Im Doo Jung
UNIST 기계공학과 · 공학
이 교수의 연구실은 첨단 3D 프린팅, 스마트 소재 및 에너지 수확 기술을 중심으로 한 혁신적 연구를 수행하고 있습니다. 특히, 분포형 온도 센싱과 인공지능을 융합한 파이프 인 파이프 유량 누설 탐지 기술, 프루시안 블루 기반 미세 패턴화 및 전기화학적 에너지 수확 장치 개발을 통해 산업용 고신뢰성 소재 및 에너지 솔루션을 모색하고 있습니다. 또한, 유기-무기 페로브스카이트 양자점을 활용한 고성능 복합재료의 3차원 구조 제작 기술도 핵심 연구 분야로 다룹니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Pipe-in-pipe (PIP) system is essential for high thermal and high pressure fluid transportation. However, in the existing PIP systems, fluid leakage between inner and outer pipe has been difficult to discover or detect, which has worked as bottle neck to utilize PIP system in high risk industries as nuclear reactor, chemical plant or oil drilling systems. Here, we propose a noble PIP leakage detection system utilizing distributed temperature sensing (DTS) with Machine Learning (ML). With the Four
Using energy-saving electrochromic (EC) displays in smart devices for augmented reality makes cost-effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal-organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro-patterning. This work presents a novel micro-printing strategy for PB patterns using localized crystallization of FeFe(CN)<sub>6</sub> on a substrat
Abstract Organic–inorganic perovskite quantum dot (PQD)–polymer composites are emerging optoelectronic materials with exceptional properties that are promising widespread application in next‐generation electronics. Advances in the utilization of these materials depend on the development of suitable fabrication techniques to create 3D architectures composed of PQD–polymer for sophisticated optoelectronics. This study introduces a straightforward and effective method for producing 3D architectures
The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process
Kinetic energy is an ideal energy source for powering wearable devices or internet of things (IoTs) because of its abundant availability. Currently, most kinetic energy harvesting systems are based on friction or deformation, which require high-frequency motion or high material durability for sustainable energy harvesting. Here, we introduce selective ion sweeping in a hybrid cell consisting of an ion-adsorbing activated carbon and an ion-hosting Prussian blue analogue nanoparticle for electroch
