Matoke Peter Mose

Dr. Matoke Peter Mose

National Taiwan University of Science and Technology
orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 July 2025 in Materials Chemistry and Physics
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MMMatoke Peter Mose
Matoke Peter Mose
Sathiyalingam KannaiyanSathiyalingam Kannaiyan
Sathiyalingam Kannaiyan
Song-Jeng HuangSong-Jeng Huang
Song-Jeng Huang

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Song-Jeng HuangSong-Jeng Huang
Song-Jeng Huang
MMMatoke Peter Mose
Matoke Peter Mose
Sathiyalingam KannaiyanSathiyalingam Kannaiyan
Sathiyalingam Kannaiyan
The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.


Verified
Journal Article
Open access
PDF file
Song-Jeng HuangSong-Jeng Huang
Song-Jeng Huang
Sathiyalingam KannaiyanSathiyalingam Kannaiyan
Sathiyalingam Kannaiyan
Manas SarkarManas Sarkar
Manas Sarkar
MMMatoke Peter Mose
Matoke Peter Mose
The automobile industry uses magnesium for load-bearing components due to its low density, durability, and ductility. This study investigated a nanocomposite containing Nb2O5 (3 and 6 wt%) nanoparticles as reinforcement with AZ31 magnesium alloy made by stir casting. A severe plastic deformation was conducted on the cast samples via equal-channel angular pressing (ECAP) after homogenization at 410 °C for 24 h and aging at 200 °C for 10 h. The microstructural distributions and mechanical properties of the magnesium metal matrix composites (MMCs) reinforced with Nb2O5 nanoparticles were investigated via ECAP. With the increase in the number of ECAP passes, the grain sizes became uniform, and the size of secondary phases reduced in the pure Nb2O5/AZ31 MMC. The grain size decreased remarkably after the ECAP process from 31.95 µm to 18.41µm due to the dynamic recrystallization during plastic deformation. The mechanical properties of hardness, ultimate tensile strength, and elongation effectively improved after each ECAP pass. The maximum values achieved for the Nb2O5/AZ31 composite subjected to ECAP were 64.12 ± 12 HV, 151.2 MPa, and 52.71%.

6 April 2026 in Journal of Materials Research and Technology
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Journal Article
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Song-Jeng HuangSong-Jeng Huang
Song-Jeng Huang
CWChih-Feng Wang
Chih-Feng Wang
YLYu-Quan Lee
Yu-Quan Lee
MMMatoke Peter Mose
Matoke Peter Mose