Prof. Dehui WanTaiwan
National Tsing Hua University
Current Position
2023/08 to presentProfessor, Institute of Biomedical Engineering, NTHU
Academic Experiences
2011 - 2012Postdoctoral research fellow, Department of Biomedical Engineering, Georgia Institute of Technology
Past Professional Experiences
2017/08 - 2023/07Associate Professor, Institute of Biomedical Engineering, NTHU
2013/02 - 2017/07Assistant Professor, Institute of Biomedical Engineering, NTHU
Honors and Awards
2025Research Scholar Award from the Dr. Chau-Jen Lee Biomedical Engineering Foundation
2024Young Chemist Award from Chemical Society Located in Taipei
2024, 2025Future Tech Award
Specialty & Expertise
Nanomaterials for Sensing, Healthcare, and Energy
Others

Unlocking the Potential of Surface-Enhanced Raman Scattering in Precision Medicine


TBA TBA Japan-Taiwan Joint-Session on Materials and Structures/TBA

​Surface-enhanced Raman spectroscopy (SERS) biosensors offer label-free, ultrasensitive detection with unique molecular fingerprinting, promising significant advances in precision medicine. This talk introduces a novel wafer-scale, portable, and highly uniform SERS platform fabricated on cost-effective paper substrates containing densely packed gold nanoparticles. These nanoparticles are precisely deposited onto fluorosilane-modified cellulose fibers through a controlled thermal evaporation process, meticulously managing atom diffusion to achieve an exceptional Raman enhancement factor of 3.9 × 10¹¹, enabling single-molecule sensitivity. The portability and user-friendliness of our system allow for excellent reproducibility, demonstrated by a low relative standard deviation (RSD) of only 3.97%. Remarkably sensitive, the platform can detect analytes at sub-femtomolar concentrations, reaching detection limits as low as 16.2 parts per quadrillion (ppq) for nicotine. Its versatility has been validated across a wide spectrum of analytes, including clinical medicines, pesticides, environmental carcinogens, and illegal drugs, underscoring its robust adaptability for varied diagnostic scenarios. Potential clinical applications include therapeutic drug monitoring for personalized medication adjustments, rapid and ultra-early diagnosis of pesticide intoxication, and non-invasive, pain-free sampling of interstitial fluids via a microneedle-assisted approach. Furthermore, integration with advanced deep learning algorithms can substantially enhance analytical performance, enabling sophisticated clinical prognostic diagnostic tools and significantly boosting its practicality for future precision medicine initiatives.​

Organizer