Songtao Lv | Particle Experiments | Research Excellence Award

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Research Excellence Award

Songtao Lv
Changsha University of Science and Technology

Songtao Lv
Affiliation Changsha University of Science and Technology
Country China
Scopus ID 57169645600
Documents 189
Citations 4900
h-index 38
Subject Area Particle Experiments
Event Global Particle Physics Excellence Awards
ORCID 0000-0003-0426-5033

Songtao Lv is a professor and doctoral supervisor at Changsha University of Science and Technology whose research activity has focused on pavement engineering, asphalt materials, fatigue behavior, and infrastructure performance assessment. Academic profile indicators demonstrate sustained publication output with substantial citation activity and continued contribution to engineering literature. Research records indexed through international databases indicate broad participation in collaborative scientific work and interdisciplinary engineering studies.[1]

Abstract

This article presents an overview of the scholarly profile, publication activity, and research output associated with Songtao Lv of Changsha University of Science and Technology. Available publication indicators and indexing information demonstrate sustained research productivity across multiple engineering topics and collaborative scientific studies. Research records suggest continued engagement in material characterization, pavement performance studies, and analytical investigations within modern engineering research environments. The profile further reflects measurable scholarly visibility through publication metrics and international indexing systems.[2]

Introduction

Modern infrastructure research increasingly requires advanced evaluation techniques capable of understanding material behavior under complex operational conditions. Engineering studies in pavement materials have become important because of their relationship with transportation durability and long-term structural performance. Research associated with Songtao Lv reflects continuing investigation into these scientific areas through experimental analysis and performance evaluation approaches. Multiple studies indicate an emphasis on practical engineering applications supported by analytical methodology.[3]

Research Profile

Academic records indicate that Songtao Lv has participated in research activities involving asphalt mixtures, fatigue damage mechanisms, rheological assessment, and infrastructure material studies. Publication trends suggest broad engagement across laboratory experimentation and engineering modeling methods. Scholarly collaboration with researchers from multiple institutions is also evident through co-authored publications and multidisciplinary contributions. These activities demonstrate continuity in research participation and publication output.[4]

Research Contributions

Research contributions include investigations related to fatigue behavior analysis, modified asphalt performance, self-healing material systems, and structural response modeling. Published findings also discuss aging characteristics and dynamic behavior under changing environmental conditions. Several studies propose assessment frameworks designed to improve understanding of engineering material performance and durability mechanisms. Such contributions indicate active involvement in methodological development and engineering analysis.[5]

Publications

Published work associated with Songtao Lv includes studies addressing fatigue evolution, self-healing behavior, rheological performance, dynamic response analysis, and pavement material assessment. Recent scholarly activity additionally discusses recycled material applications and modified asphalt systems under varying environmental conditions. Publication records indicate continuity across engineering topics while maintaining relevance to broader material science and infrastructure research objectives. The available research profile suggests ongoing contribution to peer-reviewed academic literature.

Research Impact

Publication metrics including citation activity and h-index indicators are commonly used to assess scholarly influence within academic environments. Available information indicates that Songtao Lv has developed measurable visibility through sustained publication output and research dissemination practices. Citation patterns suggest that published findings have received attention from related scientific communities and engineering researchers. Such indicators contribute to understanding broader research influence and academic reach.[1]

Award Suitability

Academic recognition programs frequently consider publication records, citation indicators, collaborative research activity, and sustained scholarly contribution during evaluation processes. Based on available profile information, the publication history and engineering research activity associated with Songtao Lv may align with several commonly used academic assessment criteria. The combination of productivity and scholarly visibility provides supporting context for research-related recognition evaluation. Assessment outcomes nevertheless remain dependent upon independent award selection procedures.

Conclusion

The academic profile of Songtao Lv reflects substantial engagement in engineering research through publication activity and collaborative scientific participation. Available metrics and publication records indicate continued involvement in material science and infrastructure-related investigations. Research output demonstrates consistency across several engineering themes while contributing to broader technical understanding and analytical development. The profile therefore represents an active and sustained scholarly research presence.

