengineering, reinforcement learning, and optimization. His primary focus lies in developing hybrid intelligence systems to solve real-world challenges in Experimental Physics and computational design. His contributions span the development of AI-powered BMIs, reinforcement learning-enhanced evolutionary algorithms, and advanced sensor systems used in neuroimaging and engineering design. His work integrates data-driven modeling with bio-inspired computation, making significant inroads in signal decoding, system optimization, and neural data interpretation.

Impact and Influence

Through interdisciplinary collaborations in the UK, Iran, and the U.S., Dr. Tahernezhad-Javazm has advanced impactful methodologies in both engineering and Experimental Physics. His influence is evident in his work with high-profile teams like Prof. Andy Tyrrell’s group at the University of York and Prof. Alice E. Smith’s group at Auburn University. He bridges computational sciences with experimental applications  particularly in bioengineering, neuroimaging, and smart system design. His MEG research also supports cognitive neuroscience by ensuring accurate and calibrated data collection, which is critical to the experimental integrity of advanced physics-driven medical systems.

Academic Cites

Dr. Tahernezhad-Javazm’s academic contributions have been published and cited across several interdisciplinary fields, including intelligent systems, optimization algorithms, and computational neuroscience. His work has been featured in machine learning conferences and neuroscience venues, particularly those focusing on Experimental Physics and real-time brain-machine interface systems. His publications reflect an evolving and impactful research profile with applications in healthcare, manufacturing, and engineering design.

Legacy and Future Contributions

Dr. Farajollah Tahernezhad-Javazm is on a promising trajectory to become a leading figure in AI-driven research with applications in Experimental Physics. With his experience across disciplines   from brain-computer interfaces to evolutionary design he is poised to develop the next generation of adaptive, learning-based systems for real-world problems. His future work will likely explore deeper integration of physical sensor data with AI for predictive diagnostics, smart engineering systems, and dynamic experimentation frameworks. His legacy will be defined by a commitment to bridging machine learning with experimental sciences for enhanced innovation and societal impact.

Experimental Physics

Dr. Tahernezhad-Javazm’s research actively contributes to Experimental Physics through MEG calibration systems, real-time brain-machine interface innovations, and AI-driven optimization. His hybrid models enhance algorithmic performance in domains rooted in Experimental Physics, especially in neurotechnology and intelligent systems. The intersection of machine learning, signal processing, and physical experimentation in his research will continue to shape developments in Experimental Physics for years to come.

 Notable Publications

Mapping the path to decarbonised agri-food products: A hybrid geographic information system and life cycle inventory methodology for assessing sustainable agriculture
Authors: W. Martindale, A. Saeidan, F. Tahernezhad-Javazm, T.Æ. Hollands, L. Duong, ...
Journal: International Journal of Food Science and Technology
Year: 2024
Citations: 2

R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm
Authors: F. Tahernezhad-Javazm, D. Rankin, N. Du Bois, A.E. Smith, D. Coyle
Journal: arXiv preprint
Year: 2024
Citations: 1

R2-HMEWO: Hybrid multi-objective evolutionary algorithm based on the Equilibrium Optimizer and Whale Optimization Algorithm
Authors: F. Tahernezhad-Javazm, D. Rankin, D. Coyle
Conference: IEEE World Congress on Computational Intelligence
Year: 2022
Citations: 8

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator
Authors: F. Tahernezhad-Javazm, D. Rankin, D. Coyle
Conference: International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
Year: 2022
Citations: 2

Ontology based information integration: A survey
Authors: M. Alizadeh, M.H. Shahrezaei, F. Tahernezhad-Javazm
Journal: arXiv preprint
Year: 2019
Citations: 9