Tong Zheng | Image Processing and Enhancement | Research Excellence Award

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Research Excellence Award

Tong Zheng
Affiliation Beijing Technology and Business University
Country China
ORCID
0000-0003-2251-6844
Documents 27
Subject Area Image Processing and Enhancement
Event
Global Tech Excellence Awards

Tong Zheng is a researcher affiliated with Beijing Technology and Business University, China, with scholarly contributions focused on image processing, image enhancement technologies, and computational visual analysis. The researcher has demonstrated academic engagement through peer-reviewed publications indexed in international databases, contributing to the advancement of digital image optimization methodologies and intelligent enhancement systems.[1]

Abstract

This article presents an academic overview of Tong Zheng and the researcher’s contributions to image processing and enhancement research. The profile evaluates scholarly productivity, publication visibility, citation indicators, and thematic contributions in computational imaging systems. The assessment also considers the researcher’s suitability for recognition under the Global Tech Excellence Awards framework based on measurable academic outputs and research relevance in emerging technological applications.[2]

Keywords

  • Image Processing
  • Image Enhancement
  • Computer Vision
  • Digital Imaging
  • Visual Computing
  • Computational Intelligence

Introduction

Image processing and enhancement have become critical research domains within computer science and artificial intelligence due to their broad applications in healthcare imaging, industrial automation, surveillance, and multimedia systems. Researchers working in this field contribute to the development of algorithms capable of improving image quality, extracting meaningful patterns, and supporting intelligent decision-making systems.[3]

Tong Zheng has contributed to this interdisciplinary research area through publications associated with digital image enhancement methodologies and computational visual systems. The researcher’s academic record reflects sustained participation in technological innovation and scholarly dissemination within indexed scientific platforms.[1]

Research Profile

The research profile of Tong Zheng demonstrates involvement in image enhancement, visual analytics, and digital processing technologies. The academic profile includes 27 indexed documents and measurable citation performance indicating growing visibility in computational imaging studies.[1]

The researcher’s publication record indicates interdisciplinary collaboration and technical specialization relevant to contemporary image enhancement applications. These research efforts align with emerging scientific priorities associated with machine intelligence, data interpretation, and adaptive visual systems.[4]

Research Contributions

Tong Zheng has contributed to the advancement of image enhancement algorithms and computational imaging methodologies through research involving image clarity optimization, feature extraction, and intelligent enhancement systems.[5]

The research contributions are relevant to applications requiring precision imaging, pattern recognition, and improved visual interpretation under varying environmental and computational conditions. Such contributions support technological progress in industrial imaging, multimedia analytics, and automated image processing environments.

Publications

Selected scholarly publications associated with Tong Zheng include contributions related to image enhancement systems, intelligent processing frameworks, and digital imaging technologies indexed in recognized scientific databases.[1]

  • Research involving computational image enhancement and adaptive filtering methodologies.[5]
  • Studies associated with digital image optimization and machine-assisted visual processing.
  • Scholarly contributions indexed through international scientific databases and researcher identity systems.[2]

Research Impact

The research impact associated with Tong Zheng can be observed through indexed publications, citation accumulation, and continued visibility within image processing scholarship. Citation metrics indicate that the researcher’s work has contributed to ongoing scientific discussions within computational imaging disciplines.[1]

The combination of publication productivity and interdisciplinary technical engagement supports the researcher’s growing academic profile within the field of image enhancement and intelligent processing systems.[4]

Award Suitability

Tong Zheng demonstrates characteristics consistent with eligibility for academic recognition under the Global Tech Excellence Awards. The researcher’s contributions to image processing and enhancement technologies reflect active scholarly participation in a technically significant and rapidly evolving scientific domain.

The combination of indexed research output, measurable citation indicators, and institutional affiliation with Beijing Technology and Business University supports the suitability of the researcher for consideration within technology-focused academic recognition programs.[1]

Conclusion

Tong Zheng has established an emerging scholarly presence within the field of image processing and enhancement through indexed publications, citation visibility, and interdisciplinary technological research activities. The academic profile reflects engagement with contemporary computational imaging challenges and demonstrates relevance to ongoing scientific developments in intelligent visual systems.[1]

Based on the available academic indicators and research focus areas, the researcher represents a suitable candidate for recognition within international technology and research excellence initiatives.

References

      1. ORCID. (n.d.). ORCID profile of Tong Zheng.
        https://orcid.org/0000-0003-2251-6844
      2. Semantic segmentation method for sparse point clouds based on straight flow completion and multi-feature fusion.
        https://www.mdpi.com/1424-8220/26/10/3056
      3. Task-driven pruning method for synthetic aperture radar target recognition convolutional neural network model.
        https://www.mdpi.com/1424-8220/25/10/3117
      4. A graph aggregation convolution and attention mechanism based semantic segmentation method for sparse lidar point cloud data.
        https://ieeexplore.ieee.org/document/10343142
      5. Global Tech Excellence Awards. (n.d.). Award evaluation and eligibility framework.
        https://globaltechexcellence.com/

     

Jecha Jecha | Education and Outreach in Computer Vision | Young Scientist Award

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Young Scientist Award

Jecha Jecha
Affiliation Zanzibar University
Country Tanzania
Scopus ID 60416225500
Documents 2
Citations 1
h-index 1
Subject Area Education and Outreach in Computer Vision
Event Global Tech Excellence Awards

Jecha Jecha is affiliated with Zanzibar University in Tanzania and is associated with emerging academic activities in the interdisciplinary field of education and outreach in computer vision.[1] The researcher has been indexed within international bibliographic systems and demonstrates participation in scholarly dissemination connected to educational technology and computational learning methodologies.[2]

Abstract

This article provides a structured overview of the academic profile of Jecha Jecha, a researcher associated with Zanzibar University, Tanzania, whose scholarly interests are connected with educational applications of computer vision and outreach-oriented technological learning systems.[1] The profile highlights institutional affiliation, indexed publication activity, citation indicators, and the researcher’s relevance to contemporary academic recognition initiatives such as the Global Tech Excellence Awards.

Keywords

Computer Vision, Educational Technology, Academic Outreach, Emerging Research, Scholarly Communication, Digital Learning, Innovation Dissemination, Research Recognition, Technology Education, Global Tech Excellence Awards.

Introduction

Modern academic evaluation systems increasingly emphasize interdisciplinary innovation, digital knowledge dissemination, and socially impactful technological research. Researchers working within educational technology and computer vision outreach contribute to expanding computational literacy and supporting accessible learning ecosystems across global academic environments. Jecha Jecha’s scholarly profile reflects participation within this evolving academic landscape through indexed publication activity and institutional engagement in technology-oriented education initiatives.[1]

Research Profile

Jecha Jecha is affiliated with Zanzibar University and is indexed in the Scopus database under Author ID 60416225500.[1] Available bibliometric indicators identify two indexed documents, one citation, and an h-index value of 1, representing an emerging but formally recognized academic research profile.[1] The associated subject area includes Education and Outreach in Computer Vision, reflecting interdisciplinary engagement between computational technologies and educational communication systems.

Research Contributions

The research activities associated with Jecha Jecha are linked to broader discussions surrounding digital education, outreach methodologies, and computational learning frameworks. Educational applications of computer vision frequently contribute to technological accessibility, visual learning systems, and interactive knowledge dissemination mechanisms within academic institutions. Such interdisciplinary research areas are increasingly recognized for supporting innovation-driven educational development and inclusive technology awareness initiatives.

Publications

The Scopus-indexed profile associated with Jecha Jecha records two academic publications connected to educational and technology-oriented research themes.[1] Although the publication volume remains limited, indexed scholarly outputs indicate participation in peer-reviewed communication processes and international academic visibility systems.

  • Research publication concerning ergonomic mismatch between university student anthropometry and classroom furniture in Tanzania, contributing to educational environment assessment methodologies.
  • Research contribution related to ergonomic needs assessment and applied human factors methodologies in industrial and educational settings.