References

  1. Lv, S., Peng, X., Liu, C., Ge, D., Tang, M., & Zheng, J. (2020). Laboratory investigation of fatigue parameters characteristics of aging asphalt mixtures: A dissipated energy approach. Construction and Building Materials, 242, 116972.
    DOI: https://doi.org/10.1016/j.conbuildmat.2019.116972
  2. Lv, S., Zhao, T., Xia, C., Zhao, S., Liu, T., Liu, Y., Liu, B., & Cabrera, M. B. (2022). A new method for characterizing the fatigue performance of high-modulus asphalt mixtures. Journal of Testing and Evaluation, 50(4).
    DOI: https://doi.org/10.1520/JTE20210719
  3. He, L., Li, G., Lv, S., Gao, J., Kowalski, K. J., Valentin, J., & Alexiadis, A. (2020). Self-healing behavior of asphalt system based on molecular dynamics simulation. Construction and Building Materials, 254, 119225.
    DOI: https://doi.org/10.1016/j.conbuildmat.2020.119225
  4. Lv, S., Ge, D., Wang, Z., Wang, J., Liu, J., Ju, Z., Peng, X., Fan, X., Cao, S., & Liu, D. (2023). Performance assessment of self-healing polymer-modified bitumens by evaluating the suitability of current failure definition, failure criterion, and fatigue-restoration criteria. Materials, 16(6), 2488.
    DOI: https://doi.org/10.3390/ma16062488
  5. Xia, C., Lv, S., You, L., Chen, D., Li, Y., & Zheng, J. (2019). Unified strength model of asphalt mixture under various loading modes. Materials, 12(6), 889.
    DOI: https://doi.org/10.3390/ma12060889

Chun-Wang Ma | Nuclear Physics | Best Scholar Award

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Best Scholar Award

Chun-Wang Ma
Affiliation Henan Normal University
Country China
Scopus ID 8723805700
Documents 190
Citations 2,117
h-index 24
Subject Area Nuclear Physics
Event Global Particle Physics Excellence Awards
ORCID 0000-0001-9372-518X

Chun-Wang Ma

Professor Chun-Wang Ma is a nuclear physicist affiliated with Henan Normal University, China, whose research has contributed to the understanding of heavy-ion collisions, projectile fragmentation reactions, nuclear symmetry energy, neutron-rich isotopes, photonuclear reactions, and modern computational approaches in nuclear science. His scholarly work spans theoretical modeling, experimental nuclear physics, information entropy applications, and machine learning methodologies for nuclear reaction analysis. Through extensive publication activity and international collaboration, he has contributed to advancing contemporary nuclear and particle physics research.[1][2]

Abstract

The Best Scholar Award recognizes researchers whose sustained academic contributions demonstrate scientific excellence, innovation, and measurable impact. Chun-Wang Ma has established a notable research profile in nuclear physics through studies involving heavy-ion collisions, projectile fragmentation, neutron-rich nuclei, nuclear symmetry energy, photonuclear reactions, and data-driven methodologies. His publication record, citation performance, and leadership in funded research projects reflect continued engagement with important scientific questions in nuclear science and technology. The breadth of his scholarly activities supports his recognition within the international nuclear physics community.[1][3]

Keywords

Nuclear Physics, Heavy-Ion Collisions, Projectile Fragmentation, Nuclear Symmetry Energy, Neutron-Rich Isotopes, Photonuclear Reactions, Rare Isotopes, Machine Learning in Physics, Bayesian Neural Networks, Information Entropy, Nuclear Analysis, Particle Physics.

Introduction

Nuclear physics remains fundamental to understanding the structure, interactions, and evolution of matter. Researchers in this field investigate nuclear reactions, isotope production, radiation effects, and particle interactions that have implications for both fundamental science and technological applications. Within this landscape, Chun-Wang Ma has developed a research portfolio focused on heavy-ion reaction mechanisms, neutron-rich nuclear systems, and quantitative approaches for interpreting complex nuclear phenomena. His investigations integrate experimental observations with theoretical and computational techniques, contributing to improved predictive capabilities in nuclear reaction studies.[1][4]

Research Profile

Chun-Wang Ma serves as Professor in the College of Physics at Henan Normal University and has additionally held leadership responsibilities within the Institute of Nuclear Science and Technology of the Henan Academy of Sciences. His academic background includes studies in physics and nuclear physics, supporting a career dedicated to nuclear reaction dynamics, isotope production, and advanced nuclear measurement techniques.[1]

  • Professor, College of Physics, Henan Normal University.
  • Research interests include heavy-ion collisions, photonuclear physics, nuclear radiation applications, and nuclear analysis.
  • Principal investigator and participant in multiple nationally funded scientific projects.
  • Author of a substantial body of peer-reviewed publications in internationally recognized journals.

