Mateen Ullah
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Also affiliated: Quaid-i-Azam University (2018); IBM (United States) (1994); Ayub Medical College (2025); Islamia College University (2019–2025); Rashid Latif Medical College (2026); Lady Reading Hospital (2025); University of Peshawar (2017–2020); University of Science and Technology Bannu (2019–2025); University of the West of Scotland (2024)
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Mateen Ullah's research has focused on materials science, investigating the properties of various ceramics and thin films. His work includes studies on lead-free piezoelectric ceramics, such as those based on BNT-ST and its solid solutions, examining their dielectric, ferroelectric, and piezoelectric characteristics for potential applications in energy storage and sensing. Ullah has also explored the properties of thin-film media, including Ba-ferrite and sendust films, for high-density magnetic recording. More recently, his research has extended to organic compound-based humidity sensors, investigating their electrical and sensing properties in response to water molecules. Ullah has published 32 papers, accumulating 249 citations, and has an h-index of 6. He collaborates with researchers at the University of Arkansas at Fayetteville, including Awais Riaz, Ainong Shi, Renuka Khanal, and Kenani Chiwina.
Metrics
- h-index: 6
- Publications: 32
- Citations: 249
Selected Publications
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Genome-wide association study and genomic prediction of fruit weight in spinach (2026)
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Genome-wide association study and genomic prediction of bolting trait in spinach (Spinacia oleracea L.) (2026)
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Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress (2025)
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Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and in-silico Expression Profiling in Response to Drought and Salt Stresses (2025)
Collaboration Network
Top Collaborators
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and in-silico Expression Profiling in Response to Drought and Salt Stresses
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and in-silico Expression Profiling in Response to Drought and Salt Stresses
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and in-silico Expression Profiling in Response to Drought and Salt Stresses
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and in-silico Expression Profiling in Response to Drought and Salt Stresses
- Genome-Wide Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
- Genome-wide association study and genomic prediction of fruit weight in spinach
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