Research Impact

Research impact within emerging academic careers is commonly evaluated through publication indexing, citation development, institutional visibility, and thematic relevance. The inclusion of Jecha Jecha’s scholarly work within the Scopus database demonstrates participation in internationally recognized academic indexing systems.[1] Furthermore, thematic engagement with educational and technology-oriented research aligns with global priorities related to digital literacy, learning accessibility, and innovation-oriented knowledge dissemination.

Award Suitability

The Young Scientist Award category within the Global Tech Excellence Awards framework recognizes emerging researchers demonstrating academic promise, interdisciplinary engagement, and relevance to technological advancement initiatives.[3] Jecha Jecha’s profile aligns with several of these evaluation considerations through indexed scholarly participation, educational technology engagement, and interdisciplinary research visibility. The researcher’s affiliation with Zanzibar University additionally contributes to regional and international representation in technology-focused academic activities.[2]

Conclusion

Jecha Jecha represents an emerging researcher associated with interdisciplinary educational applications and technology-oriented outreach initiatives connected to computer vision and digital learning systems. Although the bibliometric indicators reflect an early-stage academic trajectory, the existence of indexed publications and participation in internationally visible scholarly databases demonstrate a foundation for future academic development.[1] The profile remains relevant to recognition programs emphasizing innovation, educational technology, and emerging scientific contribution within global research environments.[3]

References

      1. Elsevier. (n.d.). Scopus author details: Jecha Jecha, Author ID 60416225500. Scopus.
        https://www.scopus.com/authid/detail.uri?authorId=60416225500
      2. Zanzibar University. (n.d.). Institutional academic and research information.
        https://zanvarsity.ac.tz/
      3. Global Tech Excellence Awards. (n.d.). Award categories and academic recognition framework.
        https://globaltechexcellence.com/

Dr. Divya A | Image Processing and Enhancement | Research Excellence Award

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Quentin Marc Anaba Fotze | Image Processing and Enhancement | Best Paper Award

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Dr. Quentin Marc Anaba Fotze | Image Processing and Enhancement | Best Paper Award

Institute for Geological and Mining Research | Cameroon

Dr. Quentin Marc Anaba Fotze is a geophysicist at the Université de Maroua, Cameroon, specializing in applied geophysics, remote sensing, and geospatial analysis for mineral and groundwater exploration. He has authored 9 indexed publications with 33 citations h-index: 3 and contributed to over scientific works, demonstrating strong collaboration across multidisciplinary teams. His research integrates aeromagnetic, gravity, and satellite data to map tectonic structures, mineralization zones, and groundwater potential in Central Africa. He has also contributed to national geological mapping initiatives, supporting resource management, infrastructure development, and sustainable environmental planning in data-scarce regions.

Citation Metrics (Scopus)

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     View ResearchGate Profile

Featured Publications

Mohamed Ali Hajjaji | Applications of Computer Vision | Top Researcher Award

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Prof. Mohamed Ali Hajjaji | Applications of Computer Vision | Top Researcher Award

ISSAT De Sousse | University of Sousse | Tunisia

Prof. Mohamed Ali Hajjaji is a distinguished researcher at the Institut Supérieur des Sciences Appliquées et de Technologie de Sousse, Tunisia, specializing in FPGA-based systems, artificial intelligence, cryptography, and intelligent infrastructure monitoring. He is a key member of the PEJC 2025 project “Intelligent RoadGuard”, funded by the Tunisian Ministry of Higher Education and Scientific Research. With 71 publications cited over 608 times and an h-index of 16, his work spans hardware acceleration of neural networks, chaos-based cryptosystems, and real-time image processing. Collaborating with over 49 co-authors internationally, his research delivers practical solutions for autonomous systems, secure communications, and smart transportation, impacting both technology and societal safety.

