Hanyang University · 工学
Professor Ju Hun Lee's research lab specializes in bioinspired nanomaterials and biosensing, focusing on developing highly sensitive, low-cost diagnostic platforms by leveraging the unique properties of biological systems—particularly the M13 bacteriophage—as functional scaffolds. The lab pioneers innovative sensor designs that integrate structural color, magnetic responsiveness, and signal amplification for applications in clinical diagnostics, environmental monitoring, and point-of-care testing. Key research directions include engineering phage-based arrays for chemical and biomarker detection, designing multifunctional nanowires for multiplexed sensing, and advancing magnetic nanoparticle-integrated assays for liquid biopsy applications such as ctDNA detection.
Figures are computed from collected data and may differ slightly.
The mammalian olfactory system provides great inspiration for the design of intelligent sensors. To this end, we have developed a bioinspired phage nanostructure-based color sensor array and a smartphone-based sensing network system. Using a M13 bacteriophage (phage) as a basic building block, we created structural color matrices that are composed of liquid-crystalline bundled nanofibers from self-assembled phages. The phages were engineered to express cross-responsive receptors on their major c
Stripped wires: Multifunctional (magnetic and optical) iron–gold barcode nanowires were electrochemically fabricated using nanoporous templates. Structural analysis by TEM elemental line scan and mapping (see images) clearly revealed the well-separated, bamboo-like nanostructures composed of Fe and Au strips.
Sensitive protein detection and accurate identification continues to be in great demand for disease screening in clinical and laboratory settings. For these diagnostics to be of clinical value, it is necessary to develop sensors that have high sensitivity but favorable cost-to-benefit ratios. However, many of these sensing platforms are thermally unstable or require significant materials synthesis, engineering, or fabrication. Recently, we demonstrated that naturally occurring M13 bacteriophage
Because of their unique properties, nanomaterials have been actively investigated in recent years for biosensing applications. A typical approach for biomarker detection is to attach capture or detection antibodies to nanomaterials, allow the analyte to bind, and measure the resulting change in signal. While antibodies or aptamers possess at most one binding site each for the nanomaterial and analyte, it is shown that the high surface area filamentous M13 bacteriophage can be utilized as a scaff
Detection of desired target chemicals in a sensitive and selective manner is critically important to protect human health, environment and national security. Nature has been a great source of inspiration for the design of sensitive and selective sensors. In this mini-review, we overview the recent developments in bio-inspired sensor development. There are four major components of sensor design: design of receptors for specific targets; coating materials to integrate receptors to transducing mach
Circulating tumor DNA (ctDNA) detection has been acknowledged as a promising liquid biopsy approach for cancer diagnosis, with various ctDNA assays used for early detection and treatment monitoring. Dispersible magnetic nanoparticle-based electrochemical detection methods have been proposed as promising candidates for ctDNA detection based on the detection performance and features of the platform material. This study proposes a nanoparticle surface-localized genetic amplification approach by int
A new method to engineer unique, solution-based protein diagnostics with femotomole sensitivies from modified bacteriophage is reported. These sensors are highly facile to use, rapid to run, possible to read without any spectroscopic or microscopic analysis, and do not require thermally unstable enzymes. These sensing platforms should be functional in locations with limited access to equipment and facilities.
Open papers in the app to read, cite, and organize with AI.