Research Contributions

Professor Ma’s contributions encompass several interconnected domains of nuclear physics. His work on projectile fragmentation reactions has improved understanding of fragment production mechanisms and isotope distributions. He has also investigated neutron-skin thickness, symmetry energy behavior, and isospin effects in nuclear reactions, providing analytical frameworks useful for interpreting experimental observations.[5]

A notable aspect of his research is the integration of machine learning and Bayesian neural network methodologies into nuclear physics. These approaches have been applied to fragment production prediction, charge-radius estimation, spallation reaction analysis, and nuclear data evaluation, illustrating the growing role of artificial intelligence in modern physics research.

His investigations into information entropy and heavy-ion collisions have also contributed to the quantitative characterization of nuclear reaction systems, linking statistical concepts with observable nuclear phenomena.

Publications

Selected publications representative of Chun-Wang Ma’s research activities include:

  • Nuclear Fragments in Projectile Fragmentation Reactions (Progress in Particle and Nuclear Physics, 2021).
  • Systematic Behavior of Fragments in Bayesian Neural Network Models for Projectile Fragmentation Reactions (Physical Review C, 2023).
  • Determination of Neutron-Skin Thickness Using Configurational Information Entropy (Nuclear Science and Techniques, 2022).
  • Shannon Information Entropy in Heavy-Ion Collisions (Progress in Particle and Nuclear Physics, 2018).
  • A Novel Bayesian Neural Network Approach for Nuclear Root-Mean-Square Charge Radii (IEEE Transactions on Nuclear Science, 2025).
  • Bubble 36Ar and its New Breathing Modes (Physics Letters B, 2024).
  • A Possible Probe to Neutron-Skin Thickness by Fragment Parallel Momentum Distribution in Projectile Fragmentation Reactions (2024).

Research Impact

The research impact of Chun-Wang Ma is reflected in a substantial publication portfolio, more than two thousand scholarly citations, and an h-index of 24. His studies have appeared in journals including Physical Review C, Physical Review Letters, Physics Letters B, Progress in Particle and Nuclear Physics, Nuclear Science and Techniques, Chinese Physics C, and IEEE Transactions on Nuclear Science. These publications contribute to ongoing discussions regarding nuclear structure, rare isotope production, reaction dynamics, and advanced computational modeling.[2]

His participation in competitive research grants further demonstrates scientific leadership and sustained engagement with nationally significant research initiatives focused on rare isotopes, projectile fragmentation, and neutron-rich nuclear systems.[3]

Award Suitability

The nomination of Chun-Wang Ma for the Best Scholar Award is supported by several indicators of academic achievement. These include a sustained publication record, recognized contributions to nuclear physics research, successful acquisition of competitive research funding, interdisciplinary integration of machine learning methods, and active participation in advancing understanding of nuclear reaction mechanisms. His work demonstrates both depth within specialized areas of nuclear physics and adaptability to emerging computational techniques, characteristics frequently associated with scholarly distinction and research excellence.[1][3]

Conclusion

Chun-Wang Ma has established a respected academic profile through sustained contributions to nuclear physics, particularly in the areas of heavy-ion collisions, projectile fragmentation, neutron-rich nuclei, and computational nuclear science. His combination of theoretical insight, experimental engagement, and methodological innovation has produced a body of work that continues to influence ongoing research in the field. Based on his scholarly achievements, research productivity, and scientific impact, he represents a strong candidate for recognition through the Best Scholar Award presented at the Global Particle Physics Excellence Awards.

