Papers

3

Total Citations

13

H-Index

2

About

Nur Azmah Nordin is a rising researcher at the forefront of intelligent materials for soft robotics and sensor technology. Her work centers on magnetorheological (MR) foams and hydrogels—smart composites whose mechanical properties can be controlled by magnetic fields. Nordin’s major contributions include pioneering the use of machine learning to predict the magnetostriction behavior of MR foams, a critical step for developing precise soft actuators. Her most cited paper (2022, 8 citations) demonstrates how extreme learning machines can model these complex materials, offering a powerful shortcut for design and optimization. She has also advanced force-sensing performance by integrating graphite into hydrogel-based magnetorheological plastomers, expanding the potential for flexible, responsive sensors in robotic systems. Her latest work (2025) provides deep structural and viscoelastic insights into MR foams, revealing how constraint volume foaming dramatically boosts storage modulus—a key finding for tailoring material stiffness. With a growing citation record and a focus on bridging computational modeling with experimental material science, Nordin is establishing herself as a key innovator in the next generation of adaptive, soft-matter devices.

Research Focus

Key Achievements

2
H-Index
3
Papers
13
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Prediction for magnetostriction magnetorheological foam using machine learning method
8 citations · 2022
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 13
🏛 Institutions: University of Technology Malaysia, Universitas Gadjah Mada, University of Kuala Lumpur

Top Papers

  1. 1
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago