Gan Jin
Yonsei University · 歯学
研究室紹介
Professor Gan Jin's research lab specializes in advanced dental materials and digital dentistry, focusing on the development and optimization of 3D-printed dental resins, post-processing techniques, and biocompatible materials for clinical applications. The lab investigates the mechanical, surface, and biological properties of resin-based materials, with particular emphasis on water-washable resins, silica particle reinforcement, and the impact of processing parameters on print accuracy and cytocompatibility. Additionally, the lab explores digital workflow integration, including intraoral scanning accuracy and automated smile analysis using computational modeling.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Three-dimensional (3D) printing, otherwise known as additive manufacturing in a non-technical context, is becoming increasingly popular in the field of dentistry. As an essential step in the 3D printing process, postwashing with organic solvents can damage the printed resin polymer and possibly pose a risk to human health. The development of water-washable dental resins means that water can be used as a washing agent. However, the effects of washing agents and washing times on the mechanical and
Immersing the printed photosensitive dental resins in 100 °C water for 5 min is a suitable method for increasing cytocompatibility and the DC.
This study aimed to determine the influence of the healing abutment (HA), placed at the implant placement site, on the accuracy of intraoral scanning and buccal bite registration in quadrant maxillary and mandibular models when using three types of intraoral scanner (IOS) and elucidate the distribution of arch distortion. Six experimental groups based on whether the HA was connected and the location of missing teeth were digitized using one laboratory scanner (Identica T500) and three IOSs (Trio
OBJECTIVES: This study aimed to develop and validate evaluation metric for an automated smile classification model termed the "smile index." This innovative model uses computational methods to numerically classify and analyze conventional smile types. METHODS: The datasets used in this study consisted of 300 images to verify, 150 images to validate, and 9 images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values
This study investigated the effects of silicon dioxide (SiO₂, silica) particle size on the mechanical, surface, and printing-trueness properties of 3D-printed dental resin. Silica nanoparticles (5-20 nm) and microparticles (0.5-10 μm) were incorporated at 1 wt% and 2 wt% into a commercial 3D-printing resin. The specimens were printed and postprocessed under standard conditions. Particle size distribution was determined using a Particle Size Analyzer. Surface characteristics such as color differe