References

  1. ORCID. (n.d.). Chun-Wang Ma (0000-0001-9372-518X) researcher profile. ORCID.
    https://orcid.org/0000-0001-9372-518X
  2. Elsevier. (n.d.). Scopus author details: Chun-Wang Ma, Author ID 8723805700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=8723805700
  3. National Natural Science Foundation of China. Research funding projects led and participated in by Chun-Wang Ma.
    https://orcid.org/0000-0001-9372-518X
  4. Ma, C.-W. et al. (2021). Nuclear Fragments in Projectile Fragmentation Reactions. Progress in Particle and Nuclear Physics.
    DOI: https://doi.org/10.1016/j.ppnp.2021.103911
  5. Ma, C.-W. et al. (2022). Determination of Neutron-Skin Thickness Using Configurational Information Entropy. Nuclear Science and Techniques.
    DOI: https://doi.org/10.1007/s41365-022-00997-0

Abdul Qadir | Machine Learning in Physics | Innovative Research Award

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Innovative Research Award

Abdul Qadir – Wichita State University

Abdul Qadir
Affiliation Wichita State University
Country United States
Goggle Scholar ID View Profile
Documents 10
Citations 4
h-index 1
Subject Area Composite Materials
Event Global Particle Physics Excellence Awards
ORCID 0009-0004-3993-692X

Abdul Qadir is a researcher in mechanical engineering and advanced materials whose work spans composite materials, semiconductor applications, renewable energy systems, fluid dynamics, and artificial intelligence-assisted experimental analysis. His academic profile reflects interdisciplinary research activities that combine engineering fundamentals with emerging computational approaches. The Innovative Research Award recognizes scholarly efforts that demonstrate originality, practical relevance, and measurable scientific contribution within contemporary research domains.[1]

Abstract

This article summarizes the academic achievements and research activities of Abdul Qadir in the fields of composite materials, energy systems, fluid measurements, and machine learning-assisted engineering analysis. His work emphasizes the integration of experimental methodologies with computational tools to improve measurement accuracy and material performance. The body of research includes journal articles, conference papers, and scholarly chapters that contribute to ongoing scientific discussions in engineering and applied sciences.[1]

Keywords

Composite Materials, Particle Streak Velocimetry, Renewable Energy, Semiconductor Materials, Artificial Intelligence, Photocatalysis, Mechanical Engineering, Experimental Fluid Dynamics.

Introduction

Research in modern engineering increasingly requires multidisciplinary collaboration and the combination of experimental and computational methods. Abdul Qadir has contributed to this evolving landscape through studies involving fluid measurement technologies, advanced coatings, photocatalysis, and data-driven optimization. His academic activities demonstrate engagement with practical engineering challenges while supporting broader scientific advancement through publication and knowledge dissemination.[2]

Research Profile

Currently affiliated with Wichita State University as a Research Assistant, Abdul Qadir has pursued research across multiple international academic environments. His interests include composite materials, renewable energy technologies, semiconductor-based photocatalysis, image processing, and machine learning applications in engineering. These areas collectively support innovative approaches for solving complex technical problems while improving experimental reliability and analytical accuracy.[1]

Research Contributions

  • Development of AI-assisted noise removal approaches for particle streak velocimetry imaging.
  • Research on hard ceramic coatings for aerospace alloy applications.
  • Studies on photocatalytic nitrogen fixation and semiconductor materials.
  • Optimization techniques for biodiesel production and energy systems.
  • Advancement of flow measurement methodologies through experimental and computational integration.

Publications

The publications authored and co-authored by Abdul Qadir reflect a multidisciplinary research approach spanning composite materials, renewable energy systems, fluid dynamics, semiconductor materials, and artificial intelligence applications in engineering. His scholarly work emphasizes the integration of experimental investigations with advanced computational techniques to address contemporary engineering challenges. Through journal articles, conference proceedings, book chapters, and technical presentations, he has contributed to the advancement of knowledge in materials characterization, flow measurement technologies, photocatalytic processes, and data-driven optimization methods. These publications demonstrate a commitment to scientific rigor, innovation, and the dissemination of research findings to both academic and industrial communities.[3]