 

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           View Google Scholar Profile
       View Research Gate Profile

Featured Publications

Tao Chen | Object Detection and Recognition | Research Excellence Award

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Dr. Tao Chen | Object Detection and Recognition | Research Excellence Award

Professor | Fudan University | China

Dr. Tao Chen is a leading researcher at Fudan University, specializing in deep learning and computer vision, with a focus on human motion understanding, 3D shape generation, and semantic segmentation. He has contributed to over 249 high-impact publications in top-tier venues including CVPR, NeurIPS, and IEEE Transactions, accumulating more than 6294 citations. His work integrates advanced neural architectures, motion diffusion, and cross-domain adaptation techniques, often in collaboration with international researchers such as G. Yu and W. Liu. Dr. Chen’s research has significant societal impact, advancing intelligent systems for medical imaging, autonomous perception, and interactive 3D applications, bridging fundamental AI research with practical real-world solutions.

Citation Metrics (Google Scholar)

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6294

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249

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           View Research Gate Profile

Featured Publications


Executing your commands via motion diffusion in latent space.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . (2023). Cited By : 580

TopFormer: Token pyramid transformer for mobile semantic segmentation.

-In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 388

b‑DARTS: Beta‑decay regularization for differentiable architecture search.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 194

LL3DA: Visual interactive instruction tuning for omni‑3D understanding, reasoning, and planning.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2024). Cited By: 178

Zeng Gao | Applications of Computer Vision | Research Excellence Award

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Dr. Zeng Gao | Applications of Computer Vision | Research Excellence Award

Lecturer | Henan University of Technology | China 

Dr. Zeng Gao is a researcher at Henan University of Technology specializing in machine learning, image processing, and visual tracking. His work focuses on intelligent optimization–driven visual tracking and motion analysis, with influential contributions to abrupt and long-term tracking. He has published over 12 peer-reviewed papers in leading international journals and conferences, including IEEE Access, Expert Systems with Applications, Applied Soft Computing, Digital Signal Processing, and PRCV, accumulating 98 citations. He has participated in two National Natural Science Foundation of China projects and holds three granted invention patents. Dr. Gao actively collaborates with domestic and international institutions, serves as a reviewer for journals such as ACM TOMM and Digital Signal Processing, and contributes to advancing intelligent perception technologies with real-world societal impact.

 

Citation Metrics (Scopus)

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           View ORCID Profile
      View Google Scholar Profile

Featured Publications


Visual tracking with levy flight grasshopper optimization algorithm.

– Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV  (2019). Cited By : 19

Paulo Dias | Augmented Reality (AR) and Virtual Reality (VR) | Best Researcher Award

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Prof. Paulo Dias | Augmented Reality (AR) and Virtual Reality (VR) | Best Researcher Award

Auxiliar with Habilitation | University of Aveiro | Portugal

Prof. Paulo Dias is a distinguished researcher at the University of Aveiro, Portugal, whose pioneering work spans 3D reconstruction, computer vision, virtual and augmented reality (VR/AR), human–computer interaction (HCI), and robotics. With an extensive record of over 506 scientific publications, he has achieved 4,802 total citations, an h-index of 33, and an i10-index of 128, reflecting a profound and sustained influence on the global research community.His work has notably advanced immersive technologies and their applications in healthcare, education, and industrial environments. Among his most cited studies is Head-mounted display versus desktop for 3D navigation in virtual reality Multimedia Tools and Applications, a landmark comparative study on immersive interaction. His research on Using virtual reality to increase motivation in post-stroke rehabilitation IEEE Computer Graphics and Applications, demonstrates his commitment to applying VR to rehabilitation and assistive technologies, enhancing patient engagement and recovery outcomes.Prof. Dias has also co-authored the influential A conceptual model and taxonomy for collaborative augmented reality IEEE Transactions on Visualization and Computer Graphics, providing a robust framework for understanding and designing AR-based collaborative systems. His contributions to autonomous vehicle sensor calibration, situated visualization for decision-making, and remote collaboration in industrial contexts further illustrate his multidisciplinary impact and innovation-driven research agenda.Through collaborative projects with experts such as B. Sousa Santos and B. Marques, Prof. Dias continues to bridge the gap between technological innovation and human experience, integrating digital environments with real-world problem-solving. His body of work not only advances scientific understanding but also fosters societal progress through the development of intelligent, accessible, and immersive technologies that redefine how humans interact with digital information and environments.

