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