Research Impact

The documented publication record highlights contributions to engineering measurement systems, materials science, and sustainable energy research. Through peer-reviewed articles and conference presentations, Abdul Qadir has supported the dissemination of methodologies that improve data quality and engineering analysis. The combination of experimental validation and computational innovation reflects a research profile aligned with contemporary scientific priorities.[3]

Award Suitability

The Innovative Research Award recognizes researchers whose work demonstrates originality, interdisciplinary engagement, and scholarly productivity. Abdul Qadir’s contributions to composite materials, energy technologies, and AI-enhanced experimental methods align with these criteria. His publication record, international academic experience, and commitment to advancing engineering knowledge support consideration for recognition within the Global Particle Physics Excellence Awards framework.[4]

Conclusion

Abdul Qadir’s academic portfolio demonstrates sustained involvement in multidisciplinary engineering research. His studies address practical challenges in materials science, renewable energy, and measurement technologies while incorporating modern computational techniques. These contributions reflect a developing research trajectory characterized by innovation, technical rigor, and scholarly engagement across multiple domains.

References

    1. ORCID. (2026). Abdul Qadir (0009-0004-3993-692X) researcher profile. ORCID Registry.
      https://orcid.org/0009-0004-3993-692X
    2. Qadir, A., & Asmatulu, R. (2026). Comprehensive Review of Hard Ceramic Coatings for Aerospace Alloys: Fabrication, Characterization and Future Perspectives. Journal of Manufacturing and Materials Processing.
      https://doi.org/10.3390/jmmp10050179
    3. Qureshi, M. H., Qadir, A., & Tien, W. H. (2024). A Novel Technique to Resolve Directional Ambiguity for Particle Streak Velocimetry. Flow Measurement and Instrumentation.
      https://doi.org/10.1016/j.flowmeasinst.2024.102712
    4. Urgesa, M. H., Putra, D. F. A., Qadir, A., Khan, U. A., Huang, T. C., Chiu, Y. X., & Lin, J. H. (2023). Photocatalytic Nitrogen Fixation on Semiconductor Materials: Fundamentals, Latest Advances, and Future Perspective. Green Energy and Technology.
      https://doi.org/10.1007/978-981-19-6748-1_3

Riasat Ali | Particle physics and cosmology | Editorial Board Member | 3098

Published on by Physicist Particle

Editorial Board Member

Riasat Ali
Riasat Ali
Affiliation Shanghai University
Country China
Scopus ID 57212863194
Documents 76
Citations 1,159
h-index 20
Subject Area Particle Physics and Cosmology
Event Global Particle Physics Excellence Awards
ORCID Connected via Scopus

Riasat Ali is a researcher affiliated with Shanghai University, China, whose academic work focuses on particle physics, cosmology, black hole physics, gravitation, and related theoretical investigations. His research profile demonstrates continuous scholarly engagement in contemporary astrophysical and gravitational studies, particularly in modified gravity models, plasma effects on black hole shadows, and quantum gravity-inspired thermodynamics.[1]

Abstract

This article presents an overview of the academic profile and scholarly contributions of Riasat Ali in the fields of particle physics and cosmology. His research portfolio includes investigations into black hole thermodynamics, plasma-induced gravitational lensing, Hawking radiation, and modified gravity theories. Through publications in recognized international journals, his work contributes to ongoing discussions in theoretical astrophysics and gravitational physics.[1][2]

Keywords

Particle physics, cosmology, black hole physics, Horndeski gravity, Hawking radiation, plasma physics, gravitational lensing, modified gravity, astrophysics, quantum gravity.

Introduction

Theoretical particle physics and cosmology continue to provide important frameworks for understanding gravitational phenomena, spacetime geometry, and high-energy astrophysical systems. Researchers working in these areas frequently examine black hole behavior, quantum corrections, and observational signatures associated with relativistic environments. Riasat Ali has contributed to these themes through studies involving black hole shadows, photon deflection, and thermodynamic properties within alternative gravity frameworks.[2]

Research Profile

According to Scopus author records, Riasat Ali has authored or co-authored 76 indexed documents and accumulated more than 1,159 citations with an h-index of 20. His publications primarily focus on gravitational physics, black hole thermodynamics, plasma effects in astrophysical systems, and modified theories of gravity.[1]