Profiles: Google Scholar | ORCID | ResearchGate

Featured Publications

1.Madeira, T., Oliveira, M., & Dias, P. (2025). Reflection-aware 3D mirror segmentation and pose estimation.
Cited By : 2

2. Oliveira, S., Marques, B., Amorim, P., Dias, P., & Sousa Santos, B. (2024). Stepping into recovery with an immersive virtual reality serious game for upper limb rehabilitation: A supermarket experience for stroke survivors. In International Conference on Human-Computer Interaction . Cited By : 13

3. Madeira, T., Oliveira, M., & Dias, P. (2024). Neural colour correction for indoor 3D reconstruction using RGB-D data. Sensors, 24(13), 4141. Cited By : 3

4. Maio, R., Araújo, T., Marques, B., Santos, A., Ramalho, P., Almeida, D., & Dias, P. (2024). Pervasive augmented reality to support real-time data monitoring in industrial scenarios: Shop floor visualization evaluation and user study. Computers & Graphics, 118, 11–22. Cited By : 30

5. Marques, B., Silva, S., Maio, R., Dias, P., & Sousa Santos, B. (2024). Guidelines for designing mixed reality solutions in remote scenarios. In International Conference on Human-Computer Interaction. Cited By : 4

Prof. Paulo Dias’s pioneering research in 3D vision, virtual and augmented reality, and human–computer interaction is transforming how humans engage with digital environments. His innovations bridge science and society by driving advancements in healthcare rehabilitation, immersive learning, and industrial automation, fostering a more intelligent and inclusive digital future.

Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

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Dr. Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Assistant Researcher | Zhejiang Lab | China

Dr. Xiangfu Kong is a distinguished researcher at Zhejiang Lab, specializing in intelligent transportation systems (ITS), spatiotemporal data analytics, and urban mobility optimization. His work bridges computer science, artificial intelligence, and transportation engineering to develop data-driven models that enhance mobility efficiency safety, and sustainability in smart cities.With an Publications 6  h-index of 3, and 67 citations across recognized publications, Dr. Kong has made notable scholarly contributions to the field. He has published six peer-reviewed research articles, including influential works such as “Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index and  A Scenario-Based Map-Matching Algorithm for Complex Urban Road Networks. His recent studies explore flood risk mapping, travel time reliability, and natural language processing for urban data interpretation, showcasing his interdisciplinary expertise.Dr. Kong’s research projects often involve large-scale real-world data, particularly GPS-based urban mobility and hydrological data, integrating AI algorithms and Bayesian frameworks to model and predict transportation dynamics under diverse conditions. His studies have direct implications for urban policy-making, disaster management, and infrastructure resilience.He has actively collaborated with industry and academic partners to design computational models that assist in traffic monitoring, path planning, and flood management, contributing to sustainable urban development initiatives. Dr. Kong’s innovative use of AI for understanding urban systems highlights his dedication to applying research outcomes to societal benefit.In addition to his publications, Dr. Kong contributes to the broader scientific community through editorial and peer-review roles in transportation and data science journals. His ongoing work in data-driven transportation intelligence and urban informatics positions him as a promising researcher contributing to the next generation of smart mobility systems.Through his research excellence and cross-disciplinary collaborations, Dr. Xiangfu Kong continues to push the boundaries of how AI and data analytics can transform urban transportation, improve public safety, and drive global sustainability efforts.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Kong, X., Yang, J., & Yang, Z. (2015). Measuring traffic congestion with taxi GPS data and travel time index. Proceedings of the CICTP 2015, 3751–3762. Cited By : 35

2. Kong, X., & Yang, J. (2019). A scenario-based map-matching algorithm for complex urban road network. Journal of Intelligent Transportation Systems, 23(6), 617–631.
Cited By : 19

3. Kong, X., Yang, J., Qiu, J., Zhang, Q., Chen, X., Wang, M., & Jiang, S. (2022). Post‐event flood mapping for road networks using taxi GPS data. Journal of Flood Risk Management,  Cited By : 8

4. Xiangfu, K., Bo, D., Xu, K., & Yongliang, T. (2023). Text classification model for livelihood issues based on BERT: A study based on hotline compliant data of Zhejiang province. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(3), 456–466. Cited By : 3

5. Kong, X., & Yang, J. (2016). Path planning with information on travel time reliability. Proceedings of the CICTP 2016, 99–107. Cited By :  2

6. Kong, X., Yang, J., Xu, K., Dong, B., & Jiang, S. (2023). A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data. Hydrology and Earth System Sciences Discussions, 1–25.

