Department of Chemical and Biomolecular Engineering
Korea Advanced Institute of Science and Technology


Do Hyun Kim (김도현)


Tel : +82-42-350-3929
Fax : +82-42-350-3910
E-mail :
Homepage :
- 1990 : MlT (Sc.D. in Chem. Eng.)
- 1981 : KAIST (M.S. in Chem. Eng.)
- 1979 : Seoul National Univ. (B.S. in Chem. Eng.)

Employment and Professional Experience
- 1991 ~ Present : Professor, KAIST
- 2006 ~ 2007 : Visiting Professor, Harvard Medical School
- 1998 ~ 1999 : Visiting Professor, U.C. Santa Barbara
- 1990 ~ 1991 : Research Associate, MIT
- 1981 ~ 1984 : Researcher, Chemical Process Lab., KIST

Awards and Honors
- Beomseok Excellent Paper Award, The Korean Institute of Chemical Engineers (1996)
- SNU Presidential Award, Seoul National University (1979)

Research interests
- Microfluidic systems: Fabrication and numerical analysis of mcirofluidic systems for the application in chemical and biological processes
- Numerical simulations: Numerical simulation of protein folding phenomena and semiconductor processes.
- Systems biology: Model validation and parameter estimation in biological pathways and pharmacokinetic model
- Biological detection: Development of new materials and devices for biological detection
- Semiconductor processing technology: Flexible display

Selected Publications
1. Computational study of the axial instability of rimming flow using Arnoldi method, Int. J. Num. Meth. Fluids, 53, 691-711 (2007).
2. Removal of urea from urea-rich protein samples using metal ions in a microfluidic device, Process Biochemistry, 42, 649-654 (2007) (with Prof. W. H. Hong).
3. Protein nanopatterns and biosensors using gold binding polypeptide as a fusion partner, Analytical Chemistry, 78, 7197-7205 (2006) (with Prof. S. Y. Lee)
4. Sputtering of Fe(100) due to low-energy ion bombardments: Molecular dynamics simulation, Scripta Materialia, 55, 1043-1046 (2006).
5. Effect of Process Parameters of UV-Assisted Gas-Phase Cleaning on the Removal of PEG(Polyethyleneglycol) from a Si Substrate, J. Korean Physical Society, 49, 1421-1427 (2006).
6. Roles of physical interactions in determining protein-folding mechanisms: Molecular simulation of protein G and α Spectrin SH3, PROTEINS: Structure, Function, and Bioinformatics, 55, 128-138 (2004).

Process Analysis Laboratory
Functional Nano Particles, Microfluidic Systems, Microelectronic Processes, Process Modeling and Simulation, Systems Biology, Transport Phenomena, Resource Recycling Processes

■ Separation processes in a microfluidic system 

Microfluidic system has several advantages in small scale chemical and biological processes. Several separation processes on microfluidic systems are under development including isoelectric focusing, extraction and simple distillation processes. Also, we are working on developing novel fluid handling methods including electrowetting and electrokinetic control of drops.

Fig.1. (a) Layout for dynamic concentration of proteins, (b) Electrokinetic generation of a single drop,
(c) Drop movement on the array of electrowetting electrodes.


■ Chemical and biological detection 

The detection of specific biological molecules becomes more and more important with the development of biology into molecular regime dealing with a single cell rather than population of cells. We are developing materials and devices for the detection of biological species, including HBV surface antigen.

Fig. 2. (a) MWCNT sensor for kinase assay, (b) Micro ELISA for detecting HBV antigen using
anodic aluminum oxide, (3) Micro/nano composite particle for antigen-antibody detection


■ Mathematical modeling and numerical analysis 

Our research in this area includes numerical analysis of protein folding phenomena, transport processes in a microfluidic systems and semiconductor processes. Example of mathematical modeling is shown for the sequence of protein folding.

Fig.3 Sequence of protein folding of a globular protein


■ Model validation and parameter estimation in biological pathways and pharmacokinetic model 

We are developing procedures for the systematic validation of pathway model and parameter estimation to understand the pathways for the death and survival of the cancer cells.  

Figure is shown for TNF (Tumor Necrosis Factor) pathway.