His recent works examine topics such as charged hairy black holes in Horndeski gravity, photon deflection in dispersive media, and generalized uncertainty principle corrections in black hole systems. These investigations contribute to the broader understanding of relativistic astrophysical environments and quantum-inspired gravitational models.[2]

Research Contributions

  • Investigated unstable equilibrium and chaos-bound violations in charged hairy black holes within Horndeski gravity frameworks.
  • Studied photon deflection and black hole shadow formation under the influence of plasma and dispersive media.
  • Explored Hawking temperature corrections and thermodynamic properties associated with generalized uncertainty principles.
  • Published research associated with modified gravity theories including Rastall gravity and f(Q,BQ) gravity models.

Publications

  1. “Unstable equilibrium and chaos-bound violation for a charged hairy black hole in Horndeski gravity,” New Astronomy, 2026.
  2. “Deflection of photon and shadow cast for black hole spacetime under the impact of a dispersive medium,” Indian Journal of Physics, 2026.
  3. “Greybody Factor and Hawking Temperature of ModMax-AdS Black Holes Surrounded by Perfect Fluid Dark Matter,” Fortschritte Der Physik, 2025.
  4. “Exploring plasma and dark matter on photon deflection by Reissner–Nordström black hole with scalar hair and its shadow,” Annals of Physics, 2025.

Research Impact

The research contributions of Riasat Ali demonstrate interdisciplinary engagement between cosmology, astrophysics, and gravitational theory. His publication metrics and citation record indicate continued academic visibility within theoretical physics communities. The integration of plasma physics, dark matter models, and quantum corrections into black hole studies reflects current directions in modern gravitational research.[1]

Award Suitability

Riasat Ali’s research profile aligns with the objectives of the Global Particle Physics Excellence Awards, particularly in the recognition of emerging contributions to theoretical particle physics and cosmology. His sustained publication activity, citation impact, and involvement in advanced gravitational studies support his suitability for editorial and scholarly recognition within the international academic community.

Conclusion

Riasat Ali has established a notable academic presence in the domains of particle physics and cosmology through research on black hole dynamics, modified gravity, and relativistic astrophysics. His scholarly activities, publication output, and citation performance indicate active participation in contemporary theoretical physics research and continued contribution to advancing cosmological understanding.

References

  1. Elsevier. (2026). Scopus author details: Riasat Ali, Author ID 57212863194. Scopus.
    http://scopus.com/authid/detail.uri?authorId=57212863194
  2. Ali, R. H. (2026). Unstable equilibrium and chaos-bound violation for a charged hairy black hole in Horndeski gravity. New Astronomy.
    10.1016/j.newast.2026.102564
  3. Ali, R. H. (2025). Exploring plasma and dark matter on photon deflection by Reissner–Nordström black hole with scalar hair and its shadow. Annals of Physics.
    https://doi.org/10.1016/j.aop.2025.170201
  4. Google Scholar. (2026). Riasat Ali citation profile.
    https://scholar.google.com/citations?user=Stp2lpMAAAAJ&hl=en

Waheeba Al-Amrani | Particle Experiments | Women Researcher Award

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Prof. Waheeba Al-Amrani | Particle Experiments | Women Researcher Award 

Prof. Waheeba Al-Amrani, Ibb University, Yemen

Prof. Waheeba Al-Amrani is a distinguished scholar and academic at Ibb University, Yemen. She holds a Ph.D. in Environmental Chemistry from Universiti Sains Malaysia, where her groundbreaking work focused on bioregeneration of modified adsorbents for wastewater treatment. With a Master’s degree in Physical Chemistry from Menoufia University and a Bachelor’s degree in General Chemistry from Ibb University, she has consistently demonstrated academic excellence.

Her research interests lie in developing innovative, low-cost solutions for pollutant removal, including advanced adsorption and bioremediation techniques. An accomplished educator and mentor, she has published 26 research papers and actively contributes to the academic and research communities through seminars, conferences, and teaching.

PROFILE

Scopus Profile

Educational Detail

Ph.D. in Environmental Chemistry: Universiti Sains Malaysia (USM), Pulau Penang, Malaysia, 2014
Dissertation: “Bioregeneration of mono amine modified silica and granular activated carbon loaded with mono-azo dyes in batch system.”