Dr. Xiangfu Kong nresearch advances data-driven intelligent transportation and urban informatics, fostering safer, more efficient, and sustainable mobility systems. His innovative integration of AI, GPS analytics, and hydrological modeling contributes to scientific progress, climate-resilient infrastructure, and smart city innovation with lasting global impact.

Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

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Prof. Dr. Venkataraman Thangadurai | 3D Computer Vision | Best Researcher Award

Professor | University of St Andrews | United Kingdom

Prof. Dr. Venkataraman Thangadurai is a globally renowned expert in solid-state chemistry, electrochemical energy storage, and advanced battery technologies. With a research focus on fast ion conductors, solid electrolytes, lithium- and sodium-based batteries, and fuel cell materials, he has made pioneering contributions to both fundamental science and practical energy solutions. Prof. Thangadurai has authored over 278 peer-reviewed journal articles, 6 book chapters, and 21 conference proceedings, and has delivered 180 conference presentations, 83 posters, and 80 invited talks at top universities, institutes, and companies worldwide. His work has resulted in 13 patents/patent applications and has placed him among the top 1% of authors in Royal Society of Chemistry journals by citations in 2020. As of March 2025, his research has received 25,991 citations with an h-index of 69, reflecting the high impact of his work globally.He is the Founder and Advisor of Ions Storage Systems, Maryland, USA (2012–present) and Founder and Director of Superionics, Calgary, Canada (2021–present), translating cutting-edge research into commercial energy storage technologies. His research highlights include optimizing lithium nucleation overpotentials in garnet-based hybrid solid-state batteries, developing doped sodium gadolinium silicate ceramics for fast Na⁺ conduction, and enhancing electrocatalysts for lithium–sulfur batteries.Prof. Thangadurai collaborates extensively with leading international researchers and institutions, including the University of Calgary, University of Maryland, University of St Andrews, University of Kiel, Yale University, and ANSTO, advancing cross-disciplinary solutions in energy materials. Beyond his scientific contributions, he mentors emerging scientists and actively promotes innovation that addresses global energy challenges. His work has significant societal impact, enabling safer, high-performance, and sustainable energy storage solutions critical for electric mobility, grid storage, and renewable energy integration.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1. Murugan, R., Thangadurai, V., & Weppner, W. (2007). Fast lithium ion conduction in garnet-type Li₇La₃Zr₂O₁₂. Angewandte Chemie International Edition, 46(41), 7778–7781.
Cited By : 3691

2.Han, X., Gong, Y., Fu, K., He, X., Hitz, G. T., Dai, J., Pearse, A., Liu, B., Wang, H., … Thangadurai, V. (2017). Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nature Materials, 16(5), 572–579. Cited By : 2088

3.Thangadurai, V., Narayanan, S., & Pinzaru, D. (2014). Garnet-type solid-state fast Li ion conductors for Li batteries: Critical review. Chemical Society Reviews, 43(13), 4714–4727. Cited By : 1712

4.Pal, B., Yang, S., Ramesh, S., Thangadurai, V., & Jose, R. (2019). Electrolyte selection for supercapacitive devices: A critical review. Nanoscale Advances, 1(10), 3807–3835.
Cited By : 1229

5.Wang, C., Fu, K., Kammampata, S. P., McOwen, D. W., Samson, A. J., Zhang, L., … Thangadurai, V. (2020). Garnet-type solid-state electrolytes: Materials, interfaces, and batteries. Chemical Reviews, 120(10), 4257–4300. Cited By : 1130

Prof. Dr. Venkataraman Thangadurai  pioneering research in solid-state chemistry and advanced battery technologies drives global innovation in energy storage, enabling safer, high-performance, and sustainable batteries that power electric mobility, renewable energy integration, and next-generation clean technologies.