RESEARCH AREAS

Energy

Energy technology aims to provide efficient, reliable, and sustainable energy solutions that meet the increasing global energy demand while minimizing environmental impact and promoting a transition to a low-carbon future. It refers to the various scientific and engineering practices and systems used to generate, convert, store, distribute, and utilize energy. It encompasses a wide range of technologies and approaches to meet energy demands, improve energy efficiency, and transition to cleaner and more sustainable energy sources. KAIST CBE studies both conventional energy technologies (fossil fuel-based systems) and renewable energy sources such as solar and biomass energy. We also leading the technology development of advanced battery systems.

  • Energy1
  • Energy2
Professor
  • Koh, Dong-Yeun

    Multidimensional molecular materials

  • Kim, Bumjoon

    Polymer nano electronics

  • Kim, Jihan

    Molecular simulation

  • Kim, Hee Tak

    Electrochemical energy device

  • Hong Chul, Moon

  • Seo, Jangwon

    Solar energy & organic-hybrid electronics

  • Lee, Sang Yup

    Metabolic and biomolecular engineering

  • Lee, Jae Woo

    CO₂ conversion, CO₂ chemical looping, clathrate hydrates, process intensification via reactive separation

  • Lee, Jinwoo

    Nanomaterials, porous material, electrocatalyst, energy storage, biocatalyst

  • Lee, Hyunjoo

    Heterogeneous catalysis, single-atomic catalyst, surface chemistry, gas-phase reactions, electrochemical reactions

  • Im, Sung Gap

    Functional thin films

  • Chung, Dong Young

    Electrocatalysis, electrochemistry, energy conversion and storage, in situ analysis

  • Minju Chung

  • Jung, Hee Tae

    Nano patterning, molecular self-assembly, gas sensors, catalysis, membrane

  • Cho, Eun Seon

    Functional hybrid nanomaterials

  • Choi, Nam-Soon

    Energy materials

  • Choi, Minkee

    Heterogeneous catalysis, zeolite, CO₂ adsorption and utilization, green chemistry

  • Heo, Seongmin

    Process modeling and simulation, process control, optimization, machine learning