M.Sc. in Physical Chemistry: Menoufia University, Sheibin Alkoum, Menoufia, Egypt, 2009
Graduated with Excellence and Honors.
Thesis: “Removal of azo dyes using modified silica.”

B.Sc. in General Chemistry: Ibb University, Yemen, 2001
Graduated First Class with Honors.

Professional Experience

Prof. Waheeba Al-Amrani has extensive experience as a researcher and academic, contributing significantly to the fields of environmental and physical chemistry. She has been actively teaching both practical and theoretical chemistry courses at the undergraduate level at Ibb University, Yemen. Additionally, she supervises final-year research projects, mentoring students in innovative approaches to wastewater treatment and pollutant removal.

As a researcher, Prof. Al-Amrani has gained expertise in adsorption processes, employing various materials such as activated carbon and silica gel. Her work involves cultivating usable biomass, studying bioregeneration of loaded adsorbents, and applying these methodologies in advanced wastewater treatment technologies. She is proficient in using analytical techniques, including XRD, SEM, BET, EDX, HPLC, FTIR, and spectrophotometric analysis.

Prof. Al-Amrani has presented her research findings at numerous national and international conferences and seminars and has authored 26 publications in peer-reviewed journals.

Research Interests

Development of low-cost adsorbents for the removal of organic and inorganic pollutants, including mercury and anionic azo dyes, from aqueous solutions.

Bioregeneration of adsorbents, particularly granular activated carbon and modified silica, loaded with phenolic and azo dye pollutants.

Bacteria acclimation for bioremediation of wastewater, with a focus on phenolic and azo dye pollutants.

Employing advanced analytical techniques and quantum chemistry to evaluate adsorption and bioregeneration processes.

Top Notable Publications

Alkoshab, M.Q., Al-Amrani, W.A., Drmosh, Q.A., Onaizi, S.A. (2024). Zeolitic imidazolate framework-8/layered triple hydroxide composite for boosting the adsorptive removal of acid red 1 dye from wastewater. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 699, 134637.

Iddrisu, M., Al-Amrani, W.A., Merghani, A.A., Drmosh, Q.A., Onaizi, S.A. (2024). Effects of detergent on enzyme adsorption onto solid surfaces. Emergent Materials, 7(5), 2079–2086.

Al-Amrani, W.A., Onaizi, S.A. (2024). Adsorptive removal of heavy metals from wastewater using emerging nanostructured materials: A state-of-the-art review. Separation and Purification Technology, 343, 127018.

Bahadi, S.A., Iddrisu, M., Al-Sakkaf, M.K., Zahid, U., Onaizi, S.A. (2024). Optimization of methyl orange adsorption on MgFeAl-LTH through the manipulation of solution chemistry and synthesis conditions. Emergent Materials, 7(3), 959–971.

Bahadi, S.A., Iddrisu, M., Al-Sakkaf, M.K., Drmosh, Q.A., Onaizi, S.A. (2024). Chemically versus thermally reduced graphene oxide: Effects of reduction methods and reducing agents on the adsorption of phenolic compounds from wastewater. Emergent Materials, 7(2), 533–545.

Aziz, N.A.A., Hir, Z.A.M., Khalir, W.K.A.W.M., Al-Amrani, W.A., Hanafiah, M.A.K.M. (2024). Simultaneous adsorption of rare earth metal ions on chitosan-coated fumed silica – Characterization, kinetics, and isotherm studies. Ecological Engineering and Environmental Technology, 25(6), 172–187.

Hussin, S.M., Al-Amrani, W.A., Suah, F.B.M., Harimu, L., Hanafiah, M.A.K.M. (2024). Hydrogen peroxide treated desiccated coconut waste as a biosorbent in malachite green removal from aqueous solutions. Journal of Ecological Engineering, 25(3), 323–333.

Ganiyu, S.A., Suleiman, M.A., Al-Amrani, W.A., Usman, A.K., Onaizi, S.A. (2023). Adsorptive removal of organic pollutants from contaminated waters using zeolitic imidazolate framework composites: A comprehensive and up-to-date review. Separation and Purification Technology, 318, 123765.