Mateen Ullah
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Also affiliated: Quaid-i-Azam University (2018); Creative Technology (Singapore) (2024); 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); New Leaf Center (2025); University of Science and Technology Bannu (2019–2025)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Mateen Ullah's research focuses on the development and characterization of materials for sensor applications, particularly humidity sensors. He has investigated organic compounds, Schiff bases, and metal complexes, such as Zinc(II) bis(dithiocarbamate), for their electrical and hysteretic properties in relation to water molecules. His work also extends to ferroelectric and dielectric materials, including AgTaO3 and Li-doped BNBT ceramics, exploring their electrical and strain properties for applications in energy storage. Ullah has also utilized machine learning techniques to analyze variables impacting private participation in infrastructure projects. His scholarly output includes 30 publications, with an h-index of 6 and 238 citations. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Awais Riaz, Ainong Shi, Renuka Khanal, and Kenani Chiwina, on several shared publications.
Metrics
- h-index: 6
- Publications: 32
- Citations: 246
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 Characterization of VDAC Gene Family in Soybean (Glycine max L.) and In Silico Expression Profiling in Response to Drought and Salt Stress (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 Stress
- 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 Stress
- 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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